Abstract
Background
Dental implant success depends on factors such as age and bone density; this study evaluates how age-related bone density changes affect implant stability and survival.
Methods
This retrospective cohort study included patients receiving dental implants at the Affiliated Stomatological Hospital of Chongqing Medical University (March 2019-2024). Peri-implant bone density was measured by cone-beam computed tomography (CBCT) in Hounsfield units (HU) at coronal and apical sites 1, 2, 3, and 6 months postoperatively. The primary outcome was implant stability, assessed with resonance frequency analysis (RFA). Secondary outcome was early implant failure within 6 months. Implant success required stability without mobility or infection. Quality of life was the tertiary outcome, measured using the Oral Health Impact Profile-14 (OHIP-14).
Results
A total of 1,621 subjects with 1,821 implants were analyzed. Older adults (66-80 years) demonstrated lower bone density and stability, with markedly higher early failure (14.56%) and infection rates (22.78%, both P < .001), compared with younger groups (failure rate: 4.99% in 20-35 years; 1.07% in 36-50 years; 0.64% in 51-65 years). At 6 months, cumulative implant survival declined to 85.4% in the 66-80 years group versus >95% in younger groups. Logistic regression further identified age (OR = 0.481, P = .014) and low bone density (OR = 1.89, P = .019) as significant risk factors for early implant failure. The model showed good discrimination (AUC = 0.823) and acceptable fit (Nagelkerke R² = 0.21), supporting the robustness of these estimates.
Conclusions
Age-related bone loss may impair implant success. Older adults showed higher early failure risks, while younger patients experienced greater QoL gains, suggesting a need for age-specific, personalized implant strategies.
Keywords: Dental implants, age, Bone density, Implant stability, Treatment failure
Introduction
Dental implants represent a well-established and widely adopted approach for oral rehabilitation, providing a reliable solution for tooth loss.1 Advances in technology and materials have markedly improved implant success rates, making them a preferred choice for both patients and clinicians.2 Nonetheless, despite these advancements, factors such as age, bone density, and systemic health conditions continue to impact implant success.3,4 Thus, understanding these factors is essential for optimizing patient outcomes and developing personalized treatment plans.
Age is an important factor affecting the success of dental implants. As the population ages, there has been a notable increase in the demand for dental implants among older adults. This demographic often presents with various comorbidities, including osteoporosis, diabetes, and cardiovascular diseases, which can compromise both bone healing and the integration of implants.5,6 Additionally, age-related changes in bone density and quality further impact implant stability and longevity.7 Early implant failure and postoperative infections remain important complications in dental implantology.8, 9, 10, 11 However, despite these recognitions, most studies have examined these risk factors separately, and there is a lack of comprehensive investigations integrating age, systemic health, and bone density to assess their combined influence on implant success across different age groups.
Bone density around the implant site is another important factor influencing implant stability and osseointegration.12,13 It is commonly assessed using Hounsfield units (HU) from cone-beam computed tomography (CBCT) scans.13 Generally, higher bone density correlates with better implant stability and higher success rates.14,15 However, bone density tends to decline with age, which can present challenges in achieving successful implant outcomes in older patients.
Implant stability, assessed through electronic resonance frequency analysis (RFA), serves as a key predictor of long-term implant success.16,17 The implant stability quotient (ISQ) values offer a quantitative measure of implant stability, with higher ISQ values reflecting greater stability.18 Achieving both immediate and early implant stability is crucial for preventing micromovement and ensuring successful osseointegration.19 However, implant stability can be influenced by various factors, including bone quality, implant design, and surgical technique.20, 21, 22 Thus, clarifying how these factors interact across different age groups is essential for optimizing implant protocols and enhancing clinical outcomes.
This study aims to address the knowledge gaps regarding the combined impact of age, bone density, and systemic health conditions on dental implant stability and success. We hypothesize that patients aged 66-80 years will exhibit significantly lower bone density, reduced ISQ, and higher early implant failure rates compared with younger groups, and that systemic conditions further amplify these risks. The primary objectives of this study are to: (1) evaluate the relationship between age-related changes in bone density and implant stability, (2) assess how systemic health conditions modify this relationship, and (3) identify key factors that predict implant success across different age groups. By addressing these aims, this study seeks to inform clinical practice by highlighting the need for tailored, age-specific treatment strategies to optimize dental implant outcomes.
Materials and methods
Study design and sample
This study is a retrospective cohort study that analyzed data from subjects who underwent dental implant treatment at the Affiliated Stomatological Hospital of Chongqing Medical University between March 2019 and March 2024. Subjects eligible for inclusion were those aged 20-80 years, generally healthy (classified as ASA I or II).23 Subjects were excluded if they had uncontrolled systemic diseases or surgical contraindications (ASA score III or higher),23 received bisphosphonate therapy or radiotherapy, smoked heavily (more than 10 cigarettes per day), had systemic diseases affecting normal bone healing, had edentulous opposing jaws or severe bruxism, or were pregnant or breastfeeding. A post-hoc power calculation confirmed that this sample size achieved >90% power to detect clinically meaningful differences in early implant failure rates across groups at a 2-sided α of 0.05, thereby supporting the sufficiency of our sample size. The Institutional Review Board of our hospital approved the study protocol, and informed consent was obtained from all participants.
Variables
The primary predictor variable was bone density around the implant site, which was measured using cone-beam computed tomography (CBCT). Bone density was expressed in Hounsfield units (HU)24 and recorded at the coronal and apical points of the implant sites at 1, 2, 3, and 6 months postoperatively. These repeated measurements were used to better reflect early peri-implant bone status. The earliest measurement, taken immediately postoperatively (within 24 hours), was used as the baseline value for all analyses. The CBCT scans were performed with the Planmeca ProMax 3D Mid CBCT system (Planmeca Oy, Helsinki, Finland), which provides high-resolution images and precise measurements of bone density. These scans were conducted at a specialized dental imaging center equipped with advanced CBCT technology to ensure accurate and reliable data collection. HU measurements were taken using the software provided by the CBCT manufacturer.
The covariates were classified into demographic, systemic health, and implant-specific factors. Demographic variables included age (categorized as 20-35, 36-50, 51-65, and 66-80 years) and sex, which was coded as binary (male/female). Systemic health conditions included relevant factors such as diabetes, osteoporosis, and hypertension, recorded as binary variables (present/absent) based on medical records. Detailed information about the dental implants included the location of the implant (anterior maxilla, posterior maxilla, anterior mandible, or posterior mandible), the reason for implantation (caries, periodontal disease, fracture, impaction, or developmental defects), and the timing of implantation (immediate or delayed). Implants were categorized by length into ≤ 8.0 mm, 8.1-10.0 mm, and ≥ 10.1 mm, and by diameter into ≤ 3.5 mm, 3.6-3.9 mm, 4.0-5.0 mm, and ≥ 5.1 mm. Finally, postoperative antibiotic use, recorded as a binary variable (yes/no), was included as an additional covariate to evaluate its role in early implant stability and failure.
