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Moharrami M, Farmer J, Singhal S, Watson E, Glogauer M, Johnson AEW, Schwendicke F, Quinonez C. Detecting dental caries on oral photographs using artificial intelligence: A systematic review. Oral Dis 2024; 30:1765-1783. [PMID: 37392423 DOI: 10.1111/odi.14659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 05/19/2023] [Accepted: 06/15/2023] [Indexed: 07/03/2023]
Abstract
OBJECTIVES This systematic review aimed at evaluating the performance of artificial intelligence (AI) models in detecting dental caries on oral photographs. METHODS Methodological characteristics and performance metrics of clinical studies reporting on deep learning and other machine learning algorithms were assessed. The risk of bias was evaluated using the quality assessment of diagnostic accuracy studies 2 (QUADAS-2) tool. A systematic search was conducted in EMBASE, Medline, and Scopus. RESULTS Out of 3410 identified records, 19 studies were included with six and seven studies having low risk of biases and applicability concerns for all the domains, respectively. Metrics varied widely and were assessed on multiple levels. F1-scores for classification and detection tasks were 68.3%-94.3% and 42.8%-95.4%, respectively. Irrespective of the task, F1-scores were 68.3%-95.4% for professional cameras, 78.8%-87.6%, for intraoral cameras, and 42.8%-80% for smartphone cameras. Limited studies allowed assessing AI performance for lesions of different severity. CONCLUSION Automatic detection of dental caries using AI may provide objective verification of clinicians' diagnoses and facilitate patient-clinician communication and teledentistry. Future studies should consider more robust study designs, employ comparable and standardized metrics, and focus on the severity of caries lesions.
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Affiliation(s)
- Mohammad Moharrami
- Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Geneva, Switzerland
| | - Julie Farmer
- Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada
| | - Sonica Singhal
- Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada
- Health Promotion, Chronic Disease and Injury Prevention Department, Public Health Ontario, Toronto, Canada
| | - Erin Watson
- Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada
- Department of Dental Oncology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Michael Glogauer
- Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada
- Department of Dental Oncology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Department of Dentistry, Centre for Advanced Dental Research and Care, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Alistair E W Johnson
- Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Falk Schwendicke
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Geneva, Switzerland
- Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Carlos Quinonez
- Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada
- Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
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2
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Wang Z, Song J, Lin K, Hong W, Mao S, Wu X, Zhang J. Automated detection of otosclerosis with interpretable deep learning using temporal bone computed tomography images. Heliyon 2024; 10:e29670. [PMID: 38655358 PMCID: PMC11036044 DOI: 10.1016/j.heliyon.2024.e29670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 04/10/2024] [Accepted: 04/12/2024] [Indexed: 04/26/2024] Open
Abstract
Objective This study aimed to develop an automated detection schema for otosclerosis with interpretable deep learning using temporal bone computed tomography images. Methods With approval from the institutional review board, we retrospectively analyzed high-resolution computed tomography scans of the temporal bone of 182 participants with otosclerosis (67 male subjects and 115 female subjects; average age, 36.42 years) and 157 participants without otosclerosis (52 male subjects and 102 female subjects; average age, 30.61 years) using deep learning. Transfer learning with the pretrained VGG19, Mask RCNN, and EfficientNet models was used. In addition, 3 clinical experts compared the system's performance by reading the same computed tomography images for a subset of 35 unseen subjects. An area under the receiver operating characteristic curve and a saliency map were used to further evaluate the diagnostic performance. Results In prospective unseen test data, the diagnostic performance of the automatically interpretable otosclerosis detection system at the optimal threshold was 0.97 and 0.98 for sensitivity and specificity, respectively. In comparison with the clinical acumen of otolaryngologists at P < 0.05, the proposed system was not significantly different. Moreover, the area under the receiver operating characteristic curve for the proposed system was 0.99, indicating satisfactory diagnostic accuracy. Conclusion Our research develops and evaluates a deep learning system that detects otosclerosis at a level comparable with clinical otolaryngologists. Our system is an effective schema for the differential diagnosis of otosclerosis in computed tomography examinations.
