ArticleScientific reports2024
Periapical lesion detection in periapical radiographs using the latest convolutional neural network ConvNeXt and its integrated models.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of artificial intelligence in periapical radiography: a systematic review.Odontology · 2026Pooled it
- Deep Learning in Dental Imaging: Advances, Challenges, and Future.Oral radiology · 2026Review
- Automated periapical lesion segmentation and area-based PAI indexing: a comparative deep learning study on periapical radiographs.BMC oral health · 2026Article
- Radiomics as a Decision Support Tool for Detecting Occult Periapical Lesions on Intraoral Radiographs.Journal of clinical medicine · 2026Article
- Automated assessment of peri-implant disease severity by deep learning and image processing in periapical radiographs.Journal of dental sciences · 2026Article
- Accuracy of artificial intelligence applications in periodontics: a thematic narrative review.Frontiers in dental medicine · 2026Review
- CBMNet: a dual-attention enhanced ConvNeXt model for accurate G.V. Black type I-III classification in intraoral periapical radiographs.Scientific reports · 2025Article
- Artificial Intelligence in Endodontic Education: A Systematic Review with Frequentist and Bayesian Meta-Analysis of Student-Based Evidence.Dentistry journal · 2025Review
- Automated Detection of Periodontal Bone Loss in Two-Dimensional (2D) Radiographs Using Artificial Intelligence: A Systematic Review.Dentistry journal · 2025Review
- Explainable CNN-Radiomics Fusion and Ensemble Learning for Multimodal Lesion Classification in Dental Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- A visualization system for intelligent diagnosis and statistical analysis of oral diseases based on panoramic radiography.Scientific reports · 2025Article
- Artificial Intelligence in Dentistry: A Narrative Review of Diagnostic and Therapeutic Applications.Medical science monitor : international medical journal of experimental and clinical research · 2025Review
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Authors and funding
9 authors.
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Abstract
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.
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