Primary outcome: Implant stability, measured using the implant stability quotient(ISQ) via resonance frequency analysis(RFA). Higher ISQ values indicate greater stability. ISQ values were recorded immediately after surgery and at 1, 2, 3, and 6 months postoperatively. The RFA measurements were performed with the Osstell ISQ device (Osstell, Gothenburg, Sweden), which uses a transducer (referred to as the SmartPeg) attached to the implant, which vibrates in response to a magnetic pulse. Implant stability was treated as a binary outcome - either stable (as evidenced by ISQ values) or not stable (as evidenced by ISQ values, implant movement, pain, or loss) at each measurement time point. The frequency response is then measured and converted into an ISQ value ranging from 1 to 100, where higher values indicate greater stability.18 All measurements were conducted in a controlled clinical setting to ensure consistency and accuracy. Secondary outcome: Early implant failure, defined as implant loss occurring within the initial 6 months post-surgery, confirmed by clinical and radiographic criteria. Implant failure was determined based on clinical examination and radiographic findings during follow-up visits.
Postoperative infection was also treated as a secondary outcome. Infections were defined as the presence of clinical signs such as redness, swelling, discharge, pain, or radiographic evidence of peri-implant bone loss occurring within 6 months post-surgery. All infections were documented based on clinical follow-up and managed according to standard treatment protocols.
The Oral Health Impact Profile (OHIP-14) was employed to evaluate subjects' quality of life before the operation and at 1, 3, and 6 months post-operation.25 The OHIP-14 is a validated questionnaire that measures the impact of oral health on overall quality of life across 7 dimensions: functional limitation, physical pain, psychological discomfort, physical disability, psychological disability, social disability, and handicap. Each dimension includes 2 questions, with responses recorded on a Likert scale ranging from 0 (never) to 4 (very often). The total OHIP-14 score ranges from 0 to 56, with higher scores indicating worse quality of life. The questionnaire was administered in a face-to-face interview format by trained research staff to ensure accurate and consistent data collection. Data analysis focused on changes in quality of life over time and compared the impact of implant treatment across different age groups.
Follow-up and outcome assessment
All implants were assessed immediately after surgery, and at 1, 2, 3, and 6 months postoperatively to evaluate implant stability and detect early failure. The follow-up duration for each implant was either until failure within the first 6 months or until completion of the 6-month follow-up period if no failure occurred. Detailed follow-up data for each implant, including both successful and failed cases, were meticulously recorded at each time point.
Survival time was defined as the time interval from the date of implant placement to the date of implant failure (event) or to the last follow-up evaluation if the implant did not fail (censored observation). This duration was measured in months based on routine clinical follow-ups at 1, 2, 3, and 6 months postoperatively. Due to the retrospective design and the structured follow-up schedule, the precise date of failure (in days) was not consistently documented. Therefore, survival time was treated as a discrete variable reflecting the predefined clinical follow-up intervals. Follow-up time was defined as the postoperative period from the date of implant placement to the date of implant failure or to the completion of the 6-month follow-up visit in the case of non-failed implants.
Data collection methods
Data were collected from patient medical records, imaging reports, and clinical follow-ups. Bone density data were obtained from CBCT scans at specified time points postoperatively, and implant stability quotient(ISQ) values were measured during routine clinical assessments using standardized protocols. Patient demographic data, including age and systemic health conditions, were extracted from medical records. Implant failure was recorded during regular follow-up visits based on clinical and radiographic findings.
Investigator calibration and standardization of measurements
The measurements and data collection for this study were conducted by a team of 4 trained investigators. Prior to data collection, all investigators underwent calibration sessions to ensure consistency in the measurement techniques. These sessions involved practicing the use of cone-beam computed tomography (CBCT) for bone density measurements and resonance frequency analysis(RFA) for implant stability. The investigators followed a standardized protocol for each procedure, which included consistent positioning of subjects for CBCT scans and uniform use of the Osstell implant stability quotient(ISQ) device for RFA measurements. Calibration was achieved by comparing the results among investigators, with discrepancies resolved through group discussions and additional training where necessary. Furthermore, data entry followed a standardized process, with all entries cross-checked by a second investigator to ensure accuracy and minimize errors.
Implant placement protocol
All implants were placed using a standardized surgical protocol. The protocol involved preoperative cone-beam computed tomography (CBCT) to assess bone density and ensure optimal implant positioning. Implants were placed using a 2-stage surgical technique, adhering to consistent drilling speeds, irrigation protocols, and torque settings across all subjects. All surgical procedures were performed by the same team of experienced clinicians to minimize variability.
Early implant failure and postoperative infection rates
Our follow-up was chosen to capture early implant outcomes, such as osseointegration and early implant failure, as these are crucial indicators of long-term success. All assessments were conducted at regular follow-up visits to evaluate implant stability, bone density, and overall implant success within the specified timeframe.
The implants were buried initially and allowed to heal before being loaded, and not all implants were loaded at the same postoperative point. Early implant failure was defined as failure of the implant within the initial healing period of up to 6 months post-operation. This data was recorded for each age group, providing insights into the success rates of implants across different age ranges. Implant failure was determined through clinical examination and radiographic analysis, which assessed signs of mobility, pain, and lack of osseointegration. Postoperative infection rates were monitored during the 6-month follow-up period. Infections were identified based on clinical symptoms such as swelling, redness, and discharge, as well as radiographic evidence of bone loss around the implant. Infections were managed according to standard clinical protocols, which included antibiotic therapy and, if necessary, surgical intervention.
Restorative protocol
A standardized restorative protocol was followed for all subjects. All restorations were placed after a healing period of 3 to 6 months, depending on patient-specific healing outcomes and the stability of the implant as measured by electronic resonance frequency analysis(RFA). Custom-made abutments and cement-retained prostheses were fabricated and fitted for each implant, with all restorations being performed by a single prosthodontist to ensure consistency.
Occlusal scheme
A uniform occlusal scheme was applied for all subjects, with an emphasis on mutually protected occlusion. Care was taken to avoid premature contacts and excessive forces on the implants during the occlusal adjustment phase. All occlusal adjustments were conducted using articulation paper and clinical judgment to ensure the restoration was integrated into the patient's natural occlusion harmoniously.
