ArticleDento maxillo facial radiology2019
A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography.
Article in Dento maxillo facial radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05340140 (The Accuracy of Computer Aided Detection of Second Mesio-buccal Canal of Maxillary First Molars on CBCT Images Using Deep Learning Model), which is not on this map. Cited by 129 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
The Accuracy of Computer Aided Detection of Second Mesio-buccal Canal of Maxillary First Molars on CBCT Images Using Deep Learning Model (Artificial Intelligence): Diagnostic Accuracy Study
Who cites it
129 citing papers in PubMed, 3 syntheses or guidelines pooled it, 295 citations in OpenAlex.
- Exploring a decade of deep learning in dentistry: A comprehensive mapping review.Clinical oral investigations · 2025Pooled it
- Panoramic imaging errors in machine learning model development: a systematic review.Dento maxillo facial radiology · 2024Pooled it
- The Use and Performance of Artificial Intelligence in Prosthodontics: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Dermoscopic Image Classification of Pigmented Nevus under Deep Learning and the Correlation with Pathological Features.Computational and mathematical methods in medicine · 2022Trial
- Comparative Benchmark of Seven Large Language Models for Traumatic Dental Injury Knowledge.European journal of dentistry · 2026Article
- Review
- Deep learning-enhanced detection of dental restorations and orthodontic appliances in panoramic radiographs using a clinically annotated dataset.Acta odontologica Scandinavica · 2026Article
- Artificial Intelligence in Endodontics-A Bibliometric Analysis.Australian endodontic journal : the journal of the Australian Society of Endodontology Inc · 2026Review
- Exploring the landscape of artificial intelligence in dental and maxillofacial radiology: a bibliometric analysis of studies and trends.Annals of medicine and surgery (2012) · 2026Review
- Effectiveness of Artificial Intelligence in the Automatic Diagnosis of Lesions Detected in Panoramic Radiographs.Journal of imaging informatics in medicine · 2026Article
- Weighted knowledge distillation for semi-supervised segmentation of maxillary sinus in panoramic X-ray images.Scientific reports · 2026Article
- Three-dimensional automatic segmentation of root canals with focus on the second mesiobuccal canal using nnU-Netv2 on CBCT images: deep learning approach.BMC oral health · 2026Article
- Automated Identification of Accessory Mental Foramen Using Cone-Beam Computed Tomography and Convolutional Neural Networks.International dental journal · 2026Article
- Performance of artificial intelligence models designed as adjuncts for determining working length and apical landmark assessment in endodontic procedures: a systematic review.Frontiers in dental medicine · 2026Review
- How artificial intelligence is reshaping citation impact in oral and maxillofacial radiology journals: an 8-year analysis with editorial and clinical implications (JCR 2017-2024).Frontiers in research metrics and analytics · 2026Article
- Root growth and function in New Zealand pasture systems: a perspective on research needs, methods, and system integration.Frontiers in plant science · 2026Article
- Application of artificial intelligence in oral health management: challenges and opportunities.Frontiers in medicine · 2026Review
- Application of machine learning in predicting of MB2 canal in permanent maxillary first and second molars: A CBCT study.PloS one · 2026Article
- Computational Insights into Root Canal Treatment: A Survey of Selected Methods in Imaging, Segmentation, Morphological Analysis, and Clinical Management.Dentistry journal · 2025Review
- Artificial Intelligence in The Diagnosis, Treatment, and Prognostication in Endodontics: A Comprehensive Literature Review.European endodontic journal · 2025Review
69 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectives: The distal root of the mandibular first molar occasionally has an extra root, which can directly affect the outcome of endodontic therapy. In this study, we examined the diagnostic performance of a deep learning system for classification of the root morphology of mandibular first molars on panoramic radiographs. Dental cone-beam CT (CBCT) was used as the gold standard.
methods: CBCT images and panoramic radiographs of 760 mandibular first molars from 400 patients who had not undergone root canal treatments were analyzed. Distal roots were examined on CBCT images to determine the presence of a single or extra root. Image patches of the roots were segmented from panoramic radiographs and applied to a deep learning system, and its diagnostic performance in the classification of root morphplogy was examined.
results: Extra roots were observed in 21.4% of distal roots on CBCT images. The deep learning system had diagnostic accuracy of 86.9% for the determination of whether distal roots were single or had extra roots.
conclusions: The deep learning system showed high accuracy in the differential diagnosis of a single or extra root in the distal roots of mandibular first molars.
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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.