Revolutionizing Dental Diagnostics: A Comprehensive Review of Convolutional Neural Networks (CNN) in Dentistry

International Journal of Dental Science and Innovative Research (IJDSIR) IJDSIR : Dental Publication Service Available Online at:www.ijdsir.com Volume – 8, Issue – 6, November – 2025, Page No. : 27 - 34 Corresponding Author: Dr Komal Bhosle, ijdsir, Volume – 8 Issue - 6, Page No. : 27 - 34 Page27 ISSN: 2581-5989 PubMed - National Library of Medicine - ID: 101738774 Revolutionizing Dental Diagnostics: A Comprehensive Review of Convolutional Neural Networks (CNN) in Dentistry 1Dr Komal Bhosle, BDS, MPH, Goregaon Dental Centre, India 2Dr Shireen Singh, BDS, MDS Periodontology and Oral Implantology, The White Medical Hospital, Pathankot 3Dr Varsha Aher, MDS OMDR, Goregaon Dental Centre, Mumbai, India 4Dr Aishwarya Malu, BDS, MDS Pedodontics, Goregaon Dental Centre, India 5Dr Printy Karanwal, BDS, Goregaon Dental Centre, India 6Dr Harshvardhan Narendra Jain, BDS, MCP, FAD, Mumbai, India Corresponding Author: Dr Komal Bhosle, BDS, MPH, Goregaon Dental Centre, India Citation of this Article: Dr Komal Bhosle, Dr Shireen Singh, Dr Varsha Aher, Dr Aishwarya Malu, Dr Printy Karanwal, Dr Harshvardhan Narendra Jain, “Revolutionizing Dental Diagnostics: A Comprehensive Review of Convolutional Neural Networks (CNN) in Dentistry”, IJDSIRNovember – 2025, Volume – 8, Issue – 6, P. No. 27 – 34. Copyright: © 2025, Dr Komal Bhosle, et al. This is an open access journal and article distributed under the terms of the creative common’s attribution non-commercial License. Which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given, and the new creations are licensed under the identical terms. Type of Publication: Review Article Conflicts of Interest: Nil Abstract This article reviews the application of Convolutional Neural Networks (CNNs) in dentistry, highlighting how deep learning has transformed diagnostic and treatment processes. CNNs, a type of deep learning algorithm designed for image analysis, are used across various dental specializations including radiology, orthodontics, periodontics, prosthodontics, pedodontics, endodontics and oral surgery. The review explains CNNs data driven methods, particularly its ability to extract, classify and segment medical images such as dental radiographs, CT and MRI scans. Key benefits include improved diagnostic accuracy, faster and more reliable results, automated evaluations and enhanced planning for procedures such as implant placement and age estimation in forensic dentistry. The article concludes that CNNs substantially advance dental diagnostics and treatment planning by increasing efficiency, reducing manual errors and providing early intervention for oral cancers. By reducing diagnostic time and minimizing human error, CNNs have the potential to revolutionize preventive dentistry and improve long term patient outcomes. However, CNNs face several challenges and limitations including data related issues such as absence of highquality data, obstacles in advanced infrastructure, development and maintenance cost and several ethical and legal concerns. Ultimately AI, including CNNs, is expected to serve as crucial support system for dentists, assisting clinicians and researchers without replacing human critical thinking and oversight. Keywords: Ameloblastoma, Convolutional Neural Networks, MRI Scans, X-rays Dr Komal Bhosle, et al. International Journal of Dental Science and Innovative Research (IJDSIR) ©2025 IJDSIR, All Rights Reserved Page28 Page28 Page28 Page28 Page28 Page28 Page28 Page28 Page28 Page28 Page28 Page28 Page28 Page28 Page28 Page28 Page28 Page28 Page28 Introduction Artificial Intelligence (AI) is the term which represents the big umbrella term to how it makes machines think, reason and solve problems by thinking as human brain 1. This leads to the subset of AI which focuses on machines learning from data 2. Then the deep learning which is the subset of Machine Learning. This used Artificial Neural Networks which have many programmed layers within it. Hence the layers are called “deep” 3. Convolutional Neural Network (CNN) is a type of deep learning algorithm 4. The foundational difference comes where deep learning is highly based on analyzing the data or information provided to learn and making predictions from the data. Whereas CNN is based majorly on learning and analyzing images, videos and their objects, edges