Calculating percentage bone loss is a critical test for periodontal disease staging but is sometimes imprecise and time consuming when manually calculated. This study evaluates the application of a deep learning keypoint and object detection model, YOLOv8-pose, for the automatic identification of localised periodontal bone loss landmarks, conditions and staging. YOLOv8-pose was fine-tuned on 193 annotated periapical radiographs. We propose a keypoint detection metric, Percentage of Relative Correct Keypoints (PRCK), which normalises the metric to the average tooth size of teeth in the image. We propose a heuristic post-processing module that adjusts certain keypoint predictions to align with the edge of the related tooth, using a supporting instance segmentation model trained on an open source auxiliary dataset. The model can sufficiently detect bone loss keypoints, tooth boxes, and alveolar ridge resorption, but has insufficient performance at detecting detached periodontal ligament and furcation involvement. The model with post-processing demonstrated a PRCK 0.25 of 0.726 and PRCK 0.05 of 0.401 for keypoint detection, mAP 0.5 of 0.715 for tooth object detection, mesial dice score of 0.593 for periodontal staging, and dice score of 0.280 for furcation involvement. Our annotation methodology provides a stage agnostic approach to periodontal disease detection, by ensuring most keypoints are present for each tooth in the image, allowing small imbalanced datasets. Our PRCK metric allows accurate evaluation of keypoints in dental domains. Our post-processing module adjusts predicted keypoints correctly but is dependent on a minimum quality of prediction by the pose detection and segmentation models. Code: https:// anonymous.4open.science/r/Bone-Loss-Keypoint-Detection-Code. Dataset: https://bit.ly/4hJ3aE7.

The MIT alumni-founded Overjet uses artificial intelligence to annotate dental X-rays for dentists. MIT alumni-founded company analyzes and annotates dental X-rays to help dentists offer more comprehensive care. A hospital radiologist is often pictured as a specialist who sits in a dark room and spends hours poring over X-rays to make diagnoses. Contrast that with your dentist, who in addition to interpreting X-rays must also perform surgery, communicate with patients, manage staff, and run their business. When dentists analyze X-rays, they generally do so in bright rooms and on computers that aren't specialized for radiology, often with the patient sitting right next to them.

When you picture a hospital radiologist, you might think of a specialist who sits in a dark room and spends hours poring over X-rays to make diagnoses. Contrast that with your dentist, who in addition to interpreting X-rays must also perform surgery, manage staff, communicate with patients, and run their business. When dentists analyze X-rays, they do so in bright rooms and on computers that aren't specialized for radiology, often with the patient sitting right next to them. Is it any wonder, then, that dentists given the same X-ray might propose different treatments? "Dentists are doing a great job given all the things they have to deal with," says Wardah Inam SM '13, Ph.D. '16.

When you picture a hospital radiologist, you might think of a specialist who sits in a dark room and spends hours poring over X-rays to make diagnoses. Contrast that with your dentist, who in addition to interpreting X-rays must also perform surgery, manage staff, communicate with patients, and run their business. When dentists analyze X-rays, they do so in bright rooms and on computers that aren't specialized for radiology, often with the patient sitting right next to them. Is it any wonder, then, that dentists given the same X-ray might propose different treatments? "Dentists are doing a great job given all the things they have to deal with," says Wardah Inam SM '13, PhD '16.

A deep learning algorithm successfully detects periodontal disease from 2D bitewing radiographs, according to research presented at EuroPerio10, the world's leading congress in periodontology and implant dentistry organized by the European Federation of Periodontology (EFP). "Our study shows the potential for artificial intelligence (AI) to automatically identify periodontal pathologies that might otherwise be missed," said study author Dr. Burak Yavuz of Eskisehir Osmangazi University, Turkey. "This could reduce radiation exposure by avoiding repeat assessments, prevent the silent progression of periodontal disease, and enable earlier treatment." Previous studies have examined the use of AI to detect caries, root fractures and apical lesions but there is limited research in the field of periodontology. This study evaluated the ability of deep learning, a type of AI, to determine periodontal status in bitewing radiographs.