ArticleContemporary clinical dentistry
Evaluating the Accuracy of Deep Learning Models and Dental Postgraduate Students in Measuring Working Length on Intraoral Periapical X-rays: An
Article in Contemporary clinical dentistry. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for Working Length Determination in Endodontics: A Systematic Review and Meta-Analysis.Australian endodontic journal : the journal of the Australian Society of Endodontology Inc · 2026Pooled it
- Artificial Intelligence in Endodontic Education: A Systematic Review with Frequentist and Bayesian Meta-Analysis of Student-Based Evidence.Dentistry journal · 2025Review
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Authors and funding
4 authors.
Funding
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Abstract
Background: The integration of artificial intelligence in dentistry has seen remarkable advancements, especially in diagnostic imaging. This study evaluates and compares the accuracy of deep learning models with that of dental postgraduate students in determining working length on intraoral periapical radiographs. Materials and Methods: One hundred anonymized radiographs of single-rooted teeth with files at working length were obtained. The images were preprocessed and used to train a deep learning model. Five dental postgraduates visually estimated the working length after receiving training. Pixel counting in image processing software provided the gold standard measurement. Accuracy comparisons were performed using a Results: The deep learning model demonstrated significantly higher accuracy (85%) compared to human estimations (mean accuracy 75.4%). The Conclusion: Deep learning models show great potential in enhancing precision and reliability for working length determination in endodontics. With further refinement, these models can effectively complement human expertise in dental radiographic interpretation.
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