and shapes 5. CNNs are extensively applied in different branches of dentistry, due to their ability to assist in diagnosis, treatment planning and research. To highlight their applications based on the branches of dentistry: CNN analyzes the dental radiographs, detection of caries and periodontal diseases 6. While coming to orthodontics CNN can help with cephalometric analysis. The indirect use of CNN in implants would be when it analyses the bone segmentation images for implant planning. Early cancer detection, age estimation in forensic dentistry and early detection of dental anomalies. CNN, along with deep learning, considering all the data provided and the analysis is helping dentists on a broad scale. CNN is being used in the field of medicine for medical image analysis 7, assisting doctors in procedures like dermatology, ophthalmology, radiology and oncology 2,3. It extracts images from CT and MRI and helps in diagnosis of various health care problems using different types of medical imaging 8. CNNs perform primary tasks such as categorizing an image into specific class, identification of location of lesions, organs or other objects of interest. Segmentation is a function where the opacity or radiolucency of pathology or an organ can be precisely identified 2. To understand the working of CNN, let’s understand where deep learning helps. Deep learning works on the non-image data like medical records, definitions, and all the other forms in which there is information available about the teeth and their structures. Then all this information is loaded into the system 9. On coming to CNN, we need to provide high quality images or X-rays just like how a dentist would look at an X-ray. The CNN then filters these images into detailed bits such as all the edges, shapes and textures 10. It then detects different basic and complex features, and further by activation function decides which patterns are important and what needs to be further looked up on 10. It then decides to provide and gather the detected features and make a final decision. The information is then looked up and just like a dentist’s experience, CNN learns from feedback and more information from other uploaded images and improves to accurately recognize dental problems 6. CNNs enhance diagnostic efficiency and accuracy in dental field, and it is expected that it can come close to human competence levels 6. Methodology To review the literature, studies were selected from PubMed and Google Scholar, without restrictions on publication year, to provide a comprehensive overview of current knowledge regarding the application of Convolutional Neural Networks (CNN) in dentistry. The review focuses on evaluating the effectiveness and diagnostic performance of CNNs in various dental specialties. Search terms included: “Convolutional Neural Networks”, “Deep Learning”, “Artificial Intelligence”, Dentistry”, “Dental imaging”, “Oral Radiology”, “Orthodontics”, and “Periodontics”. The Dr Komal Bhosle, et al. International Journal of Dental Science and Innovative Research (IJDSIR) ©2025 IJDSIR, All Rights Reserved Page29 Page29 Page29 Page29 Page29 Page29 Page29 Page29 Page29 Page29 Page29 Page29 Page29 Page29 Page29 Page29 Page29 Page29 Page29 research encompassed case reports, clinical studies and systematic revies. Working of CNN CNN is the type of deep learning algorithm specifically created for processing data arranged in a grid pattern 5. CNN uses a deep learning approach to automatically identify the most effective features for representing images. CNN typically involves several layers, each designed to serve a particular function. 1. Convolutional LayerThese layers filter the data which is fed to the system, capturing unique features or patterns 4. The convolutional layer applies filters to the input data and extract features which help in reducing the number of parameters 7. 2. Activation FunctionsThe Rectified Linear Unit (ReLU) is a type of activation function commonly used in CNN 5. ReLU introduces non-linearity, allowing the network to perform complex functions and improve performance 7. 3. Pooling LayersPooling layer usually follows convolutional layer, with its main purpose of reducing the size or dimensionality of the convolutional layers output 4. The pooling layer also retains the essential information while discarding unnecessary ones 5. 