Abstract
Objective
Artificial intelligence (AI) is rapidly advancing in Endodontics, particularly through the application of neural networks and deep learning models, that help in identifying complex root canal morphology and configurations, thereby enhancing diagnostic accuracy and treatment planning. This study aimed to assess the awareness of Indian Endodontists regarding AI applications in determining root canal morphology and configuration.
Methods
An online survey-based questionnaire was distributed to practicing Endodontists across India using Google Forms, and their responses were recorded. Chi-square test was used to study the association of independent and dependent variables.
Results
A survey of 338 practicing endodontists and postgraduate students revealed that less than half were aware of AI models such as Artificial Neural Networks (43.8 %) and Deep Learning (35.2 %). The majority (68.3 %) were partially aware of AI applications in endodontics. About 37.9 % considered AI as "fairly feasible" for daily endodontic clinical practice, and 82.5 % agreed that AI technology can enhance endodontic treatment success rates (p < 0.001). However, 60.90 % of the endodontists did not consider themselves trained for operating AI models, and 91.10 % never used any AI models or software (p < 0.001). Additionally, 89.30 % of the participants expressed the need for training programs and workshops on the use of AI in determining root canal morphology (p < 0.001).
Conclusion
Most Endodontists do not have sufficient knowledge to use AI models and do not employ AI software to identify root canal morphology and configuration. This study highlights the necessity for proper training for endodontists to improve the use of AI in determining root canal morphology and configuration.
Keywords: Artificial intelligence, Artificial neural networks/convolutional, Deep learning, Diagnostic precision, Root canal morphology
1. Introduction
John McCarthy, often regarded as the father of artificial intelligence, introduced the term "artificial intelligence" in 1955.1 However, the concept of artificial intelligence (AI) was formally recognized as a research discipline in 1956.2 The term Artificial Intelligence (AI) refers to the "the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision making, and translation between languages”.3
Within AI, Machine Learning (ML) constitutes a subfield that acquires knowledge from data by discerning intrinsic statistical patterns and aims to make predictions based on unseen data using this knowledge. A prominent approach within Machine Learning is called deep learning, which employs multi-layered mathematical operations to enhance learning and inference processes on complex datasets, such as imagery.4
Artificial intelligence (AI) is leading to significant advancements in diverse healthcare fields.5 In dentistry, particularly in Endodontics, AI is improving diagnostics, decision-making, treatment planning, and outcome prediction, which enhances the accuracy and efficiency of procedures.,6, 7, 8, 9, 10
Artificial intelligence (AI) models such as Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN) have demonstrated substantial utility in endodontics. They support the determination of root canal morphology, detection of periapical lesions and root fractures, working length estimation, prediction of pulp stem cell viability, forecasting of postoperative pain11 root canal failures,12 and the success of retreatment procedures7 thus enhancing the accuracy of diagnosis and treatment planning in Endodontics.13
AI has shown tremendous potential in identifying unobturated second mesio-buccal canals in root canal-treated maxillary molars.11 Predicting thrust force and torque in canal preparation, detecting subtle signs of pathology in radiographic images14 Machine learning is increasingly recognized as a valuable approach in dentistry, improving diagnostic precision, optimizing treatment planning, and supporting decision-making across clinical stages.15
The complexity and diversification in root canal morphology, which significantly affect the outcome of endodontic therapy, continue to remain an enigma and pose a major challenge for endodontists, making it difficult for them to clean, shape, and obturate. These can also result in endodontic failures due to the chances of perforation or missed canals. Deep learning has exhibited high accuracy and precision in the root canal morphology determination of the mandibular first molar and premolar16,17 and has shown promising results in the prediction, detection, and classification of C-shaped canals in mandibular second molars using panoramic radiographs18,19,20,21. Thus, it reduces the chances of root canal failures. Despite the high cost associated with its implementation, the exceptional ability of AI to determine root canal morphology emphasizes the necessity for its broader application in Endodontics.
Because of limited knowledge in this area, a cross-sectional study was performed to assess endodontists' awareness of using artificial intelligence (AI) to determine root canal morphology and its effect on treatment results. Other goals included examining their willingness to incorporate AI technologies into their daily clinical practice and addressing the limited understanding of AI applications in this field.
