Table 2.
Details of the studies that have used AI based models in various specialties of dentistry for diagnosis, treatment planning, clinical decision making, predicting the need for treatment, and predicting the prognosis.
| Serial no | Authors | Year of publication | Algorithm Architecture |
Objective of the study | No. of images/photographs for testing | Study factor | Modality | Evaluation accuracy/average accuracy | Comparison if any | Results (+)effective, (−)non effective (N) neutral |
Outcomes | Authors suggestions/recommendations |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Devito et al.11 | 2008 | ANNs | AI based model for diagnosing the proximal dental caries | 160 | Tooth Decay | Bite-wing Radiographs | ROC curve area of 0.884 | 25 examiners | (+) Effective | This neural network could improve the performance of diagnosing proximal caries. | None |
| 2 | Xie et al.12 | 2010 | ANNs | ANN based AI model for deciding if extractions are necessary prior to orthodontic treatment | 200 | Tooth malocclusion | Lateral cephalometric radiographs | Accuracy of 80% | Not mentioned | (+)Effective | ANN was effective in determining whether extraction or non-extraction treatment was best for malocclusion patients | None |
| 3 | Saghiri et al.13 | 2012 | ANNs | ANN based AI model for determining the working length | 50 | Tooth | Human cadavers | Accuracy of 96% | 2 Endodontists | (+)Effective | The accuracy of ANN was more than the endodontists | The ANN model is accurate method for working length determination |
| 4 | Saghiri et al.14 | 2012 | ANNs | ANN system for locating the minor apical foramen (AF) | 50 | Tooth | Human dried skull | Accuracy of 93% | Endodontists | (+)Effective | ANN can useful for secondary opinion for locating the AF on radiographs and it can be helpful in enhancing the accuracy in determining the working length | ANN can be used for decision making similar clinical scenarios |
| 5 | Jung et al.15 | 2016 | ANNs | Artificial Intelligence expert system for orthodontic decision-making of required permanent tooth extraction | 156 | Tooth malocclusion | Lateral cephalometric radiographs | Accuracy of 92% | 1 Experienced orthodontists | (+)Effective | The success rates of the models were 92% for the system's recommendations for extraction vs non extraction | AI expert systems with neural network machine learning could be useful in orthodontics |
| 6 | Johari et al.16 | 2017 | PNNs | Probabilistic Neural Network (PNN) for diagnosing (VRFs) in intact and the teeth that has undergone endodontic treatment | 240 | Tooth | CBCT and periapical radiographs | Accuracy of 96.6, sensitivity of 93.3 and specificity of 100% | Not mentioned | (+)Effective | The designed neural network can be used as a proper model for the diagnosis of VRFs on CBCT images of endodontically treated and intact teeth; CBCT images were more effective than periapical radiographs. | None |
| 7 | Tobel et al.17 | 2017 | CNNs | An automated technique for staging the development of lower third molar. | 200 | Tooth | Panoramic radiographs (OPG) | Mean ICC was 0.95 | 2 observers | (+)Effective | Deep CNN based AI system demonstrated similar results to the results demonstrated by other trained examiners. | Further optimization is required to achieve a fully automated system for estimating the dental age. |
| 8 | Aubreville et al.18 | 2017 | CNNs | AI based automatic system for diagnosing (OSCC) oral squamous cell carcinoma | 7894 | Oral cavity | Confocal laser endomicroscopy (CLE) images | AUC of 0.96 and a mean accuracy of 88.3%, sensitivity 86.6%, specificity 90% | Not clear | (+)Effective | This approach was found to outperform the state of the art in CLE image recognition | None |
| 9 | Imangaliyev et al.19 | 2017 | CNNs | CNN model for the automatic classification of red fluorescent dental plaque images. | 427 | Tooth | Quantitative light-induced fluorescence images | Predictive accuracy of 0.89% | Reference models | (+)Effective | CNN model prediction performance was higher than other models. | None |
| 10 | Niño-Sandoval et al.20 | 2017 | ANNs | AI based model for predicting the mandibular morphology | 229 | Anatomical landmarks | Lateral cephalograms | Coefficients from 0.84 until 0.99 | Support vector regression | (+)Effective | This model demonstrated high predictability ability | This model may be the key for facial reconstruction |
| 11 | Zhang et al.21 | 2018 | ANNs | ANN for predicting postoperative facial swelling following the extraction of impacted mandibular third molars. | 100 | Face | Data set | Accuracy of 98.00% | 1 Oral surgeon | (+)Effective | This AI based model proved to be an accurate in predicting of the facial swelling following the extraction of impacted mandibular third molars. | None |
