ArticleJournal of dental research2022
Artificial Intelligence for Caries Detection: Value of Data and Information.
Article in Journal of dental research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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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.
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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
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Economic evaluation of AI-based oral disease screening: a systematic review.Frontiers in medical technology · 2026Pooled it
- Deep Learning in Dental Imaging: Advances, Challenges, and Future.Oral radiology · 2026Review
- Artificial Intelligence in Pediatric Dentistry: Current Applications, Emerging Trends, and Future Directions.Dentistry journal · 2026Review
- Modulating Oral Microbiota to Prevent Dental Caries: A Microbial Ecology Approach.Dentistry journal · 2026Review
- Deep Learning-Based Dental Caries Diagnosis on Panoramic Radiographies: Performance of YOLOv8 Versus Human Observers.Diagnostics (Basel, Switzerland) · 2026Article
- Generative Artificial Intelligence: Applications and Future Prospects in Dentistry.International dental journal · 2026Review
- Artificial intelligence for dental caries diagnosis: translating algorithms to clinical practice.Frontiers in medicine · 2026Review
- Discounting Practices in Cost-Effectiveness Analyses in Dentistry: A Systematic Review of Studies Published After 2020.ClinicoEconomics and outcomes research : CEOR · 2026Review
- The value for money of artificial intelligence-empowered precision medicine: a systematic review and regression analysis.NPJ digital medicine · 2025Article
- Article
- Explainable CNN-Radiomics Fusion and Ensemble Learning for Multimodal Lesion Classification in Dental Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Handling uncertainty in cost-effectiveness analysis in dental medicine: a systematic review with a focus on affordability and risk-aversion.Cost effectiveness and resource allocation : C/E · 2025Review
- Assessment of the Diagnostic Accuracy of Artificial Intelligence Software in Identifying Common Periodontal and Restorative Dental Conditions (Marginal Bone Loss, Periapical Lesion, Crown, Restoration, Dental Caries) in Intraoral Periapical Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Development and Comparative Evaluation of a Reinstructed GPT-4o Model Specialized in Periodontology.Journal of clinical periodontology · 2025Article
- Global Research Trends in the Detection and Diagnosis of Dental Caries: A Bibliometric Analysis.International dental journal · 2025Review
- RadImageNet and ImageNet as Datasets for Transfer Learning in the Assessment of Dental Radiographs: A Comparative Study.Journal of imaging informatics in medicine · 2025Article
- Automated classification of midpalatal suture maturation using 2D convolutional neural networks on CBCT scans.Frontiers in dental medicine · 2025Article
- Deep Learning-Based Algorithm for Staging Secondary Caries in Bitewings.Caries research · 2025Article
- Artificial Intelligence in Dentistry: A Descriptive Review.Bioengineering (Basel, Switzerland) · 2024Review
- Development of an oral cancer detection system through deep learning.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
If increasing practitioners' diagnostic accuracy, medical artificial intelligence (AI) may lead to better treatment decisions at lower costs, while uncertainty remains around the resulting cost-effectiveness. In the present study, we assessed how enlarging the data set used for training an AI for caries detection on bitewings affects cost-effectiveness and also determined the value of information by reducing the uncertainty around other input parameters (namely, the costs of AI and the population's caries risk profile). We employed a convolutional neural network and trained it on 10%, 25%, 50%, or 100% of a labeled data set containing 29,011 teeth without and 19,760 teeth with caries lesions stemming from bitewing radiographs. We employed an established health economic modeling and analytical framework to quantify cost-effectiveness and value of information. We adopted a mixed public-private payer perspective in German health care; the health outcome was tooth retention years. A Markov model, allowing to follow posterior teeth over the lifetime of an initially 12-y-old individual, and Monte Carlo microsimulations were employed. With an increasing amount of data used to train the AI sensitivity and specificity increased nonlinearly, increasing the data set from 10% to 25% had the largest impact on accuracy and, consequently, cost-effectiveness. In the base-case scenario, AI was more effective (tooth retention for a mean [2.5%-97.5%] 62.8 [59.2-65.5] y) and less costly (378 [284-499] euros) than dentists without AI (60.4 [55.8-64.4] y; 419 [270-593] euros), with considerable uncertainty. The economic value of reducing the uncertainty around AI's accuracy or costs was limited, while information on the population's risk profile was more relevant. When developing dental AI, informed choices about the data set size may be recommended, and research toward individualized application of AI for caries detection seems warranted to optimize cost-effectiveness.
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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.