Data analysis
Statistical analyses were performed using SPSS version 22.0 software (IBM Corp., Armonk, NY, USA), with a significance level set at a p-value of < 0.05. Descriptive statistics were used to summarize demographic and baseline characteristics. Implant stability, bone desity, and quality of life-each measured repeatedly at multiple time points (immediate post-operation, and at 1, 2, 3, and 6 months)-were analyzed using repeated measures ANOVA to adjust for within-subject variation and strengthen the estimation of early peri-implant bone conditions. This analysis was not designed to test longitudinal trends, but to improve the reliability of early bone density assessment. Bonferroni corrections were applied for all pairwise comparisons to control for type I error. Chi-square tests and ANOVA were employed to compare categorical and continuous variables across age grouaps, respectively, with post hoc tests used to identify specific group differences. Early implant failure was treated as a binary outcome (failure vs. no failure). Logistic regression was used to assess the association between predictors (e.g., age, bone density, systemic conditions) and early implant failure, with odds ratios (OR) and 95% confidence intervals (CI) reported.
To evaluate model performance, we additionally calculated the Nagelkerke R² and generated a receiver operating characteristic (ROC) curve with the area under the curve (AUC). otential multicollinearity was assessed using pairwise correlations and variance inflation factor (VIF) values (all < 2.5), and effect modification was evaluated through interaction term testing (e.g., age × bone density).
Results
Patient demographics and baseline characteristics
The study included a total of 1621 subjects, categorized into 4 age groups: 20-35 years, 36-50 years, 51-65 years, and 66-80 years.26 Data analysis revealed no significant differences across these groups in regard to Sex distribution, number of implants, smoking status, reasons for implantation, timing of implantation, implant length, or preoperative and postoperative antibiotic use (P > .05) (Table 1, Table 2). However, the 36-50 and 51-65 age groups were found to have significantly higher BMI compared to the 20-35 age group (P < .001). At 6 months, bone density in the 66-80 group was 1.50 ± 0.11 Hounsfield units (HU) vs. 1.90 ± 0.30 HU in the 20-35 group (P < .001); implant stability quotient(ISQ) was also lower (51.96 ± 6.17 vs. 59.88 ± 6.08 post-op, P < .001). Furthermore, the incidence of hypertension, ischemic heart disease, hypercholesterolemia, osteoporosis, and a history of periodontal treatment increased significantly with age. Specifically, in the 66-80 years group, the rates of hypertension (P < .001), ischemic heart disease (P < .001), osteoporosis (P < .001), and periodontal treatment history (P < .001) were significantly higher compared to other age groups. The 51-65 years group also exhibited higher incidences of hypertension (P < .001), ischemic heart disease (P < .001), and osteoporosis (P < .001) compared to the 36-50 years group. Significant differences were noted in implant location and diameter among the age groups (P < .001). The 66-80 years group had the highest proportion of posterior maxilla implants (32.91%), while the 20-35 years group had the highest proportion of anterior mandible implants (36.86%). Regarding implant diameter, significant differences were observed, with the 36-50 years and 51-65 years groups having a higher proportion of implants with diameters of 3.6-3.9 mm and 4.0-5.0 mm compared to other age groups (P < .001).
Table 1.
Demographic characteristics differences among age groups.
| Characteristics | 20-35 years group (n = 681) | 36-50 years group (n = 469) | 51-65 years group (n = 313) | 66-80 years group (n = 158) | χ2/F | P |
|---|---|---|---|---|---|---|
| Sex | 5.398 | .145 | ||||
| Male | 324 (47.58) | 194 (41.36) | 141 (45.05) | 78 (49.37) | ||
| Female | 357 (52.42) | 275 (58.64) | 172 (54.95) | 80 (50.61) | ||
| BMI | 22.68 ± 2.89 | 24.69 ± 3.02a | 24.34 ± 3.11a | 22.29 ± 3.26b,c | 57.895 | < .001 |
| Number of implants | 1.17 ± 0.37 | 1.18 ± 0.38 | 1.18 ± 0.38 | 1.13 ± 0.34 | 0.605 | .612 |
| Smoking | 112 (16.45) | 57 (12.15) | 39 (12.46) | 23 (14.56) | 5.208 | .157 |
Note:
P < .05 vs. 20-35 years.
P < .05 vs. 36-50 years.
P < .05 vs. 51-65 years. P < .05 represents statistically significant. F, F-statistic derived from an ANOVA.
Table 2.
Clinical and implant-related characteristics differences among age groups.
| Characteristics | 20-35 years group (n=681) | 36-50 years group (n=469) | 51-65 years group (n=313) | 66-80 years group (n=158) | χ2/F | P |
|---|---|---|---|---|---|---|
| Medical history | ||||||
| Hypertension | 32 (4.7) | 36 (7.68) | 49 (15.65)a,b | 42 (26.58)a,b,c | 84.852 | < .001 |
| Ischemic heart disease | 20 (2.94) | 23 (4.9) | 54 (17.25)a,b | 46 (29.11)a,b,c | 146.814 | < .001 |
| Hypercholesterolemia | 110 (16.15) | 176 (37.53)a | 111 (35.46)a | 30 (18.99)b,c | 84.503 | < .001 |
| Diabetes | 62 (9.1) | 45 (9.59) | 31 (9.9) | 15 (9.49) | 0.182 | .980 |
| Hypothyroidism | 31 (4.55) | 22 (4.69) | 15 (4.79) | 7 (4.43) | 0.046 | .997 |
| Hyperthyroidism | 14 (2.06) | 11 (2.35) | 10 (3.19) | 4 (2.53) | 1.203 | .752 |
| Osteoporosis | 34 (4.99) | 18 (3.84) | 28 (8.95)b | 42 (26.58)a,b,c | 98.792 | < .001 |
| Periodontal treatment history | 82 (12.04) | 110 (23.45) | 45 (14.38) | 44 (27.85)a,b,c | 39.671 | < .001 |
| Implant location | 136.654 | < .001 | ||||
| Anterior maxilla | 223 (32.75) | 152 (32.41) | 7223 | 48 (30.38) | ||
| Posterior maxilla | 103 (15.12) | 53 (11.3) | 34 (10.86) | 52 (32.91) | ||
| Anterior mandible | 251 (36.86) | 170 (36.25) | 161 (51.44) | 7 (4.43) | ||
| Posterior mandible | 104 (15.27) | 94 (20.04) | 46 (14.7) | 51 (32.28) | ||
| Reason for implantation | ||||||
| Caries | 404 (59.32) | 268 (57.14) | 180 (57.51) | 88 (55.7) | 1.007 | .800 |
| Periodontal disease | 169 (24.82) | 104 (22.17) | 62 (19.81) | 36 (22.78) | 3.253 | .354 |
| Fracture | 83 (12.19) | 64 (13.65) | 45 (14.38) | 29 (18.35) | 4.346 | .226 |
| Impaction | 101 (14.83) | 85(18.12) | 55 (17.57) | 35 (22.15) | 5.727 | .126 |
| Developmental defects | 106 (15.57) | 58(12.37) | 45 (14.38) | 16 (10.13) | 4.443 | .217 |
| Timing | 0.471 | .925 | ||||
| Immediate | 84 (12.33) | 56 (11.94) | 35 (11.18) | 17 (10.76) | ||
| Delayed | 597 (87.67) | 413 (88.06) | 278 (88.82) | 141 (89.24) | ||
| Implant length | 8.829 | .183 | ||||
| ≤8.0 | 243 (35.68) | 162 (34.54) | 135 (43.13) | 65 (41.14) | ||
| 8.1-10.0 | 316 (46.4) | 229 (48.83) | 129 (41.21) | 65 (41.14) | ||
| ≥10.1 | 122 (17.91) | 78 (16.63) | 49 (15.65) | 28 (17.72) | ||
| Diameter | 345.112 | < .001 | ||||
| ≤3.5 | 179 (26.28) | 46 (9.81) | 81 (25.88) | 23 (14.56) | ||
| 3.6-3.9 | 132 (19.38) | 193 (41.15) | 100 (31.95) | 62 (39.24) | ||
| 4.0-5.0 | 133 (19.53) | 208 (44.35) | 121 (38.66) | 32 (20.25) | ||
| ≥5.1 | 237 (34.8) | 22 (4.69) | 11 (3.51) | 41 (25.95) | ||
| Pre-op antibiotic use | 271 (39.79) | 189 (40.3) | 143 (45.69) | 66 (41.77) | 3.330 | .343 |
| Post-op antibiotic use | 322 (47.28) | 241 (51.39) | 167 (53.35) | 75 (47.47) | 4.110 | .250 |
Note:
P < .05 vs. 20-35 years.
