Efficacy of deep learning models and dental professionals in identifying dental implants Veena Benakatti, Ramesh P. Nayakar, Mallikarjun Anandhalli, Rohit C. Sukhasare Imaging Science in Dentistry, 2025 Purpose: Implant identification is a pressing concern in dental implantology, and artificial intelligence (AI) has been evaluated for this purpose. YOLO, a state-of-the-art object detection model, is suitable for medical imaging; therefore, this study assessed YOLOv11-the latest iteration-for identifying 10 implant types in Indian clinical settings and compared its accuracy to that of dental professionals. Materials and Methods: A dataset of 3,161 radiographs, comprising both periapical and panoramic images of 10 implant types, was annotated and used to train and test YOLOv11. Training was performed on Google Colab using an NVIDIA Tesla T4 GPU (16 GB VRAM). A random sample of 200 radiographs was selected from the test dataset and presented to 50 dental practitioners for implant identification. Their responses were analysed and compared, using the chi-square test for statistical significance. Results: YOLOv11 achieved precision of 0.87, recall of 0.85, an F1-score of 0.86, and an mAP50 of 0.899. The model achieved excellent classification accuracy for Adin (95%), MIS (94%), Bego (92%), ITI (96%), and Bicon (97%). Moderate accuracy was noted for Noris (82%), Osstem (85%), AlphaBio (88%), Dentium (77%), and Bioline (75%). YOLOv11 demonstrated higher overall accuracy and consistency than dental professionals. Dentists' accuracy ranged from 27% to 49%, whereas that of YOLOv11 ranged from 92% to 100%. Conclusion: YOLOv11 recognised most implant classes with over 90% accuracy, surpassing traditional manual techniques in implant detection. Although the model is dependable and efficient, certain aspects require improvement. The study also emphasises the significance of a region-specific approach for clinical relevance.
Advanced deep learning techniques for recognition of dental implants Veena Benakatti, Ramesh P. Nayakar, Mallikarjun Anandhalli, Rohit sukhasare Journal of Oral Biology and Craniofacial Research, 2025 Background: Dental implants are the most accepted prosthetic alternative for missing teeth. With growing demands, several manufacturers have entered the market and produce a variety of implant brands creating a challenge for clinicians to identify the implant when the necessity arises. Currently, radiographs are the only tools for implant identification which is inherently a complex process, hence the need for implant identification technique. Artificial intelligence capable of analysing images in a radiograph and predicting implant type is an efficient tool. The study evaluated an advanced deep learning technique, DEtection TRanformer for implant identification. Methods: A transformer-based deep learning technique, DEtection TRanformer was trained to identify implants in radiographs. A dataset of 1138 images consisting of five implant types captured from periapical and panoramic radiographs was chosen for the study. After augmentation, a dataset of 1744 images was secured and then split into training, validation and test datasets for the model. The model was trained and evaluated for its performance. Results: The model achieved an overall precision of 0.83 and a recall score of 0.89. The model achieved an F1-score of 0.82 indicating a strong balance between recall and precision. The Precision-Recall Curve, with an AUC of 0.96, showed that the model performed well across various thresholds. The training and validation graphs showed a consistent decrease in the loss functions across classes. Conclusion: The model showed high performance on the training data, though it faced challenges with unseen validation data. High precision, recall and F1 score indicate the model's potential for implant identification. Optimizing this model for a balance between accuracy and efficiency will be necessary for real-time medical imaging applications.
Evaluation of Marginal Bone Loss and Esthetics in Screw vs Cement-retained Single Implant Prosthesis: A Systematic Review Jayashree Sajjanar, Vaishnavi Mohite, Veena Benakatti, Shylesh Kumar Basaralu Shivakumar, Zehra Rana, Ravi Teja Boppana Journal of Contemporary Dental Practice, 2025 PURPOSE The aim of this systematic review was to evaluate marginal bone loss and esthetics in single implant zirconia prostheses of screw- and cement-retained prosthesis. MATERIALS AND METHODS An electronic search on MEDLINE/PubMed, Google Scholar, Scopus, Embase, Web of Science was conducted to identify randomized controlled clinical trials (RCTs) published within the past five years from 2018 upto January 2023. Additionally, a manual search of relevant references was performed. Two reviewers independently selected studies based on predefined inclusion criteria. Marginal bone loss values and esthetic parameters were extracted, and meta-analysis was conducted where applicable. RESULTS The initial search yielded 61 articles, of which nine articles were thoroughly analyzed, resulting in five RCTs which were included. Due to limited available data on esthetic parameters, meta-analysis could not be performed. However, 164 implants revealed that screw-retained implant restorations were more likely to retain screws than cemented ones after one year (z-test value = 3.18, p = 0.001), with a mean difference of -0.30 (95% CI). CONCLUSION Marginal bone loss around implants was lower in screw-retained prostheses compared to cement-retained ones. These findings support the preference for zirconia prostheses in esthetically demanding cases. CLINICAL SIGNIFICANCE Screw-retained ceramic prosthesis exhibit optimal esthetics and minimal marginal bone loss. Cement-retained prosthesis fail in terms of marginal bone loss and esthetics due to excess cement around the prostheses. Inadvertence of excess cement removal around implant prosthesis led to inflammation of peri-implant tissue, which consequently increased probing depth. A stringent protocol in the procedure of cementation of prosthesis aids in the removal of excess cement, which reduces marginal bone loss and enhances esthetic. How to cite this article: Sajjanar J, Mohite V, Benakatti V, et al. Evaluation of Marginal Bone Loss and Esthetics in Screw vs Cement-retained Single Implant Prosthesis: A Systematic Review. J Contemp Dent Pract 2025;26(1):103-109.
