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A diagnostic research on the detection of occlusal caries from a scientific {photograph} utilizing a deep studying algorithm will likely be offered on the 101st General Session of the IADR, which will likely be held together with the 9th Meeting of the Latin American Region and the 12th World Congress on Preventive Dentistry on June 21-24, 2023, in Bogotá, Colombia.
The Interactive Talk presentation, “Automated Detection of Occlusal Caries Using Deep Learning Algorithm,” will happen on Saturday, June 24 at 4:25 p.m. Colombia Time (UTC-05:00) throughout the “Prevalence of Health Conditions and Risk Factors” session.
The research by Chukwuebuka Elozona Ogwo of Temple University, Philadelphia, PA, USA sought to find out the accuracy, precision, and sensitivity of the YOLOv7 object detection algorithm in occlusal caries detection from scientific images and (2) develop software program for occlusal caries detection.
Only consenting adults (>=18 years outdated) with everlasting dentition receiving care on the Temple University Kornberg School of Dentistry had been included within the research. 300 intraoral images of the occlusal surfaces of each mandibular and maxillary arches had been collected by 4th-year dental college students utilizing the Coolpix L840 cameras. The pictures had been annotated utilizing Roboflow V4. After knowledge preprocessing and augmentation, 845 pictures had been generated and randomly cut up into three units: coaching, validation, and testing – 70:20:10, respectively.
The knowledge was then analyzed utilizing the YOLO v7 at 100 epochs, with a batch dimension of 1 and picture dimension of 1280×640. The algorithm efficiency metrics had been imply common precision (mAP), recall (sensitivity), and precision (Positive predictive worth). The closing algorithm was used to create software program on Flask and deployed it on Heroku.
The algorithm resulted in 79.5% precision, 83% recall, an 81.2% F1-score, and 80% [email protected] rating within the detection of occlusal caries on a scientific {photograph} of each the mandibular and maxillary arches. The research yielded a promising results of AI in automating the detection of the carious lesion from a scientific {photograph}. When deployed as a telephone app, it might function an vital instrument for teledentistry and enhance entry to care.