Automatic detection of temporomandibular joint osteoarthritis radiographic features using deep learning artificial intelligence. A Diagnostic accuracy study.
Louloua Mourad, Nayer Aboelsaad, Wael M Talaat, Nada M H Fahmy, Hams H Abdelrahman, Yehia El-Mahallawy
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引用次数: 0
Abstract
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.
期刊介绍：
J Stomatol Oral Maxillofac Surg publishes research papers and techniques - (guest) editorials, original articles, reviews, technical notes, case reports, images, letters to the editor, guidelines - dedicated to enhancing surgical expertise in all fields relevant to oral and maxillofacial surgery: from plastic and reconstructive surgery of the face, oral surgery and medicine, … to dentofacial and maxillofacial orthopedics.
Original articles include clinical or laboratory investigations and clinical or equipment reports. Reviews include narrative reviews, systematic reviews and meta-analyses.
All manuscripts submitted to the journal are subjected to peer review by international experts, and must:
Be written in excellent English, clear and easy to understand, precise and concise;
Bring new, interesting, valid information - and improve clinical care or guide future research;
Be solely the work of the author(s) stated;
Not have been previously published elsewhere and not be under consideration by another journal;
Be in accordance with the journal''s Guide for Authors'' instructions: manuscripts that fail to comply with these rules may be returned to the authors without being reviewed.
Under no circumstances does the journal guarantee publication before the editorial board makes its final decision.
The journal is indexed in the main international databases and is accessible worldwide through the ScienceDirect and ClinicalKey Platforms.
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