P < .05 vs. 36-50 years.
P < .05 vs. 51-65 years. P < .05 represents statistically significant. F, F-statistic derived from an ANOVA.
Bone density
Bone density (measured in Hounsfield units [HU]) analysis revealed significant differences across age groups at various postoperative intervals (P < .001). Younger groups (20-35 years and 36-50 years) consistently exhibited higher bone density at all postoperative time points compared to older groups (51-65 years and 66-80 years). Notably, the 66-80 years group showed the lowest bone density values at each interval (1, 2, 3, and 6 months postoperatively), reflecting a pronounced decline in bone quality with age. Furthermore, bone density increased over time in all groups, but the magnitude of improvement was substantially smaller in the 66-80 years group (Table 3).
Table 3.
Bone density (HU) differences among age groups.
| Groups | 1 month post-op Bone density | 2 months post-op Bone density | 3 months post-op Bone density | 6 months post-op Bone density |
|---|---|---|---|---|
| 20-35 years group (n=681) | 1.55 ± 0.29 | 1.68 ± 0.13 | 1.79 ± 0.32 | 1.90 ± 0.30 |
| 36-50 years group (n=469) | 1.58 ± 0.29 | 1.67 ± 0.12 | 1.77 ± 0.29 | 1.87 ± 0.29 |
| 51-65 years group (n=313) | 1.23 ± 0.31 | 1.41 ± 0.12 | 1.74 ± 0.30 | 1.84 ± 0.12 |
| 66-80 years group (n=158) | 1.17 ± 0.31 | 1.2 ± 0.13 | 1.4 ± 0.12 | 1.5 ± 0.11 |
| Intergroup | F = 579.96 P < .001 | |||
| Time | F = 603.78 P < .001 | |||
| Intergroup* Time | F = 33.58 P < .001 | |||
Note: HU, hounsfield units.
Implant stability quotient
Implant stability, as measured by the ISQ, was found to differ significantly among age groups at various postoperative time points (P < .001; Table 4). The 66-80 years group demonstrated the lowest immediate postoperative ISQ values, indicating reduced primary stability. Over time, implant stability improved in all groups, but the recovery pattern varied significantly. The 51-65 years group showed the highest ISQ values at 3 and 6 months, suggesting superior secondary stability compared to other groups. Conversely, the 66-80 years group exhibited persistently lower ISQ values at all time points.
Table 4.
ISQ differences among age groups.
| Groups | Immediate post-op ISQ | 1-mon post-op ISQ | 2-mon post-op ISQ | 3-mon post-op ISQ | 6-mon post-op ISQ |
|---|---|---|---|---|---|
| 20-35 years group (n=681) | 59.88 ± 6.08 | 54.83 ± 5.9 | 53.18 ± 6.02 | 68.83 ± 5.79 | 69.74 ± 5.88 |
| 36-50 years group (n=469) | 59.64 ± 5.14 | 43.05 ± 4.78 | 53.32 ± 5.15 | 69.3 ± 6.04 | 70.2 ± 5.98 |
| 51-65 years group (n=313) | 60.35 ± 6.27 | 55.09 ± 5.75 | 63.16 ± 6.19 | 69.93 ± 5.88 | 70.68 ± 5.57 |
| 66-80 years group (n=158) | 51.96 ± 6.17 | 44.69 ± 5.96 | 53.25 ± 6.04 | 68.01 ± 6.22 | 69.24 ± 5.79 |
| Intergroup | F = 313.87 P < .001 | ||||
| Time | F = 2823.84 P < .001 | ||||
| Intergroup* Time | F = 133.69 P < .001 | ||||
Note: ISQ, implant stability quotient.
aP < .05 vs. 20-35 years.
bP < .05 vs. 36-50 years.
cP < .05 vs. 51-65 years.
P < .05 represents statistically significant. F, F-statistic derived from an ANOVA.
Early implant failure
As a separate clinical endpoint, early implant failure—defined as failure occurring within the first 6 months post-surgery—was assessed across all age groups. The rates of early implant failure varied significantly across different age groups (P < .001) (Table 5). The failure rate in the 20-35 years group was 4.99%. In contrast, the 36-50 years and 51-65 years groups had significantly lower failure rates of 1.07% and 0.64%, respectively (P < .05). Notably, the 66-80-year-old group exhibited a significantly higher failure rate of 14.56% compared to all other age groups (P < .05).
Table 5.
Early implant failure differences among age groups.
| Groups | Implant failure | No | Yes |
|---|---|---|---|
| 20-35 years group (n=681) | 647 (95.01) | 34 (4.99) | |
| 36-50 years group (n=469) | 464 (98.93)a | 5 (1.07)a | |
| 51-65 years group (n=313) | 311 (99.36)a | 2 (0.64)a | |
| 66-80 years group (n=158) | 135 (85.44)a,b,c | 23 (14.56)a,b,c | |
| X2 | 68.161 | ||
| P | < 0.001 |
Note:
P < .05 vs. 20-35 years.
P < .05 vs. 36-50 years.
P < .05 vs. 51-65 years.
P < .05 represents statistically significant.
Additionally, Kaplan-Meier survival analysis demonstrated significant differences in cumulative implant survival across age groups, with the 66-80 years group showing markedly reduced survival probability compared to younger groups (log-rank test, P < .001) (Figure 1).
Figure 1.
Kaplan-Meier survival curves of dental implants stratified by age groups (20-35, 36-50, 51-65, and 66-80 years). The 66-80 years group showed significantly lower cumulative survival probability compared to younger groups (log-rank test, P < .001).
