Louloua Mourad , Nayer Aboelsaad , Wael M. Talaat , Nada M.H. Fahmy , Hams H. Abdelrahman , Yehia El-Mahallawy
{"title":"利用深度学习人工智能自动检测颞下颌关节骨关节炎的影像学特征。诊断准确性研究。","authors":"Louloua Mourad , Nayer Aboelsaad , Wael M. Talaat , Nada M.H. Fahmy , Hams H. Abdelrahman , Yehia El-Mahallawy","doi":"10.1016/j.jormas.2024.102124","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><div>The purpose of this study was to investigate the diagnostic performance of a neural network Artificial Intelligence model for the radiographic confirmation of Temporomandibular Joint Osteoarthritis in reference to an experienced radiologist.</div></div><div><h3>Materials and Methods</h3><div>The diagnostic performance of an AI model in identifying radiographic features in patients with TMJ-OA was evaluated in a diagnostic accuracy cohort study. Adult patients elected for radiographic examination by the Diagnostic Criteria for Temporomandibular Disorders decision tree were included. Cone-beam computed Tomography images were evaluated by object detection YOLO deep learning model. The diagnostic performance was verified against examiner radiographic evaluation.</div></div><div><h3>Results</h3><div>The differences between the AI model and examiner were non-significant statistically, except in the subcortical cyst (<em>P</em> <em>=</em> <em>0.049*</em>). AI model showed substantial to near-perfect levels of agreement when compared to those of the examiner data. Regarding each radiographic phenotype, the AI model reported favorable sensitivity, specificity, accuracy, and highly statistically significant Receiver Operating Characteristic (ROC) analysis (<em>p</em> <em><</em> <em>0.001</em>). Area Under Curve ranged from 0.872, for surface erosion, to 0.911 for subcortical cyst.</div></div><div><h3>Conclusion</h3><div>AI object detection model could open the horizon for a valid, automated, and convenient modality for TMJ-OA radiographic confirmation and radiomic features identification with a significant diagnostic power.</div></div>","PeriodicalId":55993,"journal":{"name":"Journal of Stomatology Oral and Maxillofacial Surgery","volume":"126 4","pages":"Article 102124"},"PeriodicalIF":1.8000,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automatic detection of temporomandibular joint osteoarthritis radiographic features using deep learning artificial intelligence. A Diagnostic accuracy study\",\"authors\":\"Louloua Mourad , Nayer Aboelsaad , Wael M. Talaat , Nada M.H. Fahmy , Hams H. Abdelrahman , Yehia El-Mahallawy\",\"doi\":\"10.1016/j.jormas.2024.102124\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><div>The purpose of this study was to investigate the diagnostic performance of a neural network Artificial Intelligence model for the radiographic confirmation of Temporomandibular Joint Osteoarthritis in reference to an experienced radiologist.</div></div><div><h3>Materials and Methods</h3><div>The diagnostic performance of an AI model in identifying radiographic features in patients with TMJ-OA was evaluated in a diagnostic accuracy cohort study. Adult patients elected for radiographic examination by the Diagnostic Criteria for Temporomandibular Disorders decision tree were included. Cone-beam computed Tomography images were evaluated by object detection YOLO deep learning model. The diagnostic performance was verified against examiner radiographic evaluation.</div></div><div><h3>Results</h3><div>The differences between the AI model and examiner were non-significant statistically, except in the subcortical cyst (<em>P</em> <em>=</em> <em>0.049*</em>). AI model showed substantial to near-perfect levels of agreement when compared to those of the examiner data. Regarding each radiographic phenotype, the AI model reported favorable sensitivity, specificity, accuracy, and highly statistically significant Receiver Operating Characteristic (ROC) analysis (<em>p</em> <em><</em> <em>0.001</em>). Area Under Curve ranged from 0.872, for surface erosion, to 0.911 for subcortical cyst.</div></div><div><h3>Conclusion</h3><div>AI object detection model could open the horizon for a valid, automated, and convenient modality for TMJ-OA radiographic confirmation and radiomic features identification with a significant diagnostic power.</div></div>\",\"PeriodicalId\":55993,\"journal\":{\"name\":\"Journal of Stomatology Oral and Maxillofacial Surgery\",\"volume\":\"126 4\",\"pages\":\"Article 102124\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Stomatology Oral and Maxillofacial Surgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2468785524004130\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"DENTISTRY, ORAL SURGERY & MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Stomatology Oral and Maxillofacial Surgery","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468785524004130","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"DENTISTRY, ORAL SURGERY & MEDICINE","Score":null,"Total":0}
Automatic detection of temporomandibular joint osteoarthritis radiographic features using deep learning artificial intelligence. A Diagnostic accuracy study
Objective
The purpose of this study was to investigate the diagnostic performance of a neural network Artificial Intelligence model for the radiographic confirmation of Temporomandibular Joint Osteoarthritis in reference to an experienced radiologist.
Materials and Methods
The diagnostic performance of an AI model in identifying radiographic features in patients with TMJ-OA was evaluated in a diagnostic accuracy cohort study. Adult patients elected for radiographic examination by the Diagnostic Criteria for Temporomandibular Disorders decision tree were included. Cone-beam computed Tomography images were evaluated by object detection YOLO deep learning model. The diagnostic performance was verified against examiner radiographic evaluation.
Results
The differences between the AI model and examiner were non-significant statistically, except in the subcortical cyst (P=0.049*). AI model showed substantial to near-perfect levels of agreement when compared to those of the examiner data. Regarding each radiographic phenotype, the AI model reported favorable sensitivity, specificity, accuracy, and highly statistically significant Receiver Operating Characteristic (ROC) analysis (p<0.001). Area Under Curve ranged from 0.872, for surface erosion, to 0.911 for subcortical cyst.
Conclusion
AI object detection model could open the horizon for a valid, automated, and convenient modality for TMJ-OA radiographic confirmation and radiomic features identification with a significant diagnostic power.