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 diagnosis made by experienced dentists was used as the reference diagnosis. RESULTS: The Faster R-CNN and YOLOv4 models obtained great sensitivity, specificity, accuracy, and precision for detecting periapical lesions. No clear difference in the performance of both models among these three regions was found. The true prediction of Faster R-CNN was 89%, 83.01% and 91.84% for PAI 3, PAI 4 and PAI 5 lesions, respectively. The corresponding values of YOLOv4 were 68.06%, 50.94%, and 65.31%. CONCLUSIONS: Our study demonstrated the potential of YOLOv4 and Faster R-CNN models for detecting and classifying periapical lesions based on the PAI scoring system using periapical radiographs.

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. By using object detection models and algorithms, this work is much easier and needs less experienced medical practitioners to clear their doubts while diagnosing a medical case. In this paper, we propose a dental defect recognition model by the integration of Adaptive Convolution Neural Network and Bag of Visual Word (BoVW). In this model, BoVW is used to save the features extracted from images. After that, a designed Convolutional Neural Network (CNN) model is used to make quality prediction. To evaluate the proposed model, we collected a dataset of radiography images of 447 patients in Hanoi Medical Hospital, Vietnam, with third molar complications. The results of the model suggest accuracy of 84% ± 4%. This accuracy is comparable to that of experienced dentists and radiologists.

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. It firstly divides a dental X-Ray image into some segments and identified equivalent diseases by a classification method called Affinity Propagation Clustering (APC+). Lastly, the most potential disease is found using fuzzy aggregation operators. The experimental validation on real dental datasets of Hanoi Medical University Hospital, Vietnam showed the superiority of the proposed method against the relevant ones in terms of accuracy.