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Kurt A, Günaçar DN, Şılbır FY, Yeşil Z, Bayrakdar İŞ, Çelik Ö, Bilgir E, Orhan K. Evaluation of tooth development stages with deep learning-based artificial intelligence algorithm. BMC Oral Health 2024; 24:1034. [PMID: 39227802 PMCID: PMC11370008 DOI: 10.1186/s12903-024-04786-6] [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: 05/17/2024] [Accepted: 08/20/2024] [Indexed: 09/05/2024] Open
Abstract
BACKGROUND This study aims to evaluate the performance of a deep learning system for the evaluation of tooth development stages on images obtained from panoramic radiographs from child patients. METHODS The study collected a total of 1500 images obtained from panoramic radiographs from child patients between the ages of 5 and 14 years. YOLOv5, a convolutional neural network (CNN)-based object detection model, was used to automatically detect the calcification states of teeth. Images obtained from panoramic radiographs from child patients were trained and tested in the YOLOv5 algorithm. True-positive (TP), false-positive (FP), and false-negative (FN) ratios were calculated. A confusion matrix was used to evaluate the performance of the model. RESULTS Among the 146 test group images with 1022 labels, there were 828 TPs, 308 FPs, and 1 FN. The sensitivity, precision, and F1-score values of the detection model of the tooth stage development model were 0.99, 0.72, and 0.84, respectively. CONCLUSIONS In conclusion, utilizing a deep learning-based approach for the detection of dental development on pediatric panoramic radiographs may facilitate a precise evaluation of the chronological correlation between tooth development stages and age. This can help clinicians make treatment decisions and aid dentists in finding more accurate treatment options.
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Affiliation(s)
- Ayça Kurt
- Faculty of Dentistry, Department of Pediatric Dentistry, Recep Tayyip Erdogan University, Rize, Turkey.
| | - Dilara Nil Günaçar
- Faculty of Dentistry, Department of Oral and Dentomaxillofacial Radiology, Recep Tayyip Erdogan University, Rize, Turkey
| | - Fatma Yanık Şılbır
- Faculty of Dentistry, Department of Pediatric Dentistry, Recep Tayyip Erdogan University, Rize, Turkey
| | - Zeynep Yeşil
- Faculty of Dentistry, Department of Prosthetic Dentistry, Recep Tayyip Erdogan University, Rize, Turkey
- Faculty of Dentistry, Prosthetic Dentistry, Ataturk University, Erzurum, Türkiye
| | - İbrahim Şevki Bayrakdar
- Faculty of Dentistry, Department of Oral and Dentomaxillofacial Radiology, Eskişehir Osmangazi University, Eskişehir, Turkey
| | - Özer Çelik
- Department of Mathematics and Computer Science, Eskişehir Osmangazi University, Eskişehir, Turkey
| | - Elif Bilgir
- Faculty of Dentistry, Department of Oral and Dentomaxillofacial Radiology, Eskişehir Osmangazi University, Eskişehir, Turkey
| | - Kaan Orhan
- Faculty of Dentistry, Department of Oral and Dentomaxillofacial Radiology, Ankara University, Ankara, Turkey
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Balasamy S, Sundramoorthy AK. Comment on "Utilizing deep learning for automated detection of oral lesions: A multicenter study". Oral Oncol 2024; 156:106932. [PMID: 38968726 DOI: 10.1016/j.oraloncology.2024.106932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Accepted: 06/29/2024] [Indexed: 07/07/2024]
Affiliation(s)
- Sesuraj Balasamy
- Department of Prosthodontics and Materials Science, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Chennai, 600077, Tamil Nadu, India
| | - Ashok K Sundramoorthy
- Department of Prosthodontics and Materials Science, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Chennai, 600077, Tamil Nadu, India.
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Li J, Kot WY, McGrath CP, Chan BWA, Ho JWK, Zheng LW. Diagnostic accuracy of artificial intelligence assisted clinical imaging in the detection of oral potentially malignant disorders and oral cancer: a systematic review and meta-analysis. Int J Surg 2024; 110:5034-5046. [PMID: 38652301 PMCID: PMC11325952 DOI: 10.1097/js9.0000000000001469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 03/30/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND The objective of this study is to examine the application of artificial intelligence (AI) algorithms in detecting oral potentially malignant disorders (OPMD) and oral cancerous lesions, and to evaluate the accuracy variations among different imaging tools employed in these diagnostic processes. MATERIALS AND METHODS A systematic search was conducted in four databases: Embase, Web of Science, PubMed, and Scopus. The inclusion criteria included studies using machine learning algorithms to provide diagnostic information on specific oral lesions, prospective or retrospective design, and inclusion of OPMD. Sensitivity and specificity analyses were also required. Forest plots were generated to display overall diagnostic odds ratio (DOR), sensitivity, specificity, negative predictive values, and summary receiver operating characteristic (SROC) curves. Meta-regression analysis was conducted to examine potential differences among different imaging tools. RESULTS The overall DOR for AI-based screening of OPMD and oral mucosal cancerous lesions from normal mucosa was 68.438 (95% CI= [39.484-118.623], I2 =86%). The area under the SROC curve was 0.938, indicating excellent diagnostic performance. AI-assisted screening showed a sensitivity of 89.9% (95% CI= [0.866-0.925]; I2 =81%), specificity of 89.2% (95% CI= [0.851-0.922], I2 =79%), and a high negative predictive value of 89.5% (95% CI= [0.851-0.927], I2 =96%). Meta-regression analysis revealed no significant difference among the three image tools. After generating a GOSH plot, the DOR was calculated to be 49.30, and the area under the SROC curve was 0.877. Additionally, sensitivity, specificity, and negative predictive value were 90.5% (95% CI [0.873-0.929], I2 =4%), 87.0% (95% CI [0.813-0.912], I2 =49%) and 90.1% (95% CI [0.860-0.931], I2 =57%), respectively. Subgroup analysis showed that clinical photography had the highest diagnostic accuracy. CONCLUSIONS AI-based detection using clinical photography shows a high DOR and is easily accessible in the current era with billions of phone subscribers globally. This indicates that there is significant potential for AI to enhance the diagnostic capabilities of general practitioners to the level of specialists by utilizing clinical photographs, without the need for expensive specialized imaging equipment.
