Comparing the accuracy of two machine learning models in detection and classification of periapical lesions using periapical radiographs.

Background: Previous deep learning-based studies were mainly conducted on detecting periapical lesions; limited information in classification, such as the periapical index (PAI) scoring system, is available. The study aimed to apply two deep learning models, Faster R-CNN and YOLOv4, in detecting and classifying periapical lesions using the PAI score from periapical radiographs (PR) in three different regions of the dental arch: anterior teeth, premolars, and molars.

Methods: Out of 2658 PR selected for the study, 2122 PR were used for training, 268 PR were used for validation and 268 PR were used for testing.

The Combination of Adaptive Convolutional Neural Network and Bag of Visual Words in Automatic Diagnosis of Third Molar Complications on Dental X-Ray Images.

In dental diagnosis, recognizing tooth complications quickly from radiology (e.g., X-rays) takes highly experienced medical professionals.

Decision Making Based on Fuzzy Aggregation Operators for Medical Diagnosis from Dental X-ray images.

Medical diagnosis is considered as an important step in dentistry treatment which assists clinicians to give their decision about diseases of a patient. It has been affirmed that the accuracy of medical diagnosis, which is much influenced by the clinicians' experience and knowledge, plays an important role to effective treatment therapies. In this paper, we propose a novel decision making method based on fuzzy aggregation operators for medical diagnosis from dental X-Ray images.