Document Type

Article

Publication Date

12-2026

Publisher

Biomed Central (BMC)

Source Publication

BMC Oral Health

Source ISSN

1472-6831

Abstract

Background

To develop a no-code artificial intelligence (AI) model for the detection of apical radiolucent lesions and to assess how lesion location influences the model’s diagnostic performance.

Methods

312 periapical radiographs were retrospectively collected, each accompanied by a corresponding cone-beam computed tomography (CBCT) scan obtained within six months and a radiology report authored by board-certified oral and maxillofacial radiologists. These reports served as the reference standard, and all findings were cross-verified by the primary investigator through CBCT review. The dataset included 181 images with at least one apical radiolucent lesion and 131 lesion-free controls. Using the no-code AI platform LandingLens (Landing AI LLC, Palo Alto, CA), a diagnostic model was developed. De-identified radiographs were manually annotated with bounding boxes around lesions and automatically divided into training (70%), validation (20%), and testing (10%) subsets. Model performance was evaluated at the tooth level using sensitivity, specificity, accuracy, precision, and F1 score. To investigate the influence of anatomical sites, data were stratified by region and analyzed using chi-square and pairwise chi-square tests (α = 0.05).

Results

The AI model demonstrated robust diagnostic performance, achieving 88.7% sensitivity, 93.0% specificity, 76.6% precision, 92.1% accuracy, and an F1 score of 0.822. Sensitivity was significantly higher in the maxilla (93.1%) compared to the mandible (83.8%; p = 0.049), whereas specificity was greater in the mandible (95.4%) than in the maxilla (90.8%; p = 0.01). No differences in performance were found between anterior and posterior teeth (p > 0.05). Regionally, the highest sensitivity was observed in the anterior maxilla (97.7%), while specificity peaked in the anterior mandible (97.9%; p <  0.05). Other diagnostic metrics did not vary significantly across regions (p > 0.05).

Conclusions

This no-code AI model demonstrated high accuracy in detecting apical radiolucent lesions and revealed that diagnostic performance can vary based on anatomical location. The model's increased sensitivity in the maxilla and higher specificity in the mandible highlight the relevance of regional anatomy in AI-assisted diagnostics. These findings support the potential of no-code AI platforms as accessible tools for building clinically meaningful diagnostic models and underscore the importance of anatomical considerations in their implementation.

Comments

Published version. BMC Oral Health, Vol. 26, No. 52 (2026). DOI. © 2025 The Authors and published by Springer Nature. Used with permission.

This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material.

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