Diagnosis of interproximal caries lesions in bitewing radiographs using a deep convolutional neural network-based software.

The aim of this study was to evaluate the diagnostic reliability of a web-based artificial intelligence program for the detection of interproximal caries in bitewing radiographs. Three hundred bitewing radiographs of patients were subjected to the evaluation of a convolutional neural network. First, the images were visually evaluated by a previously trained and calibrated operator with radiodiagnosis experience. After, ground truth was established and was clinically validated. For enamel caries, clinical assessment included a combination of clinical-visual and radiography evaluations. For dentin caries, clinical validation was performed by instrumentally accessing to the cavity. Secondly, the images were uploaded and analyzed by the web-based software. Four different models were established to analyze its evaluations according to the confidence threshold (0-100%) offered by the program: model 1 (values> 0% were considered positive and values of 0% were considered negative), model 2 (values ≥ 25% were considered positive and values

Source: Caries Research - November 1, 2022 Category: Dentistry Source Type: research