ArticleBioengineering (Basel, Switzerland)2022
A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph.
Article in Bioengineering (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- Diagnostic performance of artificial intelligence in periapical radiography: a systematic review.Odontology · 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
- A Network-Medicine Framework for Intra-Oral Comorbidity: Age-Stratified Clustering and Quasi-Causal Progression Modeling from Outpatient Electronic Health Records.Bioengineering (Basel, Switzerland) · 2026Article
- Evaluating the Test Characteristics of a Prototype for AI-Assisted Radiographic Detection.Dentistry journal · 2026Article
- Detection of Periapical Lesions Using Artificial Intelligence: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Evaluation of a no-code AI model for detecting periapical radiolucencies: impact of anatomical region on diagnostic performance.BMC oral health · 2025Article
- Role of Artificial Intelligence and Machine Learning in Conservative Dentistry and Endodontics: A Review.Cureus · 2025Review
- Automated Caries Detection Under Dental Restorations and Braces Using Deep Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Deep Learning-Assisted Diagnostic System: Apices and Odontogenic Sinus Floor Level Analysis in Dental Panoramic Radiographs.Bioengineering (Basel, Switzerland) · 2025Article
- Precision Medicine Assessment of the Radiographic Defect Angle of the Intrabony Defect in Periodontal Lesions by Deep Learning of Bitewing Radiographs.Bioengineering (Basel, Switzerland) · 2025Article
- Comprehensive Insights into Artificial Intelligence for Dental Lesion Detection: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Precision Medicine for Apical Lesions and Peri-Endo Combined Lesions Based on Transfer Learning Using Periapical Radiographs.Bioengineering (Basel, Switzerland) · 2024Article
- Auxiliary Diagnosis of Dental Calculus Based on Deep Learning and Image Enhancement by Bitewing Radiographs.Bioengineering (Basel, Switzerland) · 2024Article
- Deep Learning-Based Recognition of Periodontitis and Dental Caries in Dental X-ray Images.Bioengineering (Basel, Switzerland) · 2023Article
- Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Deep Learning for Dental Diagnosis: A Novel Approach to Furcation Involvement Detection on Periapical Radiographs.Bioengineering (Basel, Switzerland) · 2023Article
- Improving Dental Implant Outcomes: CNN-Based System Accurately Measures Degree of Peri-Implantitis Damage on Periapical Film.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 3 countries.
Funding
Abstract
Apical Lesions, one of the most common oral diseases, can be effectively detected in daily dental examinations by a periapical radiograph (PA). In the current popular endodontic treatment, most dentists spend a lot of time manually marking the lesion area. In order to reduce the burden on dentists, this paper proposes a convolutional neural network (CNN)-based regional analysis model for spical lesions for periapical radiographs. In this study, the database was provided by dentists with more than three years of practical experience, meeting the criteria for clinical practical application. The contributions of this work are (1) an advanced adaptive threshold preprocessing technique for image segmentation, which can achieve an accuracy rate of more than 96%; (2) a better and more intuitive apical lesions symptom enhancement technique; and (3) a model for apical lesions detection with an accuracy as high as 96.21%. Compared with existing state-of-the-art technology, the proposed model has improved the accuracy by more than 5%. The proposed model has successfully improved the automatic diagnosis of apical lesions. With the help of automation, dentists can focus more on technical and medical diagnoses, such as treatment, tooth cleaning, or medical communication. This proposal has been certified by the Institutional Review Board (IRB) with the certification number 202002030B0.
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Registered trials
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.