ArticleDento maxillo facial radiology2022
The effect of a deep-learning tool on dentists' performances in detecting apical radiolucencies on periapical radiographs.
Article in Dento maxillo facial radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 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
- Sector Classification of Unerupted Maxillary Canines: A Deep Learning-Based Automated Framework Using Panoramic Radiographs.Orthodontics & craniofacial research · 2026Article
- Diagnostic and localization performance of multimodal large language models in the interpretation of dental periapical radiographs.BMC oral health · 2026Article
- Evaluating AI diagnostic accuracy in approximal dental caries detection on bitewing radiographs.Clinical oral investigations · 2026Article
- Evaluating the Test Characteristics of a Prototype for AI-Assisted Radiographic Detection.Dentistry journal · 2026Article
- FDA-Approved AI Solutions in Dental Imaging: A Narrative Review of Applications, Evidence, and Outlook.International dental journal · 2026Review
- Impact of AI-assisted decision support on radiological diagnosis of jawbone lesions.Clinical oral investigations · 2026Article
- Detection of Periapical Lesions Using Artificial Intelligence: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Detection of Dental Anomalies in Digital Panoramic Images Using YOLO: A Next Generation Approach Based on Single Stage Detection Models.Diagnostics (Basel, Switzerland) · 2025Article
- Diagnostic Accuracy of Cone-Beam Computed Tomography (CBCT) in Identifying Periapical Lesions: A Comparative Study with Conventional Radiography.Journal of pharmacy & bioallied sciences · 2025Article
- Diagnostic Efficiency of an Artificial Intelligence-Based Technology in Dental Radiography.Bulletin of experimental biology and medicine · 2025Article
- The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview. Part 1: Fundamentals of AI, and its Contemporary Applications in Dentistry.International dental journal · 2025Review
- Review
- An Assessment of Deep Learning's Impact on General Dentists' Ability to Detect Alveolar Bone Loss in 2D Intraoral Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Comprehensive Insights into Artificial Intelligence for Dental Lesion Detection: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- Machine learning and deep learning models for the diagnosis of apical periodontitis: a scoping review.Clinical oral investigations · 2024Article
- Automatic deep learning detection of overhanging restorations in bitewing radiographs.Dento maxillo facial radiology · 2024Article
- Periapical Lesions in Panoramic Radiography and CBCT Imaging-Assessment of AI's Diagnostic Accuracy.Journal of clinical medicine · 2024Article
- Combining public datasets for automated tooth assessment in panoramic radiographs.BMC oral health · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo determine the efficacy of a deep-learning (DL) tool in assisting dentists in detecting apical radiolucencies on periapical radiographs.
methodsSixty-eight intraoral periapical radiographs with CBCT-proven presence or absence of apical radiolucencies were selected to serve as the testing subset. Eight readers examined the subset, denoted the positions of apical radiolucencies, and used a 5-point confidence scale to score each radiolucency. The same subset was assessed by readers under two conditions: with and without Denti.AI DL tool predictions. For the two sessions, the performance of the readers was compared. The comparison was performed with the alternate free response receiver operating characteristic (AFROC) methodology.
resultsLocalization of lesion accuracy (AFROC-AUC), specificity and sensitivity (by lesion) detection demonstrated improvements in the DL aided session in comparison with the unaided reading session. Subgroup performance analysis revealed an increase in sensitivity for small radiolucencies and in radiolucencies located apical to endodontically treated teeth..
conclusionThe study revealed that the DL technology (Denti.AI) enhances dental professionals' abilities to detect apical radiolucencies on intraoral radiographs. ADVANCES IN KNOWLEDGE: DL tools have the potential to improve diagnostic efficacy of dentists in identifying apical radiolucencies on periapical radiographs.
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