ArticleJournal of periodontal & implant science2018
Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm.
Article in Journal of periodontal & implant science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 189 papers, 9 of them syntheses that pooled it.
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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.
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Who cites it
189 citing papers in PubMed, 9 syntheses or guidelines pooled it.
- Diagnostic accuracy of artificial intelligence-based deep learning models in detecting furcation involvement: A systematic review and meta-analysis.Journal of periodontology · 2026Pooled it
- Transformer-based models in dentistry: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Exploring a decade of deep learning in dentistry: A comprehensive mapping review.Clinical oral investigations · 2025Pooled it
- Detection of periodontal bone loss and periodontitis from 2D dental radiographs via machine learning and deep learning: systematic review employing APPRAISE-AI and meta-analysis.Dento maxillo facial radiology · 2025Pooled it
- Deep learning for classifying the stages of periodontitis on dental images: a systematic review and meta-analysis.BMC oral health · 2023Pooled it
- Pooled it
- The Use and Performance of Artificial Intelligence in Prosthodontics: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review.Dento maxillo facial radiology · 2020Pooled it
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- Factors influencing the response to periodontal therapy in patients with diabetes: post hoc analysis of a randomized clinical trial using machine learning.Journal of applied oral science : revista FOB · 2025Trial
- Portable Deep Learning-Assisted Device for Automated Interincisal Opening Assessment and Oral Submucous Fibrosis Lesion Identification: A Translational Clinical Study.Journal of imaging informatics in medicine · 2026Article
- AI-informed healthcare pedagogy: a review of the literature and application to pedagogical practice informed by case studies from dental education.British dental journal · 2026Review
- Artificial Intelligence in Periodontology: From Automated Diagnosis to Prediction and Clinical Decision Support-A Narrative Review.Dentistry journal · 2026Review
- Deep Learning for the Assessment of Alveolar Bone Loss on Intraoral Radiographs: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Automated prediction of hand-wrist maturation stage from lateral cephalometric and panoramic radiographs using a deep learning approach.European journal of orthodontics · 2026Article
- Reframing Periodontitis: A Concise Review of Shifting Paradigms and Contemporary Clinical Practice.International dental journal · 2026Review
- Performance assessment of five artificial intelligence-based algorithms for automated tooth segmentation and labeling on intraoral scans.Journal of prosthodontics : official journal of the American College of Prosthodontists · 2026Article
- YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.Journal of imaging · 2026Article
- Large language models in implant dentistry: a scoping review of applications, performance, and limitations.BMC oral health · 2026Article
- MultiDentNet: a unified deep learning framework for multi-class dental condition screening and preliminary oral lesion triage.Scientific reports · 2026Article
129 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT).
methodsCombining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python.
resultsThe periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars.
conclusionsWe demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.
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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.