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Affiliation(s)
- JingWen Li
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong
| | - Wai Ying Kot
- Faculty of Dentistry, The University of Hong Kong
| | - Colman Patrick McGrath
- Division of Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong
| | - Bik Wan Amy Chan
- Department of Anatomical and Cellular Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong
| | - Joshua Wing Kei Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, People's Republic of China
| | - Li Wu Zheng
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong
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Kim P, Seo B, De Silva H. Concordance of clinician, Chat-GPT4, and ORAD diagnoses against histopathology in Odontogenic Keratocysts and tumours: a 15-Year New Zealand retrospective study. Oral Maxillofac Surg 2024:10.1007/s10006-024-01284-5. [PMID: 39060850 DOI: 10.1007/s10006-024-01284-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Accepted: 07/19/2024] [Indexed: 07/28/2024]
Abstract
BACKGROUND This research aimed to investigate the concordance between clinical impressions and histopathologic diagnoses made by clinicians and artificial intelligence tools for odontogenic keratocyst (OKC) and Odontogenic tumours (OT) in a New Zealand population from 2008 to 2023. METHODS Histopathological records from the Oral Pathology Centre, University of Otago (2008-2023) were examined to identify OKCs and OT. Specimen referral details, histopathologic reports, and clinician differential diagnoses, as well as those provided by ORAD and Chat-GPT4, were documented. Data were analyzed using SPSS, and concordance between provisional and histopathologic diagnoses was ascertained. RESULTS Of the 34,225 biopsies, 302 and 321 samples were identified as OTs and OKCs. Concordance rates were 43.2% for clinicians, 45.6% for ORAD, and 41.4% for Chat-GPT4. Corresponding Kappa value against histological diagnosis were 0.23, 0.13 and 0.14. Surgeons achieved a higher concordance rate (47.7%) compared to non-surgeons (29.82%). Odds ratio of having concordant diagnosis using Chat-GPT4 and ORAD were between 1.4 and 2.8 (p < 0.05). ROC-AUC and PR-AUC were similar between the groups (Clinician 0.62/0.42, ORAD 0.58/0.28, Char-GPT4 0.63/0.37) for ameloblastoma and for OKC (Clinician 0.64/0.78, ORAD 0.66/0.77, Char-GPT4 0.60/0.71). CONCLUSION Clinicians with surgical training achieved higher concordance rate when it comes to OT and OKC. Chat-GPT4 and Bayesian approach (ORAD) have shown potential in enhancing diagnostic capabilities.
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Affiliation(s)
- Paul Kim
- Oral and Maxillofacial Surgery Registrar, Dunedin Hospital, Dunedin, New Zealand.
| | - Benedict Seo
- Department of Oral Diagnostic and Surgical Sciences, University of Otago, Dunedin, New Zealand
| | - Harsha De Silva
- Department of Oral Diagnostic and Surgical Sciences, University of Otago, Dunedin, New Zealand
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Vinayahalingam S, van Nistelrooij N, Rothweiler R, Tel A, Verhoeven T, Tröltzsch D, Kesting M, Bergé S, Xi T, Heiland M, Flügge T. Advancements in diagnosing oral potentially malignant disorders: leveraging Vision transformers for multi-class detection. Clin Oral Investig 2024; 28:364. [PMID: 38849649 PMCID: PMC11161543 DOI: 10.1007/s00784-024-05762-8] [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: 04/13/2024] [Accepted: 06/01/2024] [Indexed: 06/09/2024]
Abstract
OBJECTIVES Diagnosing oral potentially malignant disorders (OPMD) is critical to prevent oral cancer. This study aims to automatically detect and classify the most common pre-malignant oral lesions, such as leukoplakia and oral lichen planus (OLP), and distinguish them from oral squamous cell carcinomas (OSCC) and healthy oral mucosa on clinical photographs using vision transformers. METHODS 4,161 photographs of healthy mucosa, leukoplakia, OLP, and OSCC were included. Findings were annotated pixel-wise and reviewed by three clinicians. The photographs were divided into 3,337 for training and validation and 824 for testing. The training and validation images were further divided into five folds with stratification. A Mask R-CNN with a Swin Transformer was trained five times with cross-validation, and the held-out test split was used to evaluate the model performance. The precision, F1-score, sensitivity, specificity, and accuracy were calculated. The area under the receiver operating characteristics curve (AUC) and the confusion matrix of the most effective model were presented. RESULTS The detection of OSCC with the employed model yielded an F1 of 0.852 and AUC of 0.974. The detection of OLP had an F1 of 0.825 and AUC of 0.948. For leukoplakia the F1 was 0.796 and the AUC was 0.938. CONCLUSIONS OSCC were effectively detected with the employed model, whereas the detection of OLP and leukoplakia was moderately effective. CLINICAL RELEVANCE Oral cancer is often detected in advanced stages. The demonstrated technology may support the detection and observation of OPMD to lower the disease burden and identify malignant oral cavity lesions earlier.
