Periapical lesion detection in periapical radiographs using the latest convolutional neural network ConvNeXt and its integrated models

• Published in: Scientific reports Vol. 14; no. 1; pp. 25429 - 10
• Main Authors: Liu, Jian, Liu, Xiaohua, Shao, Yu, Gao, Yongzhen, Pan, Kexu, Jin, Chaoran, Ji, Honghai, Du, Yi, Yu, Xijiao
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 25-10-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308; 692/699; Accuracy; Classification; Deep Learning; Humanities and Social Sciences; Humans; Image processing; Lesions; multidisciplinary; Neural networks; Neural Networks, Computer; Periapical Diseases - diagnostic imaging; Radiography; Science; Science (multidisciplinary); Segmentation; Sensitivity analysis; Teeth
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-75748-9

To overcome the limitation of a single classification model’s inability to simultaneously identify multiple lesion targets within periapical radiographs, This study proposes YoCNET (Yolov5 + ConvNeXt), a novel deep learning integrated model. YoCNET leverages the target detection capability of Yolov5 and the image classification capability of ConvNeXt to achieve automatic segmentation of individual teeth and concurrent detection of periapical lesions across multiple teeth. A dataset of 1,305 periapical radiographs was used to train and validate the ConvNeXt and ResNet34 models, with an 8:2 split for training and validation. Deciduous teeth were excluded from the dataset. Furthermore, 717 individual teeth images were extracted from 200 previously unused periapical radiographs for integrated model validation. Evaluation metrics included accuracy, precision, sensitivity, F1 score, AUC (Area Under Curve), and a confusion matrix.The YoCNET integrated model demonstrated values of 90.93%, 98.88%, 85.30%, 0.9159, and 0.9757 for accuracy, precision, sensitivity, F1 score, and AUC, respectively. These metrics were superior to those achieved by the YoRNET (Yolov5 + ResNet34) integrated model, which recorded 80.47%, 83.78%, 82.16%, 0.8296, and 0.8822. The integrated model achieved high accuracy and efficiency in automatic teeh segmentation by Yolov5 and in automatically detecting multiple periapical lesions by ConvNeXt. YoCNET exhibited superior overall data performance, making it a more suitable deep learning integrated model for clinical applications.