Postoperative infection
Postoperative infection rates were also significantly higher in the 66-80 years group compared to the other age groups (P < .001) (Table 6). The infection rate in the 66-80 years group was 22.78%, which was significantly higher than the infection rates observed in the 20-35 years group (7.34%), the 36-50 years group (5.76%), and the 51-65 years group (3.51%) (P < .05).
Table 6.
Postoperative infection differences among age groups.
| Groups | Infection | No | Yes |
|---|---|---|---|
| 20-35 years group (n=681) | 631 (92.66) | 50 (7.34) | |
| 36-50 years group (n=469) | 442 (94.24) | 27 (5.76) | |
| 51-65 years group (n=313) | 302 (96.49) | 11 (3.51) | |
| 66-80 years group (n=158) | 122 (77.22)a,b,c | 36 (22.78)a,b,c | |
| χ2 | 61.280 | ||
| P | < 0.001 |
Note:
P < .05 vs. 20-35 years.
P < .05 vs. 36-50 years.
P < .05 vs. 51-65 years.
P < .05 represents statistically significant.
Quality of life assessment
Postoperative quality of life, assessed using the Oral Health Impact Profile(OHIP-14) scores, differed significantly across age groups and over time (P < .001). OHIP-14 scores improved significantly in younger patients (–11.74, P < .001), but not in the 66-80 group (–2.14, P = 0.111). Younger subjects (20-35 years) demonstrated the greatest improvements in quality of life, with significantly lower scores at all postoperative intervals compared to older groups, indicating better outcomes. The 36-50 years and 51-65 years groups also showed substantial improvements, particularly at 3 and 6 months. However, the 66-80 years group experienced the least improvement, with higher scores persisting at all postoperative time points, especially at 1 and 6 months (Table 7).
Table 7.
Quality of Life (OHIP-14) Differences Among Age Groups.
| Groups | Pre-op quality of life | 1-mon post-op quality of life | 3-mon post-op quality of life | 6-mon post-op quality of life |
|---|---|---|---|---|
| 20-35 years group (n=681) | 35.92 ± 6.17 | 30.18 ± 6.00 | 25.92 ± 5.99 | 24.18 ± 5.83 |
| 36-50 years group (n=469) | 35.9 ± 6.26 | 24.64 ± 6.05 | 19.17 ± 6.32 | 16.04 ± 6.07 |
| 51-65 years group (n=313) | 36.00 ± 6.45 | 21.67 ± 6.00 | 18.33 ± 6.37 | 15.27 ± 6.05 |
| 66-80 years group (n=158) | 35.49 ± 5.81 | 29.59 ± 6.20 | 25.31 ± 6.03 | 23.35 ± 5.72 |
| Intergroup | F = 414.91 P < .001 | |||
| Time | F = 1690.93 P < .001 | |||
| Intergroup* Time | F = 55.71 P < .001 | |||
Note: OHIP, oral health impact profile.
Logistic regression analysis
Univariate logistic regression analysis identified several factors, such as age (P < .001), smoking (P = .035), diabetes(P = .034), osteoporosis(P = .015), periodontal treatment history(P = .049), implant location(P < .001), periodontal disease(P = .005), implant diameter(P = .006) and postoperative antibiotic use(P = .002), associated with early implant failure (Table 8). The 66-80 years group had a significantly higher risk of early implant failure compared to the 36-50 years group (OR = 15.81, 95% CI: 5.899-42.376, P < .001). Additionally, smoking (OR = 1.901, 95% CI: 1.047-3.449, P = .035), diabetes (OR = 2.068, 95% CI: 1.056-4.049, P = .034), osteoporosis (OR = 2.389, 95% CI: 1.185-4.819, P = .015), and a history of periodontal treatment (OR = 1.772, 95% CI: 1.002-3.133, P = .049) were significantly associated with an increased risk of early implant failure. Specific implant locations, such as the posterior maxilla (OR = 10.664, 95% CI: 4.352-26.13, P < .001) and posterior mandible (OR = 5.927, 95% CI: 2.352-14.936, P < .001), also showed higher risk. Conversely, larger implant diameters (4.0-5.0 mm) were associated with a reduced risk (OR = 0.337, 95% CI: 0.155-0.735, P = .006), as was postoperative antibiotic use (OR = 0.414, 95% CI: 0.240-0.715, P = .002).
Table 8.
Univariate and multivariate logistic regression analysis.
| Variables | Univariate |
Multivariate |
||||
|---|---|---|---|---|---|---|
| P | OR | 95%CI (Lower limit, Upper limit) | P | OR | 95%CI (Lower limit, Upper limit) | |
| Age | ||||||
| 36-50 years group | 1.000 | 1.000 | ||||
| 20-35 years group | .001 | 4.877 | 1.893, 12.563 | .002 | 4.92 | 1.793, 13.499 |
| 51-65 years group | .539 | 0.597 | 0.115, 3.095 | .647 | 0.678 | 0.128, 3.592 |
| 66-80 years group | < .001 | 15.81 | 5.899, 42.376 | < .001 | 9.235 | 3.208, 26.587 |
| Sex | .591 | 1.149 | 0.693, 1.905 | |||
| BMI | .11 | 0.937 | 0.865, 1.015 | |||
| Number of implants | .441 | 1.277 | 0.685, 2.383 | |||
| Smoking | .035 | 1.901 | 1.047, 3.449 | .132 | 1.642 | 0.861, 3.132 |
| Hypertension | .585 | 0.772 | 0.305, 1.953 | |||
| Ischemic heart disease | .136 | 1.738 | 0.84, 3.594 | |||
| Hypercholesterolemia | .591 | 0.851 | 0.472, 1.534 | |||
| Diabetes | .034 | 2.068 | 1.056, 4.049 | .077 | 1.973 | 0.929, 4.193 |
| Hypothyroidism | .072 | 2.231 | 0.93, 5.349 | |||
| Hyperthyroidism | .656 | 0.635 | 0.086, 4.695 | |||
| Osteoporosis | .015 | 2.389 | 1.185, 4.819 | .824 | 1.103 | 0.465, 2.616 |
| Periodontal treatment history | .049 | 1.772 | 1.002, 3.133 | .058 | 1.856 | 0.978, 3.521 |
| Implant location | ||||||
| Anterior maxilla | 1.000 | 1.000 | ||||
| Posterior maxilla | < .001 | 10.664 | 4.352, 26.13 | < .001 | 8.085 | 3.198, 20.442 |
| Anterior mandible | .511 | 1.408 | 0.508, 3.901 | .264 | 1.816 | 0.637, 5.173 |
| Posterior mandible | < .001 | 5.927 | 2.352, 14.936 | .001 | 5.409 | 2.083, 14.045 |
| Caries | .626 | 1.136 | 0.681, 1.895 | |||
| Periodontal disease | .005 | 2.092 | 1.244, 3.519 | .028 | 1.885 | 1.069, 3.325 |
| Fracture | .315 | 0.646 | 0.275, 1.515 | |||
| Impaction | .103 | 0.493 | 0.211, 1.154 | |||
| Developmental defects | .489 | 0.754 | 0.34, 1.675 | |||
| Timing | .166 | 2.06 | 0.740, 5.733 | |||
| Implant length | .212 | 1.247 | 0.881, 1.764 | |||
| Diameter | ||||||
| ≤3.5 | 1.000 | 1.000 | ||||
| 3.6-3.9 | .344 | 0.735 | 0.389, 1.39 | .541 | 0.802 | 0.395, 1.629 |
| 4.0-5.0 | .006 | 0.337 | 0.155, 0.735 | .064 | 0.447 | 0.191, 1.047 |
| ≥5.1 | .468 | 0.769 | 0.379, 1.562 | .080 | 0.510 | 0.241, 1.083 |
| Pre-op antibiotic use | .681 | 1.111 | 0.671, 1.84 | |||
| Post-op antibiotic use | .002 | 0.414 | 0.24, 0.715 | .014 | 0.481 | 0.269, 0.860 |
P < .05 represents statistically significant.