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Affiliation(s)
- Shankeeth Vinayahalingam
- Department of Oral and Maxillofacial Surgery, Radboud University Medical Centre, Nijmegen, the Netherlands
- Department of Artificial Intelligence, Radboud University, Nijmegen, the Netherlands
- Department of Oral and Maxillofacial Surgery, Universitätsklinikum Münster, Münster, Germany
| | - Niels van Nistelrooij
- Department of Oral and Maxillofacial Surgery, Radboud University Medical Centre, Nijmegen, the Netherlands
- Department of Oral and Maxillofacial Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - René Rothweiler
- Department of Oral and Maxillofacial Surgery, Translational Implantology, Medical Center, Faculty of Medicine, University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Alessandro Tel
- Clinic of Maxillofacial Surgery, Head&Neck and Neuroscience Department, University Hospital of Udine, Udine, Italy
| | - Tim Verhoeven
- Department of Oral and Maxillofacial Surgery, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Daniel Tröltzsch
- Department of Oral and Maxillofacial Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Marco Kesting
- Department of Oral and Cranio-Maxillofacial Surgery, Friedrich-Alexander-University Erlangen- Nuremberg (FAU), Erlangen, Germany
| | - Stefaan Bergé
- Department of Oral and Maxillofacial Surgery, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Tong Xi
- Department of Oral and Maxillofacial Surgery, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Max Heiland
- Department of Oral and Maxillofacial Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Tabea Flügge
- Department of Oral and Maxillofacial Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany.
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Somyanonthanakul R, Warin K, Chaowchuen S, Jinaporntham S, Panichkitkosolkul W, Suebnukarn S. Survival estimation of oral cancer using fuzzy deep learning. BMC Oral Health 2024; 24:519. [PMID: 38698358 PMCID: PMC11067185 DOI: 10.1186/s12903-024-04279-6] [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: 09/22/2023] [Accepted: 04/19/2024] [Indexed: 05/05/2024] Open
Abstract
BACKGROUND Oral cancer is a deadly disease and a major cause of morbidity and mortality worldwide. The purpose of this study was to develop a fuzzy deep learning (FDL)-based model to estimate the survival time based on clinicopathologic data of oral cancer. METHODS Electronic medical records of 581 oral squamous cell carcinoma (OSCC) patients, treated with surgery with or without radiochemotherapy, were collected retrospectively from the Oral and Maxillofacial Surgery Clinic and the Regional Cancer Center from 2011 to 2019. The deep learning (DL) model was trained to classify survival time classes based on clinicopathologic data. Fuzzy logic was integrated into the DL model and trained to create FDL-based models to estimate the survival time classes. RESULTS The performance of the models was evaluated on a test dataset. The performance of the DL and FDL models for estimation of survival time achieved an accuracy of 0.74 and 0.97 and an area under the receiver operating characteristic (AUC) curve of 0.84 to 1.00 and 1.00, respectively. CONCLUSIONS The integration of fuzzy logic into DL models could improve the accuracy to estimate survival time based on clinicopathologic data of oral cancer.
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Affiliation(s)
| | - Kritsasith Warin
- Faculty of Dentistry, Thammasat University, Pathum Thani, Thailand.
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Ozsunkar PS, Özen DÇ, Abdelkarim AZ, Duman S, Uğurlu M, Demİr MR, Kuleli B, Çelİk Ö, Imamoglu BS, Bayrakdar IS, Duman SB. Detecting white spot lesions on post-orthodontic oral photographs using deep learning based on the YOLOv5x algorithm: a pilot study. BMC Oral Health 2024; 24:490. [PMID: 38658959 PMCID: PMC11044306 DOI: 10.1186/s12903-024-04262-1] [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: 10/10/2023] [Accepted: 04/15/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND Deep learning model trained on a large image dataset, can be used to detect and discriminate targets with similar but not identical appearances. The aim of this study is to evaluate the post-training performance of the CNN-based YOLOv5x algorithm in the detection of white spot lesions in post-orthodontic oral photographs using the limited data available and to make a preliminary study for fully automated models that can be clinically integrated in the future. METHODS A total of 435 images in JPG format were uploaded into the CranioCatch labeling software and labeled white spot lesions. The labeled images were resized to 640 × 320 while maintaining their aspect ratio before model training. The labeled images were randomly divided into three groups (Training:349 images (1589 labels), Validation:43 images (181 labels), Test:43 images (215 labels)). YOLOv5x algorithm was used to perform deep learning. The segmentation performance of the tested model was visualized and analyzed using ROC analysis and a confusion matrix. True Positive (TP), False Positive (FP), and False Negative (FN) values were determined. RESULTS Among the test group images, there were 133 TPs, 36 FPs, and 82 FNs. The model's performance metrics include precision, recall, and F1 score values of detecting white spot lesions were 0.786, 0.618, and 0.692. The AUC value obtained from the ROC analysis was 0.712. The mAP value obtained from the Precision-Recall curve graph was 0.425. CONCLUSIONS The model's accuracy and sensitivity in detecting white spot lesions remained lower than expected for practical application, but is a promising and acceptable detection rate compared to previous study. The current study provides a preliminary insight to further improved by increasing the dataset for training, and applying modifications to the deep learning algorithm. CLINICAL REVELANCE Deep learning systems can help clinicians to distinguish white spot lesions that may be missed during visual inspection.