| 12 | Lee et al.22 | 2018 | CNNs | (DCNN)-Based Computer-Assisted Diagnosis (CAD) systems. Single-Column DCNN (SC-DCNN), Single-Column with Data Augmentation DCNN (SC-DCNN Augment) and Multicolumn DCNN (MC-DCNN). | 200 | Face | Panoramic radiographs (OPG) | (AUC) values obtained using SC-DCNN was 0.9763, SC-DCNN (Augment) was 0.9991, MC-DCNN for 0.9987 | 2 Experienced oral and maxillofacial radiologists | (+)Effective | The system that was based on DCNN was effective in detecting osteoporosis and also demonstrated high agreement with the experienced oral and maxillofacial radiologists | None |
| 13 | Lee et al.23 | 2018 | CNNs | Diagnosing and predicting of PCT using a computer -assisted detection system based on a deep CNN | 348 | Tooth | Intra oral periapical radiographs | Mean predictive accuracy of 78.9% | 3 calibrated board-certified periodontists | (+)Effective | The DCNN based model was effective and efficient in diagnosing and predicting of (PCT). | Further optimization of the PCT dataset is required for improvement |
| 14 | Thanathornwong24 | 2018 | Bayesian network (BNs) | Bayesian network (BN) for predicting the need for orthodontic treatment. | 1000 | Tooth malocclusion | Data sets | AUC (0.91) | 2 Experienced orthodontists | (+)Effective | This BN based system; and demonstrated promising results with high degree of accuracy in the need for orthodontic treatment. | None |
| 15 | Zhang et al.25 | 2018 | CNNs | Teeth recognition using label tree with cascade network structure. | 200 | Tooth | Intra oral periapical radiographs | Precision of 95.8% | Reference models | (+)Effective | This approach demonstrated a high precision of 95.8% and recall of 96.1%. | None |
| 16 | Lee et al.26 | 2018 | CNNs | AI based deep learning system for detecting and diagnosing dental caries | 600 | Dental caries | Intraoral periapical radiographic images | Mean AUC of 0.890 | 4 calibrated board-certified dentists | (+)Effective | This DCNN based system algorithm performed considerably good in detection dental caries on periapical radiographs. | None |
| 17 | Yauney et al.27 | 2018 | CNNs | AI based system for automated oral health screenings and cross correlations of oral-systemic health | 810 | Periodontium | Intraoral fluorescence images | AUC of 0.677, precision of 0.271, Recall of 0.429 | Dentists | (+)Effective | This automated process was effective in correlating poor periodontal health with systemic health outcomes | Machine learning, can be used for automated diagnoses and systemic health screenings for other diseases |
| 18 | Kök et al.28 | 2019 | ANNs | AI algorithms for determining the stages of the growth and development by cervical vertebrae | 300 | Cervical Vertebrae | Cephalometric radiographs | Mean accuracy of 77.02% | 1 orthodontists | (+)Effective | ANN could be the preferred method for determining cervical vertebrae stages | None |
| 19 | Park JH et al.29 | 2019 | CNNs | Comparing latest deep-CNN based systems for identifying cephalometric landmarks | 283 | Landmarks | Cephalometric radiographs | 5% higher accuracy with (YOLOv3) than Single (SSD) | Single shot multibox detector (SSD) | (+)Effective | You-Only-Look-Once model outperformed in accuracy and computational time than the shot multibox detector | This model can be used in clinical practice for identifying the cephalometric landmarks. |
| 20 | Choi et al.30 | 2019 | ANNs | ANN based model for deciding on surgery/non-surgery and determining extractions. | 316 | Landmarks | Lateral cephalometric radiographs | ICC 0.97–0.99 | 1 Experienced orthodontists | (+)Effective | This ANN based model demonstrated higher success rate in deciding on surgery/non-surgery and was also successful in deciding on the extractions. | This ANN based model will be useful in diagnosing of orthognathic surgery cases. |
| 21 | Patcas. et al.31 | 2019 | CNNs | AI system for describing the impact of orthognathic treatments on facial attractiveness and age appearance | 2164 | Facial landmarks | Facial photographs | Not Clear | Not mentioned | (+)Effective | This CNN based AI system can be used for scoring facial attractiveness and apparent age in patients under orthognathic treatments. | None |
| 22 | Casalegno et al.32 | 2019 | CNNs | AI based model for detecting and localizing dental lesions in Near-Infrared Transillumination (TI) images | 217 | Dental caries | Near-infrared transillumination (TI) imaging | ROC of 83.6 for occlusal and ROC of 84.6% for proximal | Dental experts with clinical experience | (+)Effective | This CNN based model demonstrated promising results with increased speed and accuracy in detecting caries. | None |