2. Methods
A descriptive cross-sectional online survey was conducted over 2 months to assess the knowledge of practicing Endodontists regarding the implementation of AI in determining root canal configuration and morphology. A self-validated questionnaire was used in the present study. The questionnaire was sent to 10 experts in the field of Endodontics and Artificial Intelligence for face and content validation. Appropriate changes were made to the questionnaire based on the feedback obtained from these experts. Internal consistency of the questionnaire was assessed using Cronbach's alpha. A value of 0.76 was obtained, suggesting an acceptable level of internal consistency and reliability of the questionnaire. This survey was carried out after obtaining approval from the Internal Institutional Ethics Committee at Jamia Millia Islamia (FOD/IRRC/134/02032024/F). The study enrolled all the dentists who were currently pursuing or have done their Masters in Dental Surgery (MDS) in the field of Conservative Dentistry and Endodontics and/or are currently practicing in hospitals or as private practitioners, and were willing to participate in the study. Informed consent was obtained from the participants before the start of the questionnaire, and confidentiality of the responses were maintained.
The survey was distributed via various social media platforms and personal contacts. A snowball sampling method was employed to achieve a sample size of 400 participants, of which 388 responded. The questionnaire was administered via a Google Form (Google LLC, Mountain View, California, United States), primarily in the form of a Likert scale, and had 22 questions, divided into two sections. The first section collected the demographic details of the participants while the second section had 16 questions about the knowledge and applicability of AI in endodontic treatment.
3. Statistical analysis
Statistical Package for Social Sciences (version 22.0, IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp.) was utilized for data analysis and non-parametric tests like the Chi-square test. A p-value of less than 0.05 was considered statistically significant.
4. Results
A total of 338 responses were collected from practicing endodontists and postgraduate students, consisting of 221 females (65.4 %) and 117 males (34.6 %). The current study population mainly consisted of females in the 25–35 age group (73.7 %), currently pursuing a postgraduate course in endodontics (60.7 %), and currently practicing in a government or private academic institution (60.4 %). The present study showed that less than half of the respondents were aware of AI models such as Artificial Neural Network (ANN) (43.8 %) and Deep Learning models (35.2 %). Nearly one-third (30.5 %) of the participants were unaware of any AI models. The majority of participants (68.3 %) had partial awareness of AI applications in Endodontics, showing statistically significant results. Approximately two-thirds (71.9 %) of the participants had a partial understanding of the applications of AI in endodontics. The results were found to be statistically significant, where 59.8 % of respondents recognized the significance of AI and its role, indicating that they acknowledged its importance.
More than two-thirds (84.3 %) of the respondents commonly used IOPAR to determine root canal morphology. Around 50.3 % of the respondents indicated "neither agreed nor disagreed" regarding the use of AI over CBCT for determining root morphology. Almost one-third of respondents (37.9 %) considered AI as “fairly feasible” for daily endodontic clinical practice in terms of ease of operation and cost, showing statistically significant results.
Almost half of the respondents (47.6 %) “sometimes” utilized 3D radiographic techniques to determine the root canal morphology. Also, more than half of the respondents (56.2 %) mentioned the frequency of root canal failures ‘sometimes’ due to undetected extra roots or C-shaped canals with a statistical significance of p < 0.001.
The majority (82.5 %) of respondents agree that AI technology can enhance endodontic treatment success rates (p < 0.001). Almost two-thirds (60.90 %) did not consider themselves as adequately trained for operating AI models. Also, more than two-thirds of the study participants (91.10 %) were not using any AI models or software and 90.20 % of respondents felt that more research is required to prove the accuracy of AI for root canal morphology.
Moreover, 89.30 % of the participants expressed the need for training programs and workshops on the use of AI in determining root canal morphology (p < 0.001). 86.4 % of the respondents were likely to use AI for determining root canal morphology in the future (p < 0.001) (Table-1 & Graphs).