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Affiliation(s)
- Pelin Senem Ozsunkar
- Department of Paediatric Dentistry, Faculty of Dentistry, Inonu University, Malatya, Turkey
| | - Duygu Çelİk Özen
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Inonu University, Malatya, Turkey
| | - Ahmed Z Abdelkarim
- Division of Oral & Maxillofacial Radiology, College of Dentistry, The Ohio State Universiy, Columbus, OH, USA
| | - Sacide Duman
- Department of Paediatric Dentistry, Faculty of Dentistry, Inonu University, Malatya, Turkey
| | - Mehmet Uğurlu
- Department of Orthodontics, Faculty of Dentistry, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Mehmet Rıdvan Demİr
- Department of Orthodontics, Faculty of Dentistry, Ataturk University, Erzurum, Turkey
| | - Batuhan Kuleli
- Department of Orthodontics, Faculty of Dentistry, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Özer Çelİk
- Department of Mathematics-Computer, Eskişehir Osmangazi University Faculty of Science, Eskişehir, Turkey
| | - Busra Seda Imamoglu
- Department of Orthodontics, Hamidiye Faculty of Dentistry, University of Health Sciences, Istanbul, Turkey
| | - Ibrahim Sevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Suayip Burak Duman
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Inonu University, Malatya, 44280, Turkey.
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Rabinovici-Cohen S, Fridman N, Weinbaum M, Melul E, Hexter E, Rosen-Zvi M, Aizenberg Y, Porat Ben Amy D. From Pixels to Diagnosis: Algorithmic Analysis of Clinical Oral Photos for Early Detection of Oral Squamous Cell Carcinoma. Cancers (Basel) 2024; 16:1019. [PMID: 38473377 DOI: 10.3390/cancers16051019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/22/2024] [Accepted: 02/26/2024] [Indexed: 03/14/2024] Open
Abstract
Oral squamous cell carcinoma (OSCC) accounts for more than 90% of oral malignancies. Despite numerous advancements in understanding its biology, the mean five-year survival rate of OSCC is still very poor at about 50%, with even lower rates when the disease is detected at later stages. We investigate the use of clinical photographic images taken by common smartphones for the automated detection of OSCC cases and for the identification of suspicious cases mimicking cancer that require an urgent biopsy. We perform a retrospective study on a cohort of 1470 patients drawn from both hospital records and online academic sources. We examine various deep learning methods for the early detection of OSCC cases as well as for the detection of suspicious cases. Our results demonstrate the efficacy of these methods in both tasks, providing a comprehensive understanding of the patient's condition. When evaluated on holdout data, the model to predict OSCC achieved an AUC of 0.96 (CI: 0.91, 0.98), with a sensitivity of 0.91 and specificity of 0.81. When the data are stratified based on lesion location, we find that our models can provide enhanced accuracy (AUC 1.00) in differentiating specific groups of patients that have lesions in the lingual mucosa, floor of mouth, or posterior tongue. These results underscore the potential of leveraging clinical photos for the timely and accurate identification of OSCC.
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Affiliation(s)
| | - Naomi Fridman
- TIMNA-Big Data Research Platform Unit, Ministry of Health, Jerusalem 9446724, Israel
- The Department of Industrial Engineering & Management, Ariel University, Ariel 40700, Israel
| | - Michal Weinbaum
- TIMNA-Big Data Research Platform Unit, Ministry of Health, Jerusalem 9446724, Israel
| | - Eli Melul
- TIMNA-Big Data Research Platform Unit, Ministry of Health, Jerusalem 9446724, Israel
| | - Efrat Hexter
- IBM Research-Israel, Mount Carmel, Haifa 3498825, Israel
| | - Michal Rosen-Zvi
- IBM Research-Israel, Mount Carmel, Haifa 3498825, Israel
- Faculty of Medicine, The Hebrew University, Jerusalem 91120, Israel
| | - Yelena Aizenberg
- Oral Medicine Unit, Department of Oral and Maxillofacial Surgery, Tzafon Medical Center, Poriya 15208, Israel
| | - Dalit Porat Ben Amy
- Oral Medicine Unit, Department of Oral and Maxillofacial Surgery, Tzafon Medical Center, Poriya 15208, Israel
- The Azrieli Faculty of Medicine, Bar-Ilan University, Ramat Gan 5290002, Israel
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Warin K, Suebnukarn S. Deep learning in oral cancer- a systematic review. BMC Oral Health 2024; 24:212. [PMID: 38341571 PMCID: PMC10859022 DOI: 10.1186/s12903-024-03993-5] [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: 10/27/2023] [Accepted: 02/06/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Oral cancer is a life-threatening malignancy, which affects the survival rate and quality of life of patients. The aim of this systematic review was to review deep learning (DL) studies in the diagnosis and prognostic prediction of oral cancer. METHODS This systematic review was conducted following the PRISMA guidelines. Databases (Medline via PubMed, Google Scholar, Scopus) were searched for relevant studies, from January 2000 to June 2023. RESULTS Fifty-four qualified for inclusion, including diagnostic (n = 51), and prognostic prediction (n = 3). Thirteen studies showed a low risk of biases in all domains, and 40 studies low risk for concerns regarding applicability. The performance of DL models was reported of the accuracy of 85.0-100%, F1-score of 79.31 - 89.0%, Dice coefficient index of 76.0 - 96.3% and Concordance index of 0.78-0.95 for classification, object detection, segmentation, and prognostic prediction, respectively. The pooled diagnostic odds ratios were 2549.08 (95% CI 410.77-4687.39) for classification studies. CONCLUSIONS The number of DL studies in oral cancer is increasing, with a diverse type of architectures. The reported accuracy showed promising DL performance in studies of oral cancer and appeared to have potential utility in improving informed clinical decision-making of oral cancer.