4. Fully Connected LayersThese are the end layers of the CNN architecture. They process the data for final prediction and classification by mapping the image features by categorizing for classification 6. Applications of CNN in Dental Practice 1. Diagnosis and detectionCNNs are widely used for detection and diagnosis, effectively and quickly in radiological images with increased accuracy 4. CNN can be applied to panoramic radiographs, periapical and bitewing radiographs 1. Research indicates that CNN can detect approximal caries in pediatric patients with high precision. CNN has been seen surpassing the average performance of human dentists by providing high accuracy in diagnosing approximal caries and categorizing the caries according to severity 4. 2. Forensics and age estimationForensic dentistry plays a crucial role in identifying individuals in legal and criminal investigations by determining gender and age 11. CNN developed gender estimation based on dental X-ray images such as orthopantomograms (OPG) by identifying features within the images which differentiate between male and female patients. The ability to determine gender from X-ray with high accuracy is highlighted as an important step in investigating process in forensic dentistry. Dental age estimation by CNN in forensics is more successful and faster than traditional radiographic methods 11. The age estimation is developed by OPGs across different age ranges and differentiating them among specific age-related features 12. 3. Oral Medicine and RadiologyCNN has transformed the method of analysis of dental images and their implementation of artificial intelligence into dental care 5. Radiology is the first field where AI appeared, as it uses the maximum number of images to generate radiographic data 13. This can provide increased accuracy and speed of diagnostic assessments, largely applied in computer-aided diagnosis (CAD) for various medical purposes including analysis of dental images 3. CNN are effective tools for detecting dental pathologies by analyzing images 14. The three primary tasks Dr Komal Bhosle, et al. International Journal of Dental Science and Innovative Research (IJDSIR) ©2025 IJDSIR, All Rights Reserved Page30 Page30 Page30 Page30 Page30 Page30 Page30 Page30 Page30 Page30 Page30 Page30 Page30 Page30 Page30 Page30 Page30 Page30 Page30 performed by CNN are Classification, Detection and Segmentation 15. 4. PeriodontologyCNN in periodontics primarily is used in detection, measurement and assessment of periodontal bone loss and peri-implant bone loss 1. Neural networks including CNN are being used to minimize errors in diagnosis and provide more precise assessments 2. Along with the diagnosis of periodontitis CNN can also classify or put the diagnosis into stages of disease. A modified R-CNN model has been detecting dental implants and finding key points around them, such as marginal bone loss and peri-implantitis severity 16. Such AI systems detect periapical lesions which are also associated with periapical periodontitis 13. 5. OrthodonticsIn the field of Orthodontics, CNN works by landmark detection and cephalometric tracing, which by human force is a time taking process and might cause errors when performed manually. They are more accurate in detecting cephalometric landmarks in both 2D and 3D imaging 17. CNN can automatically identify and number teeth, along with evaluation of parameters like angulation, class and position of impacted teeth such as third molars on panoramic radiographs 3. CNN and similar AI systems can assist orthodontists in making treatment decisions such as the need for extraction or success of different treatment methods 1. The relevant orthodontic surgical cases including Temporomandibular Joint (TMJ) disorder, osteoarthritis and disc displacement can also get diagnosed. 6. ProsthodonticsCNN are used for classifying radiographs into categories such as Cavity, filling and implant. It also detects missing teeth which are relevant for prosthetic planning 14. CNN can also identify the stage of implant treatment on panoramic radiographs. It also measures peri implant bone loss on radiographs 2. A 3D-CNN has been developed to generate partial dental crowns in computer-aided design (CAD) for restorative dentistry 18. CNN are well suited for these tasks because their convolutional layers are effective at extracting such specific features from images. 