Table 1.
| Frequency | Percent | ||
|---|---|---|---|
| Gender | Female | 221 | 65.4 % |
| Male | 117 | 34.6 % | |
| Age range | 25–35 yrs | 249 | 73.7 % |
| 35–45 yrs | 45 | 13.3 % | |
| Above 45 yrs | 44 | 13.0 % | |
| Highest level of education completed | Associate Professor | 1 | 0.3 % |
| BDS | 2 | 0.6 % | |
| Completed M.D.S in endodontics | 121 | 35.8 % | |
| Currently pursuing a postgraduate degree in Endodontics | 205 | 60.7 % | |
| General practitioners | 1 | 0.3 % | |
| M.D.S, PhD | 1 | 0.3 % | |
| MDS in oral Pathology and microbiology | 1 | 0.3 % | |
| MDS, PGDHHM, PGDMLS | 1 | 0.3 % | |
| Pershing PHD | 1 | 0.3 % | |
| Phd | 1 | 0.3 % | |
| Professor | 1 | 0.3 % | |
| Senior lecturer | 1 | 0.3 % | |
| Working as professor | 1 | 0.3 % | |
| Years of clinical experience in endodontics | <5 yrs | 38 | 11.2 % |
| >10 yrs | 74 | 21.9 % | |
| 5–10 yrs | 28 | 8.3 % | |
| Pursuing post-graduation in Endodontics | 198 | 58.6 % | |
| Current practice as | Academician in a govt or pvt college | 204 | 60.4 % |
| Both Academician & PP | 7 | 2.1 % | |
| PP | 42 | 12.4 % | |
| Doing Pg | 1 | 0.3 % | |
| SR | 84 | 24.9 % | |
| Frequency | Percent | P value | ||
|---|---|---|---|---|
| Which AI modes you are familiar with? | ANN | 148 | 43.8 | 0.022, S |
| U net | 30 | 8.9 | <0.001, S | |
| Deep learning | 119 | 35.2 | <0.001, S | |
| CNN | 26 | 7.7 | <0.001, S | |
| Inception V3 | 9 | 2.7 | <0.001, S | |
| None | 103 | 30.5 | <0.001, S | |
| What types of radiographs do you use to determine root canal morphology? | IOPAR | 285 | 84.30 % | <0.001, S |
| CBCT | 37 | 10.90 % | ||
| Both | 3 | 0.90 % | ||
| RVG | 13 | 3.90 % | ||
| Total | 338 | 100.0 % | ||
| Do you agree that using AI to analyze panoramic radiographs is a better option than using CBCT for determining morphology? | Agree | 130 | 38.5 % | <0.001, S |
| Disagree | 38 | 11.2 % | ||
| Neither agree nor disagree | 170 | 50.3 % | ||
| Total | 338 | 100.0 % | ||
| How feasible is AI for daily endodontic clinical practice in terms of ease of operation and cost? |
Fairly feasible | 128 | 37.9 % | <0.001, S |
| Feasible | 84 | 24.9 % | ||
| Less feasible | 89 | 26.3 % | ||
| Not feasible | 22 | 6.5 % | ||
| Very feasible | 15 | 4.4 % | ||
| Total | 338 | 100.0 % | ||
| Will you consider using AI for determining root canalmorphology | Likely | 146 | 43.2 % | <0.001, S |
| Neutral | 41 | 12.1 % | ||
| Unlikely | 5 | 1.5 % | ||
| Very likely | 146 | 43.2 % | ||
| Total | 338 | 100.0 % |
| Don't know | No | Yes | P value | ||
|---|---|---|---|---|---|
| Can artificial intelligence (AI) technology be utilized to enhance the success rates of endodontic treatment? | n | 51 | 8 | 279 | <0.001, S |
| % | 15.10 % | 2.40 % | 82.50 % | ||
| Can AI enhance the accuracy of root canal morphology determination in endodontics? | n | 58 | 6 | 274 | <0.001, S |
| % | 17.20 % | 1.80 % | 81.10 % | ||
| Do you consider yourself trained for operating AI models and deep learning for root canal configuration determination | n | 78 | 206 | 54 | <0.001, S |
| % | 23.10 % | 60.90 % | 16.00 % | ||
| Are you currently using any AI models or software to determine root canal morphology? Er | n | 14 | 308 | 16 | <0.001, S |
| % | 4.10 % | 91.10 % | 4.70 % | ||
| Is there a need for training programs & and workshops regarding the use of AI in root canal morphology determination? | n | 20 | 16 | 302 | <0.001, S |
| % | 5.90 % | 4.70 % | 89.30 % | ||
| Do you think that more research is required to prove the accuracy of AI for root canal morphology determination? | n | 21 | 12 | 305 | <0.001, S |
| % | 6.20 % | 3.60 % | 90.20 % | ||
| Fully aware | Not aware | Partially aware | P value | ||
| Are you aware of the applications of AI in the field of endodontics? | n | 43 | 64 | 231 | <0.001, S |
