ArticleDiagnostics (Basel, Switzerland)2020
Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 5 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
69 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Preoperative Periapical Status and Outcomes of Vital Pulp Therapy: A Systematic Review and Meta-Analysis.Australian endodontic journal : the journal of the Australian Society of Endodontology Inc · 2026Pooled 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
- Accuracy of machine learning in the diagnosis of odontogenic cysts and tumors: a systematic review and meta-analysis.Oral radiology · 2024Pooled it
- Panoramic imaging errors in machine learning model development: a systematic review.Dento maxillo facial radiology · 2024Pooled it
- Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review.Pain research & management · 2021Pooled it
- Deep Learning in Dental Imaging: Advances, Challenges, and Future.Oral radiology · 2026Review
- Accuracy and Short-Term Consistency of Generative AI Chatbots in Guideline-Based Endodontic Decision Support: A Multi-Model Benchmarking Study.Dentistry journal · 2026Article
- Comparative diagnostic accuracy of a You Only Look Once-11s deep learning model and human observers for detection of periapical lesions on intraoral periapical radiographs.Journal of conservative dentistry and endodontics · 2026Article
- Automated Detection and Ordinal Severity Grading of Periapical Lesions on Panoramic Radiographs Using a Lesion-Preserving Tiling and Ordinal-Aware YOLO Framework.Diagnostics (Basel, Switzerland) · 2026Article
- YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.Journal of imaging · 2026Article
- The Influence of Panoramic Radiograph Quality on the Accuracy of AI-Based Tooth Detection.Diagnostics (Basel, Switzerland) · 2026Article
- Automated periapical lesion segmentation and area-based PAI indexing: a comparative deep learning study on periapical radiographs.BMC oral health · 2026Article
- Fully automated 3D segmentation of periapical lesions on CBCT images using nnU-Net v2.BMC medical imaging · 2026Article
- Artificial Intelligence in the Diagnosis of Odontogenous Cysts and Ameloblastomas-A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2026Review
- AI-Based Detection of Periapical Lesions in Endodontic Radiographs: a Diagnostic Accuracy Study.Maedica · 2026Article
- Impacts of artificial intelligence on computed tomography in endodontics: an integrative review.BMC oral health · 2026Review
- Artificial Intelligence Versus Human Dental Expertise in Diagnosing Periapical Pathosis on Periapical Radiographs: A Multicenter Study.Bioengineering (Basel, Switzerland) · 2026Article
- Evaluating the Test Characteristics of a Prototype for AI-Assisted Radiographic Detection.Dentistry journal · 2026Article
- Radiomics as a Decision Support Tool for Detecting Occult Periapical Lesions on Intraoral Radiographs.Journal of clinical medicine · 2026Article
- Clinical decisions in Orthodontics using x-ray-based images and artificial intelligence approaches: a scoping review.Dental press journal of orthodontics · 2026Article
9 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the most common radiographic findings in dentistry and have a differential diagnosis including infections, granuloma, cysts and tumors. In this study, we seek to investigate the ability with which 24 oral and maxillofacial (OMF) surgeons assess the presence of periapical lucencies on panoramic radiographs, and we compare these findings to the performance of a predictive deep learning algorithm that we have developed using a curated data set of 2902 de-identified panoramic radiographs. The mean diagnostic positive predictive value (PPV) of OMF surgeons based on their assessment of panoramic radiographic images was 0.69(± 0.13), indicating that dentists on average falsely diagnose 31% of cases as radiolucencies. However, the mean diagnostic true positive rate (TPR) was 0.51(± 0.14), indicating that on average 49% of all radiolucencies were missed. We demonstrate that the deep learning algorithm achieves a better performance than 14 of 24 OMF surgeons within the cohort, exhibiting an average precision of 0.60(± 0.04), and an F
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