Accuracy of machine learning in identification of dental implant systems in radiographs-A systematic review and meta-analysis Veena Benakatti, RameshP Nayakar, Mallikarjun Anandhalli, Vasanti Lagali-Jirge Journal of Indian Academy of Oral Medicine and Radiology, 2022 Machine learning has played a promising role in medical diagnosis. The aim of this systematic review was to evaluate the accuracy of machine learning in identification of dental implant systems from radiographs. This systematic review was conducted by searching four electronic databases, PubMed, SCOPUS, Cochrane Library, and Google Scholar. Inclusion criteria were studies that used machine learning for implant identification. Our search yielded 87,189 studies, of which a total of eight studies were found which used machine learning for implant identification. Of the included studies, three studies provided the required data to conduct meta-analysis. The overall pooled estimate of accuracy of the three included studies was 95.43%. Machine learning appears to be practically efficient in implant recognition. The findings of this review suggested an inadequate reporting of studies due to a lack of standardized guidelines for reporting and conducting the studies that investigate machine learning in implant identification. This could limit the reliable interpretation of the reported accuracy.
Machine learning for identification of dental implant systems based on shape - A descriptive study VeenaBasappa Benakatti, RameshP Nayakar, Mallikarjun Anandhalli Journal of Indian Prosthodontic Society, 2021 Aim: To evaluate the efficacy of machine learning in identification of dental implant systems from panoramic radiographs based on the shape. Settings and Design: In vitro–Descriptive study Materials and Methods: A Dataset of digital panoramic radiographs of three dental implant systems were obtained. The images were divided into two datasets: one for training and another for testing of the machine learning models. Machine learning algorithms namely, support vector machine, logistic regression, K Nearest neighbor and X boost classifiers were trained to classify implant systems from radiographs, based on the shape using Hu and Eigen values. Performance of algorithms was evaluated by its classification accuracy using the test dataset. Statistical Analysis Used: Accuracy and recover operating characteristic (ROC) curve were calculated to analyze the performance of the model. Results: The classifiers tested in the study were able to identify the implant systems with an average accuracy of 0.67. Of the classifiers trained, logistic regression showed best overall performance followed by SVM, KNN and X boost classifiers. Conclusions: Machine learning models tested in the study are proficient enough to identify dental implant systems; hence we are proposing machine learning as a method for implant identification and can be generalized with a larger dataset and more cross sectional studies.
Restoration of debilitated dentition in amelogenesis imperfecta using Hobo twin stage technique Jayashree Sajjanar, Arunkumar B. Sajjanar, Veena B Benakatti, Grishmi Niswade Journal of Datta Meghe Institute of Medical Sciences University, 2021 Amelogenesis imperfecta (AI) is a hereditary disorder that displays a group of conditions which cause developmental alterations in the structure of enamel. This disorder has an adverse impact on oral health and quality of life of the individual. The correction of such severely worn out dentition may require extensive restorative treatment to achieve appropriate results. It is important to identify the factors that contribute to the excessive wear and loss of vertical dimension. The correction of the defects has to be done without violating the biological or mechanical principles. Rehabilitation in such patients improves esthetics, function, and comfort. This case report presents a systematic approach in rehabilitating a case of AI hypomature type using full mouth metal-reinforced porcelain and metal restorations.
Evaluation of antibacterial effect and dimensional stability of self-disinfecting irreversible hydrocolloid: An in vitro study Veena B Benakatti, Abhijit P Patil, Jayashee Sajjanar, Supriya S Shetye, Ulhas N Amasi, Raghunath Patil Journal of Contemporary Dental Practice, 2017 Aim This study evaluated the antibacterial activity and dimensional stability of irreversible hydrocolloids mixed with different concentrations of chlorhexidine gluconate instead of water. Materials and methods Experimental specimens (45 specimens) were prepared and allocated into three groups of 15 each. Group I: Impression material mixed with distilled water served as control. Groups II and III were prepared with 0.12 and 0.2% chlorhexidine gluconate solution, respectively. Specimens in each group were subjected to tests for dimensional stability. For antimicrobial activity, 30 specimens were prepared and allocated into three groups of 10 each named as group I (control), group II (0.12% chlorhexidine gluconate), and group III (0.2% chlorhexidine gluconate) similar to specimens for dimensional stability. Statistical analysis was performed using a one-way analysis of variance (ANOVA) and Tukey test. Results Zones of inhibition were observed around test specimens, but not around control specimens; there was a significant intergroup difference in the diameters of the inhibition zones. In the test for dimensional stability, no significant differences were detected among groups, and the accuracy was clinically acceptable. Conclusion Irreversible hydrocolloid impression material mixed with chlorhexidine exhibits varying degrees of antibacterial activity without influencing the dimensional stability of set material. Clinical significance Many contagious diseases can be prevented by practical control of infection in the dental office. Chlorhexidine gluconate, as a mixing liquid, ensures disinfection of impression, and this method of disinfection is more convenient and avoids extra effort as in other disinfection techniques. How to cite this article Benakatti VB, Patil AP, Sajjanar J, Shetye SS, Amasi UN, Patil R. Evaluation of Antibacterial Effect and Dimensional Stability of Self-disinfecting Irreversible Hydrocolloid: An in vitro Study. J Contemp Dent Pract 2017;18(10):887-892.