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Affiliation(s)
- Zheng Wang
- School of Computer Science, Hunan First Normal University, Changsha, 410205, China
- Key Laboratory of Informalization Technology for Basic Education in Hunan Province, Changsha, 410205, China
| | - Jian Song
- Department of Otorhinolaryngology, Xiangya Hospital Central South University, Changsha, Hunan, China
- Province Key Laboratory of Otolaryngology Critical Diseases, Changsha, Hunan, China
| | - Kaibin Lin
- School of Computer Science, Hunan First Normal University, Changsha, 410205, China
- Key Laboratory of Informalization Technology for Basic Education in Hunan Province, Changsha, 410205, China
| | - Wei Hong
- School of Computer Science, Hunan First Normal University, Changsha, 410205, China
- Key Laboratory of Informalization Technology for Basic Education in Hunan Province, Changsha, 410205, China
| | - Shuang Mao
- Department of Otorhinolaryngology, Xiangya Hospital Central South University, Changsha, Hunan, China
- Province Key Laboratory of Otolaryngology Critical Diseases, Changsha, Hunan, China
| | - Xuewen Wu
- Department of Otorhinolaryngology, Xiangya Hospital Central South University, Changsha, Hunan, China
- Province Key Laboratory of Otolaryngology Critical Diseases, Changsha, Hunan, China
| | - Jianglin Zhang
- Department of Dermatology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University. The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China
- Candidate Branch of National Clinical Research Center for Skin Diseases, Shenzhen, 518020, Guangdong, China
- Department of Geriatrics, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University. The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China
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3
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Portella PD, de Oliveira LF, Ferreira MFDC, Dias BC, de Souza JF, Assunção LRDS. Improving accuracy of early dental carious lesions detection using deep learning-based automated method. Clin Oral Investig 2023; 27:7663-7670. [PMID: 37906303 DOI: 10.1007/s00784-023-05355-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 10/18/2023] [Indexed: 11/02/2023]
Abstract
OBJECTIVE To investigate the effectiveness of a convolutional neural network (CNN) in detecting healthy teeth and early carious lesions on occlusal surfaces and to assess the applicability of this deep learning algorithm as an auxiliary aid. MATERIALS AND METHODS A total of 2,481 posterior teeth (2,459 permanent and 22 deciduous teeth) with varying stages of carious lesions were classified according to the International Caries Detection and Assessment System (ICDAS). After clinical evaluation, ICDAS 0 and 2 occlusal surfaces were photographed with a professional digital camera. VGG-19 was chosen as the CNN and the findings were compared with those of a reference examiner to evaluate its detection efficiency. To verify the effectiveness of the CNN as an auxiliary detection aid, three examiners (an undergraduate student (US), a newly graduated dental surgeon (ND), and a specialist in pediatric dentistry (SP) assessed the acquired images (Phase I). In Phase II, the examiners reassessed the same images using the CNN-generated algorithms. RESULTS The training dataset consisted of 8,749 images, whereas the test dataset included 140 images. VGG-19 achieved an accuracy of 0.879, positive agreement of 0.827, precision of 0.949, negative agreement 0.800, and an F1-score of 0.887. In Phase I, the accuracy rates for examiners US, ND, and SP were 0.543, 0.771, and 0.807, respectively. In Phase II, the accuracy rates improved to 0.679, 0.886, and 0.857 for the respective examiners. The number of correct answers was significantly higher in Phase II than in Phase I for all examiners (McNemar test;P<0.05). CONCLUSIONS VGG-19 demonstrated satisfactory performance in the detection of early carious lesions, as well as an auxiliary detection aid. CLINICAL RELEVANCE Automated detection using deep learning algorithms is an important aid in detecting early caries lesions and improves the accuracy of the disease detection, enabling quicker and more reliable clinical decision-making.
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Affiliation(s)
- Paula Dresch Portella
- Stomatology Department, Universidade Federal do Paraná, Avenida Prefeito Lothário Meissner, 632, Jardim Botânico, Curitiba, PR, 80210-170, Brazil.