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Affiliation(s)
- Kritsasith Warin
- Faculty of Dentistry, Thammasat University, Pathum Thani, Thailand.
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10
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Hsieh ST, Cheng YA. Multimodal feature fusion in deep learning for comprehensive dental condition classification. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:303-321. [PMID: 38217632 DOI: 10.3233/xst-230271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2024]
Abstract
BACKGROUND Dental health issues are on the rise, necessitating prompt and precise diagnosis. Automated dental condition classification can support this need. OBJECTIVE The study aims to evaluate the effectiveness of deep learning methods and multimodal feature fusion techniques in advancing the field of automated dental condition classification. METHODS AND MATERIALS A dataset of 11,653 clinically sourced images representing six prevalent dental conditions-caries, calculus, gingivitis, tooth discoloration, ulcers, and hypodontia-was utilized. Features were extracted using five Convolutional Neural Network (CNN) models, then fused into a matrix. Classification models were constructed using Support Vector Machines (SVM) and Naive Bayes classifiers. Evaluation metrics included accuracy, recall rate, precision, and Kappa index. RESULTS The SVM classifier integrated with feature fusion demonstrated superior performance with a Kappa index of 0.909 and accuracy of 0.925. This significantly surpassed individual CNN models such as EfficientNetB0, which achieved a Kappa of 0.814 and accuracy of 0.847. CONCLUSIONS The amalgamation of feature fusion with advanced machine learning algorithms can significantly bolster the precision and robustness of dental condition classification systems. Such a method presents a valuable tool for dental professionals, facilitating enhanced diagnostic accuracy and subsequently improved patient outcomes.
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Affiliation(s)
- Shang-Ting Hsieh
- Department of Health Beauty, Fooyin University, Kaohsiung City, Taiwan
| | - Ya-Ai Cheng
- Department of Healthcare Administration, I-Shou University, Kaohsiung City, Taiwan
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Araújo ALD, de Souza ESC, Faustino ISP, Saldivia-Siracusa C, Brito-Sarracino T, Lopes MA, Vargas PA, Pearson AT, Kowalski LP, de Carvalho ACPDLF, Santos-Silva AR. Clinicians' perception of oral potentially malignant disorders: a pitfall for image annotation in supervised learning. Oral Surg Oral Med Oral Pathol Oral Radiol 2023; 136:315-321. [PMID: 37037738 DOI: 10.1016/j.oooo.2023.02.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 02/07/2023] [Accepted: 02/22/2023] [Indexed: 03/09/2023]
Abstract
OBJECTIVE The present study aims to quantify clinicians' perceptions of oral potentially malignant disorders (OPMDs) when evaluating, classifying, and manually annotating clinical images, as well as to understand the source of inter-observer variability when assessing these lesions. The hypothesis was that different interpretations could affect the quality of the annotations used to train a Supervised Learning model. STUDY DESIGN Forty-six clinical images from 37 patients were reviewed, classified, and manually annotated at the pixel level by 3 labelers. We compared the inter-examiner assessment based on clinical criteria through the κ statistics (Fleiss's kappa). The segmentations were also compared using the mean pixel-wise intersection over union (IoU). RESULTS The inter-observer agreement for homogeneous/non-homogeneous criteria was substantial (κ = 63, 95% CI: 0.47-0.80). For the subclassification of non-homogeneous lesions, the inter-observer agreement was moderate (κ = 43, 95% CI: 0.34-0.53) (P < .001). The mean IoU of 0.53 (±0.22) was considered low. CONCLUSION The subjective clinical assessment (based on human visual observation, variable criteria that have suffered adjustments over the years, different educational backgrounds, and personal experience) may explain the source of inter-observer discordance for the classification and annotation of OPMD. Therefore, there is a strong probability of transferring the subjectivity of human analysis to artificial intelligence models. The use of large data sets and segmentation based on the union of all labelers' annotations holds the potential to overcome this limitation.
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Affiliation(s)
- Anna Luíza Damaceno Araújo
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil; Head and Neck Surgery Department, University of São Paulo Medical School (UFMUSP), São Paulo, São Paulo, Brazil
| | | | | | - Cristina Saldivia-Siracusa
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil
| | - Tamires Brito-Sarracino
- Institute of Mathematics and Computer Sciences, University of São Paulo (ICMC-USP), São Carlos, São Paulo, Brazil
| | - Márcio Ajudarte Lopes
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil
| | - Pablo Agustin Vargas
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil
| | - Alexander T Pearson
- Section of Hemathology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, USA; University of Chicago Comprehensive Cancer Center, Chicago, IL, USA
| | - Luiz Paulo Kowalski
- Department of Head and Neck Surgery and Otorhinolaryngology, A.C. Camargo Cancer Center, São Paulo, São Paulo, Brazil; Head and Neck Surgery Department and LIM 28, University of São Paulo Medical School, São Paulo, São Paulo, Brazil
| | | | - Alan Roger Santos-Silva
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil.