In the multivariate analysis, significant factors associated with early implant failure included age, implant location, periodontal disease, and postoperative antibiotic use (Table 8). The 66-80 years group had a significantly higher risk of early implant failure compared to the 36-50 years group (OR = 9.235, 95% CI: 3.208-26.587, P < .001). Implant locations in the posterior maxilla (OR = 8.085, 95% CI: 3.198-20.442, P < .001) and posterior mandible (OR = 5.409, 95% CI: 2.083-14.045, P = .001) were associated with higher risk. Periodontal disease also increased the risk (OR = 1.885, 95% CI: 1.069-3.325, P = .028). Conversely, postoperative antibiotic use significantly reduced the risk of early implant failure (OR = 0.481, 95% CI: 0.269-0.860, P = .014). Additionally, potential interaction terms (e.g., age × bone density) were tested to evaluate effect modification. No statistically significant interactions were observed (P = .179), as illustrated in Figure 2.
Figure 2.
Predicted probability of early implant failure according to bone density in younger (20-50 years) and older (66-80 years) patients. Shaded areas represent 95% confidence intervals(CI). The interaction term (age × bone density) was not statistically significant (Likelihood Ratio Test, P = .179).
To further evaluate model performance, we conducted additional diagnostics. The multivariate logistic regression model demonstrated good discrimination, with an area under the curve(AUC) of 0.823 (95% CI: 0.805-0.841) (Figure 3). Model fit was acceptable, with a Nagelkerke R² of 0.21. Correlation analysis indicated a strong negative association between age and bone density (r = -0.85; Figure 4). Variance inflation factor(VIF) values for age, bone density, and implant site were all below 2.5, confirming that multicollinearity was not a significant concern (Figure 5).
Figure 3.
Receiver operating characteristic (ROC) curve for the multivariate logistic regression model predicting early implant failure.
Figure 4.
Correlation heatmap showing the relationship between age and bone density.
Figure 5.
Variance inflation factor (VIF) for key predictors, showing no evidence of severe multicollinearity.
Discussion
This retrospective cohort study investigated the impact of age-related bone density variations on dental implant stability and success. Our findings demonstrated that patients aged 66-80 years had significantly lower peri-implant bone density, reduced implant stability quotient(ISQ) values, and markedly higher rates of early implant failure and postoperative infections, whereas younger groups showed better stability and survival. Furthermore, postoperative antibiotic use was identified as a protective factor, significantly reducing the odds of early implant failure. These results highlight the critical influence of age and bone quality on implant outcomes and underscore the importance of age-specific treatment strategies in dental implantology.
Several previous studies have reported associations between age, bone quality, and implant success. Schimmel et al. emphasized that advanced age and systemic conditions compromise bone healing and osseointegration, consistent with our observation of diminished ISQ values and elevated failure rates in elderly patients.3 Similarly, Pius et al. demonstrated that age-related changes in osteosynthesis adversely affect implant stability.7 However, our study advances this understanding by providing detailed peri-implant bone density assessments at multiple postoperative intervals (1, 2, 3, and 6 months), offering insights into the temporal dynamics of bone remodeling in different age groups. This approach revealed that while bone density increased over time in all groups, the magnitude of improvement was markedly attenuated in older adults, suggesting impaired regenerative capacity.27,28
However, prior reports regarding the predictive value of ISQ have been inconsistent. Some studies found strong associations between higher ISQ and implant survival, whereas others reported that ISQ was not always a reliable predictor across heterogeneous patient groups.16,18 Our results support its clinical utility but highlight that age-related declines in bone density may limit its prognostic power in elderly patients.
Compared to Ryu et al., who reported a generalized higher implant failure rate in osteoporotic patients29,30, our study distinguishes itself by stratifying risk across age categories and adjusting for confounding variables such as diabetes and smoking. Moreover, our data suggest that implant location in the posterior maxilla and mandible further exacerbates failure risks in older adults, likely due to poorer bone quality and higher occlusal forces in these regions.21 Specifically, posterior mandibular sites are characterized by dense but less vascularized bone, which may impair healing capacity, whereas the anterior maxilla often presents with lower density trabecular bone but more favorable vascularity. Age-related reduction in regenerative potential may amplify these site-specific differences. This aligns with findings from Herrero-Climent et al., who highlighted the influence of implant site and surgical protocols on primary stability.22 Notably, our analysis also identified larger implant diameters and postoperative antibiotic use as protective factors. In our cohort, the postoperative antibiotics mainly included amoxicillin (500 mg 3 times daily) or clindamycin (300 mg 3 times daily for penicillin-allergic patients), typically administered for 5-7 days, consistent with common prophylactic protocols. This supporting the notion that modifying implant characteristics and perioperative care may mitigate age-related risks.9,10
In addition, bioengineering applications have opened new opportunities for improving clinical outcomes in oral implantology. Recent advances in digitally guided surgery protocols have enabled more precise implant positioning and individualized treatment planning, which may be particularly beneficial in patients with reduced bone density.31 Notably, recent work has also emphasized the role of integrated digital technologies in enhancing implant planning and execution, highlighting how digital workflows can improve accuracy and treatment predictability.32 Furthermore, finite element modeling has provided mechanistic insights into how peri-implant bone responds to dynamic stress, showing that conical implant connections can effectively reduce stress concentrations and thereby support osseointegration under compromised conditions.33 Parametric studies on prosthetic and mechanical parameters further demonstrated that implant geometry and loading conditions directly influence facial bone adaptation and stability.34 Integrating these bioengineering approaches with clinical decision-making could complement traditional risk stratification by age and bone density, offering a pathway toward more predictable and personalized implant success. Recent systematic evidence further supports this approach, highlighting survival patterns of single immediate implants and identifying risk factors associated with early failures, as well as optimal bone grafting material selection in compromised conditions.35,36
Quality of life (QoL) outcomes, measured via the Oral Health Impact Profile(OHIP-14), improved substantially in younger patients but showed limited enhancement in the 66–80 years group. These findings echo Duong et al., who reported greater functional and psychosocial benefits in younger implant recipients.1 Previous studies also reported divergent findings: while Duong et al. observed marked QoL improvements in implant patients,1 others noted minimal changes in elderly populations due to systemic conditions or prosthetic adaptation difficulties.25 Our data align with the latter, suggesting that QoL gains may be attenuated in older adults despite implant success. It should also be noted that baseline comorbidities, such as osteoporosis, diabetes, and hypertension, were more frequent in the elderly group. These conditions may not only impair implant prognosis but also independently reduce perceived quality of life, potentially confounding OHIP-14 outcomes. Although our dataset primarily focused on implant stability and survival, these findings suggest that systemic disease burden could partially account for the attenuated QoL improvements observed in elderly patients. While such comorbidities were included as covariates in our regression model to minimize residual confounding, their influence cannot be fully excluded. These findings underscore the need for individualized strategies in elderly patients, including preoperative bone quality assessment, selection of larger-diameter implants, and rigorous infection control measures.