| | | | | | - Bruna Cristine Dias
- Stomatology Department, Universidade Federal do Paraná, Avenida Prefeito Lothário Meissner, 632, Jardim Botânico, Curitiba, PR, 80210-170, Brazil
| | - Juliana Feltrin de Souza
- Stomatology Department, Universidade Federal do Paraná, Avenida Prefeito Lothário Meissner, 632, Jardim Botânico, Curitiba, PR, 80210-170, Brazil
| | - Luciana Reichert da Silva Assunção
- Stomatology Department, Universidade Federal do Paraná, Avenida Prefeito Lothário Meissner, 632, Jardim Botânico, Curitiba, PR, 80210-170, Brazil
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Radha RC, Raghavendra BS, Subhash BV, Rajan J, Narasimhadhan AV. Machine learning techniques for periodontitis and dental caries detection: A narrative review. Int J Med Inform 2023; 178:105170. [PMID: 37595373 DOI: 10.1016/j.ijmedinf.2023.105170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 07/07/2023] [Accepted: 07/31/2023] [Indexed: 08/20/2023]
Abstract
OBJECTIVES In recent years, periodontitis, and dental caries have become common in humans and need to be diagnosed in the early stage to prevent severe complications and tooth loss. These dental issues are diagnosed by visual inspection, measuring pocket probing depth, and radiographs findings from experienced dentists. Though a glut of machine learning (ML) algorithms has been proposed for the automated detection of periodontitis, and dental caries, determining which ML techniques are suitable for clinical practice remains under debate. This review aims to identify the research challenges by analyzing the limitations of current methods and how to address these to obtain robust systems suitable for clinical use or point-of-care testing. METHODS An extensive search of the literature published from 2015 to 2022 written in English, related to the subject of study was sought by searching the electronic databases: PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and ScienceDirect. RESULTS The initial electronic search yielded 1743 titles, and 55 studies were eventually included based on the selection criteria adopted in this review. Studies selected were on ML applications for the automatic detection of periodontitis and dental caries and related dental issues: Apical lessons, Periodontal bone loss, and Vertical root fracture. CONCLUSION While most of the ML-based studies use radiograph images for the detection of periodontitis and dental caries, few pieces of the literature revealed that good diagnostic accuracy could be achieved by training the ML model even with mobile photos representing the images of dental issues. Nowadays smartphones are used in every sector for different applications. Training the ML model with as many images of dental issues captured by the smartphone can achieve good accuracy, reduce the cost of clinical diagnosis, and provide user interaction.
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Affiliation(s)
- R C Radha
- Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, India.
| | - B S Raghavendra
- Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, India
| | - B V Subhash
- Department of Oral Medicine and Radiology, DAPM R V Dental College, Bengaluru, India
| | - Jeny Rajan
- Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India
| | - A V Narasimhadhan
- Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, India
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Fatima A, Shafi I, Afzal H, Mahmood K, Díez IDLT, Lipari V, Ballester JB, Ashraf I. Deep Learning-Based Multiclass Instance Segmentation for Dental Lesion Detection. Healthcare (Basel) 2023; 11:healthcare11030347. [PMID: 36766922 PMCID: PMC9914729 DOI: 10.3390/healthcare11030347] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 01/16/2023] [Accepted: 01/19/2023] [Indexed: 01/27/2023] Open
Abstract
Automated dental imaging interpretation is one of the most prolific areas of research using artificial intelligence. X-ray imaging systems have enabled dental clinicians to identify dental diseases. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, as well as machine and deep learning models for dental disease diagnoses using X-ray imagery. In this regard, a lightweight Mask-RCNN model is proposed for periapical disease detection. The proposed model is constructed in two parts: a lightweight modified MobileNet-v2 backbone and region-based network (RPN) are proposed for periapical disease localization on a small dataset. To measure the effectiveness of the proposed model, the lightweight Mask-RCNN is evaluated on a custom annotated dataset comprising images of five different types of periapical lesions. The results reveal that the model can detect and localize periapical lesions with an overall accuracy of 94%, a mean average precision of 85%, and a mean insection over a union of 71.0%. The proposed model improves the detection, classification, and localization accuracy significantly using a smaller number of images compared to existing methods and outperforms state-of-the-art approaches.
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Affiliation(s)
- Anum Fatima
- National Centre for Robotics, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Imran Shafi
- College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Hammad Afzal
- Military College of Signals (MCS), National University of Sciences and Technology (NUST), Rawalpindi 44000, Pakistan
| | - Khawar Mahmood
- Military College of Signals (MCS), National University of Sciences and Technology (NUST), Rawalpindi 44000, Pakistan
| | - Isabel de la Torre Díez
- Department of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
- Correspondence: (I.d.l.T.D.); (I.A.)
| | - Vivian Lipari
- Research Group on Foods, Nutritional Biochemistry and Health, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- Fundación Universitaria Internacional de Colombia Bogotá, Bogotá 11001, Colombia
| | - Julien Brito Ballester
- Research Group on Foods, Nutritional Biochemistry and Health, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Department of Project Management, Universidad Internacional Iberoamericana Arecibo, Arecibo, PR 00613, USA
- Project Management, Universidade Internacional do Cuanza, Cuito EN250, Bié, Angola
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
- Correspondence: (I.d.l.T.D.); (I.A.)
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