| % | 12.70 % | 18.90 % | 68.30 % | ||
| Are you familiar with the concept of artificial intelligence (AI)? | n | 76 | 19 | 243 | <0.001, S |
| % | 22.50 % | 5.60 % | 71.90 % | ||
| Do you know about the role of AI in determining root canal morphology? Please select one of the following options: | n | 38 | 98 | 202 | <0.001, S |
| % | 11.20 % | 29.00 % | 59.80 % |
| Always | Never | Often | Rarely | Sometimes | P value | ||
|---|---|---|---|---|---|---|---|
| How frequently do you utilize 3D radiographic techniques such as CBCT to determine root canal configuration? | n | 1 | 27 | 57 | 92 | 161 | <0.001, S |
| % | 0.30 % | 8.00 % | 16.90 % | 27.20 % | 47.60 % | ||
| Have you experienced any root canal failures due to undetected extra roots or C-shaped canals? | n | 3 | 33 | 40 | 72 | 190 | <0.001, S |
| % | 0.90 % | 9.80 % | 11.80 % | 21.30 % | 56.20 % |
Graphs.
5. Discussion
Results of the present study revealed that 68.3 % of the participants were partially aware, and 12.2 % were fully aware of AI applications in Endodontics. Additionally, 71.9 % of the respondents were partially familiar with AI. This is consistent with findings from previous studies which showed that 63.5 % were aware of AI and 38 % were aware of AI apps.22 Similar results were observed in other studies where more than 70 % of participants were aware of AI.23,24 This highlights the importance of AI and its potential applications among Dental surgeons emphasizing the need for formal training and continuous education to facilitate its effective integration into clinical practice.
Contrastingly, another study found that only 49.40 % of dental professionals were aware of AI, and just 42.2 % knew about its applications in dentistry.25 A possible explanation for the lack of familiarity could be the preference for traditional methods and hesitancy toward adopting newer techniques.
In the present study, majority of the respondents were aware of Artificial Neural Networks (ANN) and deep learning, but less familiar with models such as CNN, U-Net, and Inception V3. This observation is consistent with one of the previous studies.22 However, it contrasts with the results reported by other studies.23
The present study revealed that approximately 30 % of the respondents were unaware of any AI models, a finding comparable to a previous study in which 36.5 % of practitioners lacked awareness of AI.22 Although the role of AI in determining root canal morphology has been less explored, 59.8 % of endodontists in this study acknowledged its importance. This contrasts with a previous study, where only 29.5 % acknowledged the application of AI in oral diagnosis and 39.5 % considered its potential application in the field of medicine and dentistry.23
The majority of our study participants, comprising 84.3 % practicing Endodontists, preferred using an IOPAR over CBCT in clinical practice to determine root canal morphology. This preference is similar to a study where 71 % of the participants were aware of CBCT use in general dental practice, while only nearly a quarter (28 %) were using it in their practice.
Another study found that only 33 % of the practitioners were using IOPA X-ray, and 17 % were using digital radiography for diagnosis.26 One potential explanation for the preference of IOPAR over CBCT and AI in daily endodontic clinical practice could be the ease of operation, cost-effectiveness, and the high radiation exposure associated with CBCT, which is a potential hazard.AI requires minimal manual input after training, reducing observer variability, while CBCT interpretation depends on clinician expertise. AI automates detection and improves efficiency; CBCT provides detailed 3D images essential for complex cases.
