ArticleJournal of clinical medicine2023
Evaluating a Periapical Lesion Detection CNN on a Clinically Representative CBCT Dataset-A Validation Study.
Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application and performance of artificial intelligence-based models in the detection, segmentation and classification of periapical lesions: a systematic review.Frontiers in dental medicine · 2025Pooled it
- Comparative assessment of CBCT and digital radiography in quantifying periapical pathology (in vitro study).BMC oral health · 2026Article
- Artificial Intelligence in Endodontics-A Bibliometric Analysis.Australian endodontic journal : the journal of the Australian Society of Endodontology Inc · 2026Review
- Diagnostic Performance of AI-Based Cloud Software Regarding the Detection of Endodontic Findings on CBCT: A Single-Centre Cross-Sectional Validation Study.Journal of clinical medicine · 2026Article
- YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.Journal of imaging · 2026Article
- Review
- Automated periapical lesion segmentation and area-based PAI indexing: a comparative deep learning study on periapical radiographs.BMC oral health · 2026Article
- Applications and clinical translation of artificial intelligence in CBCT-based detection of endodontic lesions: a scoping review.Oral radiology · 2026Article
- Machine learning vs. radiomics for discriminating atypical cartilaginous tumors from benign enchondromas on MRI.Biomedical engineering online · 2026Article
- Detection of Periapical Lesions Using Artificial Intelligence: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Accuracy of artificial intelligence applications in periodontics: a thematic narrative review.Frontiers in dental medicine · 2026Review
- Evaluation of a no-code AI model for detecting periapical radiolucencies: impact of anatomical region on diagnostic performance.BMC oral health · 2025Article
- A comparative analysis of deep learning models for assisting in the diagnosis of periapical lesions in periapical radiographs.BMC oral health · 2025Article
- Comprehensive Insights into Artificial Intelligence for Dental Lesion Detection: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Periapical lesion detection in periapical radiographs using the latest convolutional neural network ConvNeXt and its integrated models.Scientific reports · 2024Article
- Implicit Is Not Enough: Explicitly Enforcing Anatomical Priors inside Landmark Localization Models.Bioengineering (Basel, Switzerland) · 2024Article
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7 authors.
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Abstract
The aim of this validation study was to comprehensively evaluate the performance and generalization capability of a deep learning-based periapical lesion detection algorithm on a clinically representative cone-beam computed tomography (CBCT) dataset and test for non-inferiority. The evaluation involved 195 CBCT images of adult upper and lower jaws, where sensitivity and specificity metrics were calculated for all teeth, stratified by jaw, and stratified by tooth type. Furthermore, each lesion was assigned a periapical index score based on its size to enable a score-based evaluation. Non-inferiority tests were conducted with proportions of 90% for sensitivity and 82% for specificity. The algorithm achieved an overall sensitivity of 86.7% and a specificity of 84.3%. The non-inferiority test indicated the rejection of the null hypothesis for specificity but not for sensitivity. However, when excluding lesions with a periapical index score of one (i.e., very small lesions), the sensitivity improved to 90.4%. Despite the challenges posed by the dataset, the algorithm demonstrated promising results. Nevertheless, further improvements are needed to enhance the algorithm's robustness, particularly in detecting very small lesions and the handling of artifacts and outliers commonly encountered in real-world clinical scenarios.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.