7. PedodonticsPediatric dentistry has the highest proportion of AI applications in making predictable decisions, primarily for dental age estimation which is crucial for timing appropriate treatment 1. As we know it is challenging as well as crucial to detect caries early in primary teeth, which are vulnerable to rapid progression 4. Proximal caries could be difficult to diagnose in children, and this is where CNN uses panoramic radiographs of children to identify dental caries. CNN also detects anomalies such as hypodontia, premolar agenesis, detection of supernumerary teeth and detection of mesiodens on panoramic radiographs of children 14. It also automatically identifies and number teeth on radiographs when it is challenging to identify during the presence of primary and permanent teeth, tooth germs and mixed dentition. 8. Conservative and EndodonticsCNN can be used to evaluate the anatomy of root canal 2. Predicting the presence of C-shaped canals in mandibular molars and detecting separated root canal instruments on panoramic radiographs 1. These are also useful in detecting vertical root fractures. CNN may analyze gene expressions for radicular cysts and periapical granulomas 2. 9. Oral and maxillofacial surgeryCNN are used for detection of anatomical structures such as identifying impacted third molars and their proximity to inferior Dr Komal Bhosle, et al. International Journal of Dental Science and Innovative Research (IJDSIR) ©2025 IJDSIR, All Rights Reserved Page31 Page31 Page31 Page31 Page31 Page31 Page31 Page31 Page31 Page31 Page31 Page31 Page31 Page31 Page31 Page31 Page31 Page31 Page31 alveolar nerves 1. It is used for automatic detection and segmentation of mandibular canals, and differential diagnosis of odontogenic cystic lesions such as ameloblastoma and odontogenic keratocyst. They have shown increased accuracy in diagnosis of cysts and tumors of jaws 1. AI systems are also diagnosing, detecting, and predicting the prognosis of oral cancer and differentiating between benign and malignant ones 19. CNN is also used to predict postoperative results for orthognathic surgery and for segmenting dental images, including teeth, bone and pathological features which are crucial for surgical treatment plans 2. 10. Oral pathologyCNN are applied to histopathological images for rapid diagnosis of Oral Squamous Cell Carcinoma 19. They have also been applied for detection and differential diagnosis of odontogenic cystic lesions such as ameloblastoma and odontogenic keratocyst 1. Challenges and Limitations While CNN shows significant effectiveness in various applications within dentistry and oral and maxillofacial surgery, several challenges are highlighted across the field. Data-Related Challenges of how a smaller dataset can impact the model accuracy, and this limits the diversity in data 19. Gathering, processing and labelling data is a laborious and time-consuming process 5. Standardization and quality of data is required where the absence of a standard dataset which can be relied upon is a major hurdle 19. There is a need for standardization of data per algorithm. Quality of data such as image contrast or uneven exposure and blurred model performance need to be monitored 4. While CNN can match or even exceed human performance on many tasks, they may have lower accuracy than experienced clinicians in certain situations with limited data or specific conditions 20. AI systems often require advanced infrastructure, and implementing AI technology may require adjustments to existing hardware/software systems and training personnel 19. Deploying AI technology may involve additional hardware and software development costs, as well as maintenance and update costs 7. Need for external validation where models trained on specific datasets and populations from one geographic region may have limited usage on data from other geographic regions or populations 13. Ethical and legal challengesThe use of AI training data raises concerns about privacy and data Field Application Benefits Diagnosis Caries detection, radiograph interpretation High accuracy, faster and reliable diagnostics Forensics Age and gender estimation from dental X-rays Quick and accurate identification Radiology Image classification, detection, segmentation Enhanced diagnostic efficiency and precision Periodontology Bone loss assessment, disease staging Precise diagnosis, automated evaluations Orthodontics Landmark detection, tooth positioning Saves time, reduces manual errors