Most of our respondents were hesitant to use 3D radiographic techniques frequently. This reluctance may be due to a lack of formal training and education in the specialized field. Moreover, participants noted experiencing root canal failures due to undetected extra roots or C-shaped canals.
A significantly higher proportion of participants in our study did not consider themselves adequately trained for operating AI models and were also not using any AI models or software's for determining root canal configuration. Moreover, 90.2 % of the endodontists felt the need for more research to prove the accuracy of AI in endodontics. These findings contrast with those of a previous study, which reported 44.5 % of the participants were using AI for diagnostic support, while 25.8 % reported its use for patient management.27
In some recent reviews, it was demonstrated that the utilization of AI has resulted in enhanced treatment plans, subsequently leading to an increased success rate of endodontic treatment outcomes.28
This finding perfectly aligns with the opinions of 82.5 % of endodontists surveyed in our study, who concurred that AI holds the potential to augment success rates in endodontic treatments. However, about 89.3 % of the endodontists felt the need for additional training in AI to use it more effectively in daily clinical practice which aligns with the results of Kalaimani et al. where 70 % expressed the need for training in AI for all dental students and contrasts with the study by some authors where only 32.2 % felt the need for AI training at both undergraduate and postgraduate levels.29
Only about 9 % of the endodontists included in our study are currently using any AI models or software for root canal morphology determination, which shows that even though they may be aware of its prospective role, they were found to be lacking in their practices. This contrasts with a study that found 47 % of practitioners used AI to simplify treatment procedures, while another study revealed that 25.4 % of participants reported using AI software or applications in dentistry.
The present study showed that 86.4 % of the respondents were likely to use AI for determining root canal morphology in the future. (p < 0.001).
6. Limitations
This was a cross-sectional study that captured only a few current perspectives and did not examine how these views might change as AI technologies evolve. The absence of open-ended questions limited a deeper understanding of participants' opinions on AI challenges. Additionally, the short survey period may not have reflected broader trends in AI adoption, and the self-administered format could have introduced response bias. Despite some limitations related to sample size and study design, this study offers a valuable foundation for future research on the role of AI in endodontics. The relatively small sample size remains a significant limitation, highlighting the necessity for larger, more methodologically sound studies to verify and extend these findings.
7. Conclusion
The rise of Artificial Intelligence (AI) in endodontics marks a significant progress in both assessment and treatment methods. Root canal morphology, known for its complex variability and clinical unpredictability, has greatly influenced endodontic success or failure. This study highlights the increasing awareness and potential of AI in endodontics, especially in improving the determination of root canal shapes and overall treatment results. While many endodontists recognize AI's benefits, such as boosting diagnostic precision and treatment effectiveness, most are still unfamiliar with how to apply it practically. Major obstacles like limited training, high costs, and lack of familiarity with AI systems continue to hinder adoption, emphasizing the need for ongoing research, thorough education, and regular training programs like workshops and structured learning modules.
Looking ahead, efforts should focus on closing the knowledge gap, overcoming the limitations to AI adoption, and encouraging its integration into clinical practice to improve patient care and treatment efficiency in endodontics. Additionally, ethical issues related to patient privacy, data security, and potential biases need careful attention to ensure the responsible use of AI technologies in this field.
Informed consent statement
Informed consent was obtained from all subjects involved in the study.
Patient/guardian consent
This study is a cross-sectional survey-based research; therefore, patient or guardian consent was not obtained.
Source of funding
Nil.
Declaration of competing 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.
Acknowledgements
The authors would like to thank all the participants who agreed to participate in this study.
Footnotes
This article is part of a special issue entitled: AI matters published in Journal of Oral Biology and Craniofacial Research.
Contributor Information
Mohd Irfan Ansari, Email: irfanansari77@live.in.
Neelam Singh, Email: nsingh@jmi.ac.in.
Shahnaz Mansoori, Email: drshahnaz84@gmail.com.
Simran Uppal, Email: simranuppal2142@gmail.com.
Abhishek Mehta, Email: amehta@jmi.ac.in.
Sweta Rastogi, Email: srastogi@jmi.ac.in.
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