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12
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Talwar V, Singh P, Mukhia N, Shetty A, Birur P, Desai KM, Sunkavalli C, Varma KS, Sethuraman R, Jawahar CV, Vinod PK. AI-Assisted Screening of Oral Potentially Malignant Disorders Using Smartphone-Based Photographic Images. Cancers (Basel) 2023; 15:4120. [PMID: 37627148 PMCID: PMC10452422 DOI: 10.3390/cancers15164120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
The prevalence of oral potentially malignant disorders (OPMDs) and oral cancer is surging in low- and middle-income countries. A lack of resources for population screening in remote locations delays the detection of these lesions in the early stages and contributes to higher mortality and a poor quality of life. Digital imaging and artificial intelligence (AI) are promising tools for cancer screening. This study aimed to evaluate the utility of AI-based techniques for detecting OPMDs in the Indian population using photographic images of oral cavities captured using a smartphone. A dataset comprising 1120 suspicious and 1058 non-suspicious oral cavity photographic images taken by trained front-line healthcare workers (FHWs) was used for evaluating the performance of different deep learning models based on convolution (DenseNets) and Transformer (Swin) architectures. The best-performing model was also tested on an additional independent test set comprising 440 photographic images taken by untrained FHWs (set I). DenseNet201 and Swin Transformer (base) models show high classification performance with an F1-score of 0.84 (CI 0.79-0.89) and 0.83 (CI 0.78-0.88) on the internal test set, respectively. However, the performance of models decreases on test set I, which has considerable variation in the image quality, with the best F1-score of 0.73 (CI 0.67-0.78) obtained using DenseNet201. The proposed AI model has the potential to identify suspicious and non-suspicious oral lesions using photographic images. This simplified image-based AI solution can assist in screening, early detection, and prompt referral for OPMDs.
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Affiliation(s)
- Vivek Talwar
- CVIT, International Institute of Information Technology, Hyderabad 500032, India; (V.T.); (C.V.J.)
| | - Pragya Singh
- INAI, International Institute of Information Technology, Hyderabad 500032, India; (P.S.); (K.S.V.)
| | - Nirza Mukhia
- Department of Oral Medicine and Radiology, KLE Society’s Institute of Dental Sciences, Bengaluru 560022, India; (N.M.); (P.B.)
| | | | - Praveen Birur
- Department of Oral Medicine and Radiology, KLE Society’s Institute of Dental Sciences, Bengaluru 560022, India; (N.M.); (P.B.)
| | - Karishma M. Desai
- iHUB-Data, International Institute of Information Technology, Hyderabad 500032, India;
| | | | - Konala S. Varma
- INAI, International Institute of Information Technology, Hyderabad 500032, India; (P.S.); (K.S.V.)
- Intel Technology India Private Limited, Bengaluru, India;
| | | | - C. V. Jawahar
- CVIT, International Institute of Information Technology, Hyderabad 500032, India; (V.T.); (C.V.J.)
| | - P. K. Vinod
- CCNSB, International Institute of Information Technology, Hyderabad 500032, India
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13
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Y D, Ramalingam K, Ramani P, Mohan Deepak R. Machine Learning in the Detection of Oral Lesions With Clinical Intraoral Images. Cureus 2023; 15:e44018. [PMID: 37753028 PMCID: PMC10519616 DOI: 10.7759/cureus.44018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 08/24/2023] [Indexed: 09/28/2023] Open
Abstract
INTRODUCTION Artificial intelligence in oncology has gained a lot of interest in recent years. Early detection of Oral squamous cell carcinoma (OSCC) is crucial for early management to attain a better prognosis and overall survival. Machine learning (ML) has also been used in oral cancer studies to explore the discrimination between clinically normal and oral cancer. MATERIALS AND METHODS A dataset comprising 360 clinical intra-oral images of OSCC, Oral Potentially Malignant Disorders (OPMDs) and clinically healthy oral mucosa were used. Clinicians trained the machine learning model with the clinical images (n=300). Roboflow software (Roboflow Inc, USA) was used to classify and annotate images along with Multi-class annotation and object detection models trained by two expert oral pathologists. The test dataset (n=60) of new clinical images was again evaluated by two clinicians and Roboflow. The results were tabulated and Kappa statistics was performed using SPSS v23.0 (IBM Corp., Armonk, NY). Results: Training dataset clinical images (n=300) were used to train the clinicians and Roboflow algorithm. The test dataset (n=60) of new clinical images was again evaluated by the clinicians and Roboflow. The observed outcomes revealed that the Mean Average Precision (mAP) was 25.4%, precision 29.8% and Recall 32.9%. Based on the kappa statistical analysis the 0.7 value shows a moderate agreement between the clinicians and the machine learning model. The test dataset showed the specificity and sensitivity of the Roboflow machine learning model to be 75% and 88.9% respectively. Conclusion: In conclusion, machine learning showed promising results in the early detection of suspected lesions using clinical intraoral images and aids general dentists and patients in the detection of suspected lesions such as OPMDs and OSCC that require biopsy and immediate treatment.