Prosthodontics Implant stage analysis, crown design (CAD) Supports planning and restoration workflows Pedodontics Age estimation, caries and anomaly detection Early intervention and better treatment timing Endodontics Root canal anatomy, fracture and lesion detection Improved root complexity visualization Oral Surgery Surgical planning, tumor and cyst diagnosis Safer procedures, better outcome predictions Oral Pathology OSCC and cyst detection from histological images Faster, more accurate pathology interpretation Dr Komal Bhosle, et al. International Journal of Dental Science and Innovative Research (IJDSIR) ©2025 IJDSIR, All Rights Reserved Page32 Page32 Page32 Page32 Page32 Page32 Page32 Page32 Page32 Page32 Page32 Page32 Page32 Page32 Page32 Page32 Page32 Page32 Page32 security, requiring regulations and informed consent. It also raises questions regarding who is liable if AI provides incorrect diagnosis 1. There could also be a sample distribution bias in datasets, which may affect the model’s fairness and applicability 7. Absence of Evaluation Standards where lack of Evaluation standard makes it difficult to compare models effectively across studies 13. Conclusion In conclusion, CNNs have emerged as a transformative technology in dental imaging analysis, offering significant advantages in objectivity, efficiency, and accuracy across numerous diagnostic tasks in almost all dental branches. They effectively address challenges related to subjective interpretation, early disease detection, variable image quality, time constraints, human error, and complex pattern recognition 1. However, their widespread clinical integration is still hampered by critical limitations, primarily centered around the availability and quality of large, diverse annotated datasets, technical issues like robustness and optimization, and practical barriers related to cost, infrastructure, training, and ethical considerations 21. Future research needs to focus on overcoming these obstacles to fully realize the potential of CNNs in enhancing dental care. Future Perspective A crucial future direction is the creation of large and more diverse datasets 22. This includes collecting images from different populations and geographical regions to improve the generalizability of models. Conducting longitudinal studies is necessary to examine the longterm impact, effectiveness and sustainability of AI applications 20. AI has potential for further application in dental identification, forensic dentistry and treatment planning 3. AI is expected to primarily serve as a decision support mechanism for dentists, helping to alleviate workload and reduce the possibility of errors, particularly for less experienced practitioners 1. Future research aims to prioritize cross-hospital collaborations to significantly expand sample sizes and enhance predictive capabilities 23. Encouraging partnerships and collaborations across various stakeholders, including dental professionals, technology developers, organizations, and academic institutions, is considered crucial to streamline the integration of AI in dentistry 20. AI can assist clinicians but not replace them. Human critical thinking and oversight are necessary. References 1. Chen W, Dhawan M, Liu J, Ing D, Mehta K, Tran D, et al. Mapping the Use of Artificial Intelligence– Based Image Analysis for Clinical Decision‐Making in Dentistry: A Scoping Review. Clin Exp Dent Res. 2024 Dec;10(6):e70035. 2. Ossowska A, Kusiak A, Świetlik D. Artificial Intelligence in Dentistry—Narrative Review. Int J Environ Res Public Health. 2022 Mar 15;19(6):3449. 3. Rubiu G, Bologna M, Cellina M, Cè M, Sala D, Pagani R, et al. Teeth Segmentation in Panoramic Dental X-ray Using Mask Regional Convolutional Neural Network. Appl Sci. 2023 July 6;13(13):7947. 4. Yavsan ZS, Orhan H, Efe E, Yavsan E. Diagnosis of approximal caries in children with convolutional neural networks based detection algorithms on radiographs: A pilot study. Acta Odontol Scand. 2025 Jan 6;84:18–25. 5. Brahmi W, Jdey I, Drira F. Exploring the role of Convolutional Neural Networks (CNN) in dental radiography segmentation: A comprehensive Systematic Literature Review. Eng Appl Artif Intell. 