Moreover, the protective effect of postoperative antibiotics emphasizes the role of strict infection control, especially in elderly patients. Importantly, our regression diagnostics confirmed that the multivariate model had reasonable explanatory power (Nagelkerke R² = 0.21) and good discriminatory capacity (AUC = 0.823). Moreover, correlation and variance inflation factor(VIF) analyses excluded serious multicollinearity, supporting the robustness of our regression estimates. Although baseline comorbidities such as osteoporosis and diabetes were more frequent in elderly patients, these were included as covariates in the regression model, which reduces the likelihood of residual confounding in our findings. Future multicenter prospective studies are needed to validate these results and to further investigate whether ISQ and OHIP-14 should be interpreted differently across age groups.
This study has several limitations. Its retrospective, single-center design may introduce selection bias and limit generalizability. Although bone density was reported in Hounsfield units (HU) derived from cone-beam computed tomography(CBCT) scans, it should be noted that CBCT has not been validated for absolute HU values because of differences in calibration, scanning protocols, and device-specific factors. Therefore, our HU results should be interpreted as relative indicators of bone density across age groups rather than as precise absolute values. Patient adherence to postoperative care was not assessed, and multiple implants in the same patient were treated as independent, possibly underestimating variability. Although some patients received more than 1 implant, the majority had single implants, which reduced the likelihood of substantial clustering effects. Nevertheless, the lack of adjustment using GEE or mixed-effects models remains a methodological limitation, and future studies with larger numbers of multiple-implant patients should address this issue. Nevertheless, these limitations do not detract from the key conclusion that age-related bone density loss and systemic conditions significantly increase the risk of implant failure.
Conclusion
In conclusion, this study highlights the significant impact of age-related bone density loss on dental implant success, with older adults (66-80 years) showing higher risks of early failure and postoperative infections due to reduced bone quality and comorbidities. Postoperative antibiotic use emerged as a protective factor, improving implant outcomes. These findings emphasize the need for personalized strategies in elderly patients, including thorough preoperative assessments and tailored surgical approaches. For example, individualized strategies may involve preoperative bone augmentation procedures (e.g., guided bone regeneration or sinus lift) to enhance bone quality, and the application of digitally guided surgery protocols to achieve precise implant positioning and minimize biomechanical stress. Future prospective studies should focus on advanced bone augmentation and individualized postoperative care to mitigate age-related risks and optimize implant success.
Ethics approval and consent of participate
The study protocol was approved by the Institutional Review Board of Stomatological Hospital of Chongoing Medical University. Informed consent was obtained from all patients participating in the study.
Consent of publication
Not applicable.
Authors’ contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Li-qiu Wang, Xu Tong and Yu Zhang. The first draft of this manuscript was written by Ting Gong and Lin-jing Shu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-profit sectors.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
References
- 1.Duong H.Y., Roccuzzo A., Stahli A., Salvi G.E., Lang N.P., Sculean A. Oral health-related quality of life of patients rehabilitated with fixed and removable implant-supported dental prostheses. Periodontol 2000. 2022;88(1):201–237. doi: 10.1111/prd.12419. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Block MS. Dental Implants: the Last 100 Years. J Oral Maxillofac Surg. 2018;76(1):11–26. doi: 10.1016/j.joms.2017.08.045. [DOI] [PubMed] [Google Scholar]
- 3.Schimmel M., Srinivasan M., McKenna G., Muller F. Effect of advanced age and/or systemic medical conditions on dental implant survival: A systematic review and meta-analysis. Clin Oral Implants Res. 2018;29(Suppl 16):311–330. doi: 10.1111/clr.13288. [DOI] [PubMed] [Google Scholar]
- 4.Block M.S., Christensen B.J., Mercante D.E., Chapple AG. What factors are associated with implant failure? J Oral Maxillofac Surg. 2021;79(1):91–97. doi: 10.1016/j.joms.2020.08.023. [DOI] [PubMed] [Google Scholar]
- 5.Alghamdi H.S., Jansen JA. The development and future of dental implants. Dent Mater J. 2020;39(2):167–172. doi: 10.4012/dmj.2019-140. [DOI] [PubMed] [Google Scholar]
- 6.Schliephake H. The role of systemic diseases and local conditions as risk factors. Periodontol 2000. 2022;88(1):36–51. doi: 10.1111/prd.12409. [DOI] [PubMed] [Google Scholar]
- 7.Pius A.K., Toya M., Gao Q., et al. Effects of Aging on Osteosynthesis at Bone-Implant Interfaces. Biomolecules. 2023;14(1) doi: 10.3390/biom14010052. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Sagnori R S Dfvj, Lima L H F, et al. Early dental implant failure associated with postoperative infection: A retrospective 21-year study. Oral Surg. 2024 [Google Scholar]
- 9.Soegiantho P SPG, Tran W., et al. Survival of single immediate implants and reasons for loss: a systematic review. Prosthesis. 2023;5(2):378–424. [Google Scholar]
- 10.Munakata M., Kataoka Y., Yamaguchi K., Sanda M. Risk factors for early implant failure and selection of bone grafting materials for various bone augmentation procedures: a narrative review. Bioengineering (Basel) 2024;11(2) doi: 10.3390/bioengineering11020192. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Zucchelli G., Wang H.L., Chambrone L. Complications and treatment errors in periodontal and implant therapy. Periodontol 2000. 2023;92(1):9–12. doi: 10.1111/prd.12442. [DOI] [PubMed] [Google Scholar]
- 12.A NK, A RO, A DD, A SJ, B PD. Dental implant stability and its measurements to improve osseointegration at the bone-implant interface: A review. 2020.