| 23 | Fukuda et al.33 | 2019 | CNNs | CNN based AI system for detection of vertical root fracture (VRF) | 60 | Tooth | Panoramic radiographs (OPG) | Precision of 0.93 Recall was 0.75 F- Measure of 0.83. |
2 radiologists and 1 endodontist | (+)Effective | The CNN based AI model is an efficient tool in detecting VRFs | None |
| 24 | Kise et al.34 | 2019 | CNNs | AI system for detection of Sjögren's syndrome (SjS) on CT, and comparing its performance with radiologists | 100 | Salivary glands | Computed tomography (CT) images | Accuracy of 96.0, Sensitivity of 100% and specificity of 92.0% | 6 radiologists | (+)Effective | The deep learning system demonstrated a higher diagnostic performance | Can be used as a diagnostic support while interpreting CT images |
| 25 | Hiraiwa et al.35 | 2019 | CNNs | AI system for classifying root morphologies of mandibular first molars | 760 | Tooth | Cone beam computed tomography (CBCT) Images | Accuracy of 86.9% | 2 radiologists | (+)Effective | The deep learning system demonstrated high accuracy in the differential diagnosis of a single or extra root in the distal roots of mandibular first molars. | None |
| 26 | Tuzoff et al.36 | 2019 | CNNs | CNN based AI system for automatic teeth detection and numbering | 222 | Tooth | Panoramic radiographs (OPG) | Precision of 0.9945 and mean sensitivity of 0.987 | Dental experts | (+)Effective | The performance of the this system was comparable to the level of performance of the experts | This system can simplify the process of filling digital dental charts. |
| 27 | Ekert et al.37 | 2019 | CNNs | CNNs based AI system for detecting apical lesions (ALs) | 2001 | Tooth | Panoramic radiographs (OPG) | AUC of 0.85 (0.04) sensitivity 0.65 and specificity 0.87 | 6 Dentists | (+)Effective | This deep CNN based AI system was successful in detecting apical lesions | None |
| 28 | Murata et al.38 | 2019 | CNNs | AI based system for diagnosing of maxillary sinusitis | 120 | Maxillary sinusitis | Panoramic radiographs (OPG) | Accuracy of 87.5%, sensitivity of 86.7%, specificity of 88.3%, AUC of 0.875 | 2 experienced radiologists, 2 dental residents. | (+)Effective | The AI based deep learning system demonstrated higher diagnostic performance. | The deep-learning system can provide diagnostic support for inexperienced dentists |
| 29 | Chen et al.39 | 2019 | CNNs | CNN based tool package for detecting and numbering the teeth | 250 | Tooth | Intra oral periapical films | Precisions and recalls exceed 90%, IOU of 91% |
3 Dentists | (+)Effective | The results indicate that machines performance was close to the level of a junior dentist | None |
| 30 | Vinayahalingam et al.40 | 2019 | CNNs | CNN based AI system to detect and segment the approximate of inferior alveolar nerve (IAN) to the roots of lower third molars (M3) on OPGs | 81 | Tooth | Panoramic radiographs (OPG) | Mean dice-coefficients for M3s and IAN were 0.947 ± 0.033 and 0.847 ± 0.099 | Portable network graphics (PNG) files as gold standard | (+)Effective | Deep-learning is an encouraging approach to segment anatomical structures | Further enhancement of the algorithm is advised to improve the accuracy |
| 31 | Mallishery et al.41 | 2019 | ANNs | Machine learning to generate an algorithm which can help predict the difficulty level of the case and decide on referral | 500 | Tooth | Data Set | Sensitivity of 94.96% | 2 pre-calibrated endodontists | (+)Effective | This study provides an option for automation for increasing the speed of decision-making and referrals. | An AAE endodontic case difficulty assessment form when utilized along with machine learning can assist general dentists in rapid assessment of the case difficulty |
| 32 | Patcas et al.42 | 2019 | CNNs | AI system for evaluating the facial attractiveness of patients who have undergone treatment for clefts and the facial attractiveness of controls and to compare these results with panel ratings performed by laypeople, orthodontists, and oral surgeons | 30 | Face | Frontal and profile images | Cleft cases (all Ps ≥ 0.19), for control group (all Ps ≤ 0.02) | 15 laypeople, 14 orthodontists, and 10 oral surgeons | (−)Non Effective | AI system scores were comparable with the scores of the other groups for the cleft patients, but the scores were lower for the controls | There is a need for further refinement in this AI based system |