2024 July;133:108510. Dr Komal Bhosle, et al. International Journal of Dental Science and Innovative Research (IJDSIR) ©2025 IJDSIR, All Rights Reserved Page33 Page33 Page33 Page33 Page33 Page33 Page33 Page33 Page33 Page33 Page33 Page33 Page33 Page33 Page33 Page33 Page33 Page33 Page33 6. Zang X, Luo C, Qiao B, Jin N, Zhao Y, Zhang H. A deep learning model using convolutional neural networks for caries detection and recognition with endoscopes. Ann Transl Med. 2022 Dec;10(24):1369–1369. 7. Abu PAR, Mao YC, Lin YJ, Chao CK, Lin YH, Wang BS, et al. Precision Medicine Assessment of the Radiographic Defect Angle of the Intrabony Defect in Periodontal Lesions by Deep Learning of Bitewing Radiographs. Bioengineering. 2025 Jan 8;12(1):43. 8. Bayraktar Y, Ayan E. Diagnosis of interproximal caries lesions with deep convolutional neural network in digital bitewing radiographs. Clin Oral Investig. 2022 Jan;26(1):623–32. 9. AL-Ghamdi ASAM, Ragab M, AlGhamdi SA, Asseri AH, Mansour RF, Koundal D. Detection of Dental Diseases through X-Ray Images Using Neural Search Architecture Network. Chaudhary G, editor. Comput Intell Neurosci. 2022 Apr 30;2022:1–7. 10. Alotaibi G, Awawdeh M, Farook FF, Aljohani M, Aldhafiri RM, Aldhoayan M. Artificial intelligence (AI) diagnostic tools: utilizing a convolutional neural network (CNN) to assess periodontal bone level radiographically—a retrospective study. BMC Oral Health. 2022 Sept 13;22(1):399. 11. Hundur Hiyari M, Pasic M, Zukic S. Application of Convolutional Neural Networks for Determining Gender and Age in Forensic Dentistry. Cureus [Internet]. 2024 Nov 5 [cited 2025 Sept 12]; Available from: https://www.cureus.com/ articles/294357-application-of-convolutional-neuralnetworks-for-determining-gender-and-age-inforensic-dentistry 12. Yavuz BS, Ekmekcioglu O, Ankarali H. Comparison of different dental age estimation methods with deep learning: Willems, Cameriere-European, London Atlas. Int J Legal Med. 2025 July;139(4):1661–72. 13. Szabó V, Orhan K, Dobó-Nagy C, Veres DS, Manulis D, Ezhov M, et al. Deep Learning-Based Periapical Lesion Detection on Panoramic Radiographs. Diagnostics. 2025 Feb 19;15(4):510. 14. Uyar T, Uyar DS. Assessment of using transfer learning with different classifiers in hypodontia diagnosis. BMC Oral Health. 2025 Jan 15;25(1):68. 15. Putra RH, Astuti ER, Nurrachman AS, Putri DK, Ghazali AB, Pradini TA, et al. Convolutional neural networks for automated tooth numbering on panoramic radiographs: A scoping review. Imaging Sci Dent. 2023;53(4):271. 16. Cha JY, Yoon HI, Yeo IS, Huh KH, Han JS. PeriImplant Bone Loss Measurement Using a RegionBased Convolutional Neural Network on Dental Periapical Radiographs. J Clin Med. 2021 Mar 2;10(5):1009. 17. Wong KF, Lam XY, Jiang Y, Yeung AWK, Lin Y. Artificial intelligence in orthodontics and orthognathic surgery: a bibliometric analysis of the 100 most-cited articles. Head Face Med. 2023 Aug 23;19(1):38. 18. Farook TH, Ahmed S, Jamayet NB, Rashid F, Barman A, Sidhu P, et al. Computer-aided design and 3-dimensional artificial/convolutional neural network for digital partial dental crown synthesis and validation. Sci Rep. 2023 Jan 28;13(1):1561. 19. Vinay V, Jodalli P, Chavan MS, Buddhikot ChaitanyaS, Luke AM, Ingafou MSH, et al. Artificial Intelligence in Oral Cancer: A Comprehensive Scoping Review of Diagnostic and Prognostic Applications. Diagnostics. 2025 Jan 24;15(3):280. 20. Jha Kukreja B, Kukreja P. Integration of Artificial Intelligence in Dentistry: A Systematic Review of Dr Komal Bhosle, et al. International Journal of Dental Science and Innovative Research (IJDSIR) ©2025 IJDSIR, All Rights Reserved Page34 Page34 Page34 Page34 Page34 Page34 Page34 Page34 Page34 Page34 Page34 Page34 Page34 Page34 Page34 Page34 Page34 Page34 Page34 Educational and Clinical Implications. Cureus [Internet]. 2025 Feb 20 [cited 2025 Sept 12]; Available from: https://www.cureus.com/ articles/ 331932-integration-of-artificial-intelligence-indentistry-a-systematic-review-of-educational-andclinical-implications 21. Musri N, Christie B, Ichwan SJA, Cahyanto A. Deep learning convolutional neural network algorithms for the early detection and diagnosis of dental caries on periapical radiographs: A systematic review. Imaging Sci Dent. 2021;51(3):237. 22. Su AY, Wu ML, Wu YH. Deep learning system for the differential diagnosis of oral mucosal lesions through clinical photographic imaging. J Dent Sci. 2025 Jan;20(1):54–60. 23. Su TY, Wu JCH, Chiu WC, Chen TJ, Lo WL, Lu HHS. Automatic classification of temporomandibular joint disorders by magnetic resonance imaging and convolutional neural networks. J Dent Sci. 2025 Jan;20(1):393–401.