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Affiliation(s)
- Dinesh Y
- Oral Pathology and Microbiology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND
| | - Karthikeyan Ramalingam
- Oral Pathology and Microbiology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND
| | - Pratibha Ramani
- Oral Pathology and Microbiology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND
| | - Ramya Mohan Deepak
- Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND
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14
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Khanagar SB, Alkadi L, Alghilan MA, Kalagi S, Awawdeh M, Bijai LK, Vishwanathaiah S, Aldhebaib A, Singh OG. Application and Performance of Artificial Intelligence (AI) in Oral Cancer Diagnosis and Prediction Using Histopathological Images: A Systematic Review. Biomedicines 2023; 11:1612. [PMID: 37371706 DOI: 10.3390/biomedicines11061612] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 05/27/2023] [Accepted: 05/31/2023] [Indexed: 06/29/2023] Open
Abstract
Oral cancer (OC) is one of the most common forms of head and neck cancer and continues to have the lowest survival rates worldwide, even with advancements in research and therapy. The prognosis of OC has not significantly improved in recent years, presenting a persistent challenge in the biomedical field. In the field of oncology, artificial intelligence (AI) has seen rapid development, with notable successes being reported in recent times. This systematic review aimed to critically appraise the available evidence regarding the utilization of AI in the diagnosis, classification, and prediction of oral cancer (OC) using histopathological images. An electronic search of several databases, including PubMed, Scopus, Embase, the Cochrane Library, Web of Science, Google Scholar, and the Saudi Digital Library, was conducted for articles published between January 2000 and January 2023. Nineteen articles that met the inclusion criteria were then subjected to critical analysis utilizing QUADAS-2, and the certainty of the evidence was assessed using the GRADE approach. AI models have been widely applied in diagnosing oral cancer, differentiating normal and malignant regions, predicting the survival of OC patients, and grading OC. The AI models used in these studies displayed an accuracy in a range from 89.47% to 100%, sensitivity from 97.76% to 99.26%, and specificity ranging from 92% to 99.42%. The models' abilities to diagnose, classify, and predict the occurrence of OC outperform existing clinical approaches. This demonstrates the potential for AI to deliver a superior level of precision and accuracy, helping pathologists significantly improve their diagnostic outcomes and reduce the probability of errors. Considering these advantages, regulatory bodies and policymakers should expedite the process of approval and marketing of these products for application in clinical scenarios.
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Affiliation(s)
- Sanjeev B Khanagar
- Preventive Dental Science Department, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
- King Abdullah International Medical Research Centre, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
| | - Lubna Alkadi
- King Abdullah International Medical Research Centre, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
- Restorative and Prosthetic Dental Sciences Department, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
| | - Maryam A Alghilan
- King Abdullah International Medical Research Centre, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
- Restorative and Prosthetic Dental Sciences Department, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
| | - Sara Kalagi
- King Abdullah International Medical Research Centre, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
- Restorative and Prosthetic Dental Sciences Department, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
| | - Mohammed Awawdeh
- Preventive Dental Science Department, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
- King Abdullah International Medical Research Centre, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
| | - Lalitytha Kumar Bijai
- King Abdullah International Medical Research Centre, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
- Maxillofacial Surgery and Diagnostic Sciences Department, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
| | - Satish Vishwanathaiah
- Department of Preventive Dental Sciences, Division of Pediatric Dentistry, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia
| | - Ali Aldhebaib
- King Abdullah International Medical Research Centre, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
- Radiological Sciences Program, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
| | - Oinam Gokulchandra Singh
- King Abdullah International Medical Research Centre, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
- Radiological Sciences Program, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
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15
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Dhopte A, Bagde H. Smart Smile: Revolutionizing Dentistry With Artificial Intelligence. Cureus 2023; 15:e41227. [PMID: 37529520 PMCID: PMC10387377 DOI: 10.7759/cureus.41227] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 06/30/2023] [Indexed: 08/03/2023] Open
Abstract
Artificial intelligence (AI) has emerged as a transformative technology in various industries, and its potential in dentistry is gaining significant attention. This abstract explores the future prospects of AI in dentistry, highlighting its potential to revolutionize clinical practice, improve patient outcomes, and enhance the overall efficiency of dental care. The application of AI in dentistry encompasses several key areas, including diagnosis, treatment planning, image analysis, patient management, and personalized care. AI algorithms have shown promising results in the automated detection and diagnosis of dental conditions, such as caries, periodontal diseases, and oral cancers, aiding clinicians in early intervention and improving treatment outcomes. Furthermore, AI-powered treatment planning systems leverage machine learning techniques to analyze vast amounts of patient data, considering factors like medical history, anatomical variations, and treatment success rates. These systems provide dentists with valuable insights and support in making evidence-based treatment decisions, ultimately leading to more predictable and tailored treatment approaches. While the potential of AI in dentistry is immense, it is essential to address certain challenges, including data privacy, algorithm bias, and regulatory considerations. Collaborative efforts between dental professionals, AI experts, and policymakers are crucial to developing robust frameworks that ensure the responsible and ethical implementation of AI in dentistry. Moreover, AI-driven robotics has introduced innovative approaches to dental surgery, enabling precise and minimally invasive procedures, and ultimately reducing patient discomfort and recovery time. Virtual reality (VR) and augmented reality (AR) applications further enhance dental education and training, allowing dental professionals to refine their skills in a realistic and immersive environment. AI holds tremendous promise in shaping the future of dentistry. Through its ability to analyze vast amounts of data, provide accurate diagnoses, facilitate treatment planning, improve image analysis, streamline patient management, and enable personalized care, AI has the potential to enhance dental practice and significantly improve patient outcomes. Embracing this technology and its future development will undoubtedly revolutionize the field of dentistry, fostering a more efficient, precise, and patient-centric approach to oral healthcare. Overall, AI represents a powerful tool that has the potential to revolutionize various aspects of society, from improving healthcare outcomes to optimizing business operations. Continued research, development, and responsible implementation of AI technologies will shape our future, unlocking new possibilities and transforming the way we live and work.