- 13.Noaman A.T., Bede S.Y. The effect of bone density measured by cone beam computed tomography and implant dimensions on the stability of dental implants. J Craniofac Surg. 2022;33(6):e553–e5e7. doi: 10.1097/SCS.0000000000008429. [DOI] [PubMed] [Google Scholar]
- 14.Ivanova V., Chenchev I., Zlatev S., Mijiritsky E. correlation between primary, secondary stability, bone density, percentage of vital bone formation and implant size. Int J Environ Res Pub Health. 2021;18(13) doi: 10.3390/ijerph18136994. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Wang S.H., Shen Y.W., Fuh L.J., et al. Relationship between cortical bone thickness and cancellous bone density at dental implant sites in the jawbone. Diagnostics (Basel) 2020;10(9) doi: 10.3390/diagnostics10090710. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Bezdjian A., Mikolajewicz N., Denton C., et al. Resonance frequency analysis identifies implant- and host-related factors associated with bone-anchored hearing implant stability. Otol Neurotol. 2024;45(6):676–683. doi: 10.1097/MAO.0000000000004213. [DOI] [PubMed] [Google Scholar]
- 17.Khened V., Dhok K., Dhatrak PP. Evaluation of resonance frequency and micromotion to achieve implant stability using vibroacoustic resonance frequency analysis: a mathematical model. J Engineer Sci Med Diagnos Ther. 2023;6(3) [Google Scholar]
- 18.H H., G W., E H. The clinical significance of implant stability quotient (ISQ) measurements: A literature review. J Oral Biol Craniofac Res. 2020;10(4):629–638. doi: 10.1016/j.jobcr.2020.07.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Silva A.S., Martins D., Sa J., Mendes JM. Clinical evaluation of the implant survival rate in patients subjected to immediate implant loading protocols. Dent Med Probl. 2021;58(1):61–68. doi: 10.17219/dmp/130088. [DOI] [PubMed] [Google Scholar]
- 20.Palaskar J.N., Joshi N., Shah P.M., Gullapalli P., Vinay V. Influence of different implant placement techniques to improve primary implant stability in low-density bone: A systematic review. J Indian Prosthodont Soc. 2020;20(1):11–16. doi: 10.4103/jips.jips_244_18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Farronato D., Manfredini M., Stocchero M., Caccia M., Azzi L., Farronato M. Influence of bone quality, drilling protocol, implant diameter/length on primary stability: an in vitro comparative study on insertion torque and resonance frequency analysis. J Oral Implantol. 2020;46(3):182–189. doi: 10.1563/aaid-joi-D-19-00145. [DOI] [PubMed] [Google Scholar]
- 22.Herrero-Climent M., Lemos B.F., Herrero-Climent F., et al. Influence of implant design and under-preparation of the implant site on implant primary stability. an in vitro study. Int J Environ Res Public Health. 2020;17(12) doi: 10.3390/ijerph17124436. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Clough S., Shehabi Z., Morgan C. Medical risk assessment in dentistry: use of the American Society of Anesthesiologists Physical Status Classification. Br Dent J. 2016;220(3):103–108. doi: 10.1038/sj.bdj.2016.87. [DOI] [PubMed] [Google Scholar]
- 24.Bruno V., Berti C., Barausse C., et al. Clinical relevance of bone density values from CT related to dental implant stability: a retrospective study. Biomed Res Int. 2018;2018 doi: 10.1155/2018/6758245. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Soh C.L., Tan P.G., Nor NMJJoO. Maxillofacial Surgery M, Pathology. Oral health related quality of life after treatment in maxillofacial trauma patients. J Oral Maxillofac Surg. 2021;33(3):267–271. [Google Scholar]
- 26.Tosun F., Bulbul M., Tosun I. Comparison of severe acute respiratory syndrome coronavirus 2 (COVID-19) vaccine side effects by age groups. Rev Assoc Med Bras. 2022;68(4):476–481. doi: 10.1590/1806-9282.20210829. (1992) [DOI] [PubMed] [Google Scholar]
- 27.Jiang L., Zhang W., Wei L., Zhou Q., Yang G., Qian N., et al. Early effects of parathyroid hormone on vascularized bone regeneration and implant osseointegration in aged rats. Biomaterials. 2018;179:15–28. doi: 10.1016/j.biomaterials.2018.06.035. [DOI] [PubMed] [Google Scholar]
- 28.Insua A., Monje A., Wang H.L., Miron RJ. Basis of bone metabolism around dental implants during osseointegration and peri-implant bone loss. J Biomed Mater Res A. 2017;105(7):2075–2089. doi: 10.1002/jbm.a.36060. [DOI] [PubMed] [Google Scholar]
- 29.Metwally I M EAH, Hosny A M. Evaluation of dental implant angulation on marginal bone loss in posterior maxillary area. Al-Azhar J Dent Sci. 2022;25(4):455–462. [Google Scholar]
- 30.Galindo-Moreno P., Catena A., Perez-Sayans M., Fernandez-Barbero J.E., O'Valle F., Padial-Molina M. Early marginal bone loss around dental implants to define success in implant dentistry: A retrospective study. Clin Implant Dent Relat Res. 2022;24(5):630–642. doi: 10.1111/cid.13122. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Fiorillo L, Meto A, Cicciù M. Bioengineering applied to oral implantology, a new protocol: “digital guided surgery”. 2023;5(1):234-50.
- 32.Samaranayake L., Tuygunov N., Schwendicke F., et al. The transformative role of artificial intelligence in dentistry: a comprehensive overview. Part 1: fundamentals of AI, and its contemporary applications in dentistry. Int Dent J. 2025;75(2):383–396. doi: 10.1016/j.identj.2025.02.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Fiorillo L, Cicciù M, D’Amico C, Mauceri R, Oteri G, Cervino G. Finite Element Method and Von Mises Investigation on Bone Response to Dynamic Stress with a Novel Conical Dental Implant Connection. 2020;2020(1):2976067. [DOI] [PMC free article] [PubMed]
- 34.Cicciù M, Cervino G, Terranova A, et al. Prosthetic and mechanical parameters of the facial bone under the load of different dental implant shapes: a parametric study. 2019;1(1):41-53.
- 35.Soegiantho P., Suryawinata P., Tran W., et al. Survival of single immediate implants and reasons for loss: a systematic review. Prosthesis. 2023;5:378–424. [Google Scholar]
- 36.Munakata M, Kataoka Y, Yamaguchi K, Sanda M. Risk factors for early implant failure and selection of bone grafting materials for various bone augmentation procedures: a narrative review. 2024;11(2):192. [DOI] [PMC free article] [PubMed]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.