| 33 | Krois et al.43 | 2019 | CNNs | Deep- CNN based system for detecting periodontal bone loss | 2001 | Periodontium | Panoramic radiographs (OPG) | Predictive accuracy of 81% and were similar to the examiners | 6 Experienced dentists | (N) Neutral | CNN demonstrated similar results to that of the dentists in detecting periodontal bone loss. | Machine -learning based technologies can reduce the dentists' diagnostic efforts. |
| 34 | Ariji et al.44 | 2019 | CNNs | AI system for diagnosing metastasis of lymph node. | 441 | Cervical lymph nodes | Computed tomography (CT) images | Accuracy of 78.2%, sensitivity of 75.4%, specificity of 81.0%, positive predictive value of 79.9%, negative predictive value of 77.1%, and ROC of 0.80 | Not clear | (N) Neutral | The diagnostic results of the CNN based system were similar to the results of the radiologists. | This CNN based system is a valuable for diagnostic support. |
| 35 | Ariji et al.45 | 2019 | CNNs | Performance of deep learning classification in diagnosing extranodal extension of cervical lymph node metastases in CT images | 703 | Cervical lymph nodes | Computed tomography (CT) images | Accuracy of 84.0% | 4 Radiologists | (+)Effective | The deep learning diagnostic performance in extra nodal extension was significantly higher when compared with the performance of the radiologists | This method is expected to improve diagnostic accuracy by further study with increasing sample size of patients. |
| 36 | Hung et al.46 | 2019 | CNNs | AI based model for predicting root caries | 5135 | Root caries | Data set | Accuracy of 97.1%, precision of 95.1%, sensitivity of 99.6% and specificity of 94.3% AUC of 0.997 |
Trained medical personnel | (+)Effective | This model perform well and can be allowed for clinical implementation | Can be utilized by both dental and non-dental professionals |
| 37 | Kim et al.47 | 2019 | CNNs | AI based (CNNs) for diagnosing maxillary sinusitis | 200 | Maxillary sinusitis | Waters' view radiographs | AUC of 0.93 for the temporal and 0.88 for geographic external |
5 Radiologists | (+)Effective | AI based (CNNs) demonstrated statistically significantly higher AUC than radiologist in both test sets | None |
| 38 | Schwendicke et al.48 | 2020 | CNNs | AI based (CNNs) to detect caries lesions in near-infrared-light transillumination (NILT) images. | 226 | Tooth decay | NILT images | The mean) AUC of 0.74, Sensitivity of 0.59 and specificity of 0.76 PPV was 0.63 and NPV was 0.73 |
2 Experienced dentists | (+)Effective | The model demonstrated satisfying discriminatory ability to detect caries lesions. | None |
| 39 | Kunz et al.49 | 2020 | CNNs | An automated cephalometric X-ray analysis using a specialized (AI) algorithm | 50 | Landmarks | Cephalometric radiographs | Not clear | 12 experienced examiners | (+)Effective | AI algorithm was able to analyze unknown cephalometric X-rays similar to the quality level of the experienced human examiners | None |
| 40 | Hwang et al.50 | 2020 | CNNs | Deep -learning based automated system for detecting the patterns of 80 cephalometric landmarks | 283 | Landmarks | Cephalometric radiographs | Not Mentioned | Human examiners | (+)Effective | This system accuracy in identifying of cephalometric landmarks similar to the human examiners | This system might be a viable option when repeated identification of multiple cephalometric landmarks. |
| 41 | Lee et al.51 | 2020 | CNNs | Deep (CNNs), on the classification of specific features of osteoporosis | 136 | Face | Dental panoramic radiographs (DPRs) | ROC of 0.858 | Gold standard reference models | (+)Effective | This Deep (CNNs), could of use and reliable system for automated screening of osteoporosis patients. | None |
| 42 | Patil et al.52 | 2020 | ANNs | ANN for gender determination | 509 | Mandible | Panoramic radiographs (OPG) | Accuracy of 75% | 1 experienced oral and maxillofacial radiologist | (+)Effective | ANN proved as a good tool for predicting the gender and can be applied in the forensic sciences for near accurate results. | This automated application is promising for identifying gender or age with minimal errors |
| 43 | Yu et al.53 | 2020 | CNNs | AI based skeletal diagnostic system | 5890 | Anatomical landmarks | Lateral cephalograms | Mean AUC of >95% | 2 orthodontists | (+)Effective | This model demonstrated excellent performance for skeletal orthodontic diagnosis | None |
ANNs = Artificial Neural Networks, CNNs = Convolutional Neural Networks, DCNNs = Deep Convolutional Neural Networks, BN = Bayesian Network, PNN = Probabilistic Neural Network, ROC = Receiver Operating Characteristic curve, AUC = Area Under the Curve, ICC = Intraclass Correlation Coefficient, F = F- measure, VRF = Vertical Root Fracture, PTC = Periodontal Compromised Teeth, Positive/Negative Predictive Values (PPV/NPV).