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Affiliation(s)
- Ashwini Dhopte
- Department of Oral Medicine and Radiology, Rama Dental College and Research Centre, Kanpur, IND
| | - Hiroj Bagde
- Department of Periodontology, Rama Dental College and Research Centre, Kanpur, IND
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16
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de Souza LL, Fonseca FP, Araújo ALD, Lopes MA, Vargas PA, Khurram SA, Kowalski LP, Dos Santos HT, Warnakulasuriya S, Dolezal J, Pearson AT, Santos-Silva AR. Machine learning for detection and classification of oral potentially malignant disorders: A conceptual review. J Oral Pathol Med 2023; 52:197-205. [PMID: 36792771 DOI: 10.1111/jop.13414] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 12/09/2022] [Indexed: 02/17/2023]
Abstract
Oral potentially malignant disorders represent precursor lesions that may undergo malignant transformation to oral cancer. There are many known risk factors associated with the development of oral potentially malignant disorders, and contribute to the risk of malignant transformation. Although many advances have been reported to understand the biological behavior of oral potentially malignant disorders, their clinical features that indicate the characteristics of malignant transformation are not well established. Early diagnosis of malignancy is the most important factor to improve patients' prognosis. The integration of machine learning into routine diagnosis has recently emerged as an adjunct to aid clinical examination. Increased performances of artificial intelligence AI-assisted medical devices are claimed to exceed the human capability in the clinical detection of early cancer. Therefore, the aim of this narrative review is to introduce artificial intelligence terminology, concepts, and models currently used in oncology to familiarize oral medicine scientists with the language skills, best research practices, and knowledge for developing machine learning models applied to the clinical detection of oral potentially malignant disorders.
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Affiliation(s)
- Lucas Lacerda de Souza
- Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), São Paulo, Brazil
| | - Felipe Paiva Fonseca
- Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), São Paulo, Brazil
- Department of Oral Surgery and Pathology, School of Dentistry, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Marcio Ajudarte Lopes
- Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), São Paulo, Brazil
| | - Pablo Agustin Vargas
- Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), São Paulo, Brazil
| | - Syed Ali Khurram
- Unit of Oral & Maxillofacial Pathology, School of Clinical Dentistry, University of Sheffield, Sheffield, UK
| | - Luiz Paulo Kowalski
- Department of Head and Neck Surgery, University of Sao Paulo Medical School and Department of Head and Neck Surgery and Otorhinolaryngology, AC Camargo Cancer Center, Sao Paulo, Brazil
| | - Harim Tavares Dos Santos
- Department of Otolaryngology-Head and Neck Surgery, University of Missouri, Columbia, Missouri, USA
- Department of Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
| | - Saman Warnakulasuriya
- King's College London, London, UK
- WHO Collaborating Centre for Oral Cancer, London, UK
| | - James Dolezal
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, Illinois, USA
| | - Alexander T Pearson
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, Illinois, USA
| | - Alan Roger Santos-Silva
- Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), São Paulo, Brazil
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17
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Ding H, Wu J, Zhao W, Matinlinna JP, Burrow MF, Tsoi JKH. Artificial intelligence in dentistry—A review. FRONTIERS IN DENTAL MEDICINE 2023. [DOI: 10.3389/fdmed.2023.1085251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023] Open
Abstract
Artificial Intelligence (AI) is the ability of machines to perform tasks that normally require human intelligence. AI is not a new term, the concept of AI can be dated back to 1950. However, it has not become a practical tool until two decades ago. Owing to the rapid development of three cornerstones of current AI technology—big data (coming through digital devices), computational power, and AI algorithm—in the past two decades, AI applications have been started to provide convenience to people's lives. In dentistry, AI has been adopted in all dental disciplines, i.e., operative dentistry, periodontics, orthodontics, oral and maxillofacial surgery, and prosthodontics. The majority of the AI applications in dentistry go to the diagnosis based on radiographic or optical images, while other tasks are not as applicable as image-based tasks mainly due to the constraints of data availability, data uniformity, and computational power for handling 3D data. Evidence-based dentistry (EBD) is regarded as the gold standard for the decision-making of dental professionals, while AI machine learning (ML) models learn from human expertise. ML can be seen as another valuable tool to assist dental professionals in multiple stages of clinical cases. This review narrated the history and classification of AI, summarised AI applications in dentistry, discussed the relationship between EBD and ML, and aimed to help dental professionals to understand AI as a tool better to assist their routine work with improved efficiency.
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