{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T22:21:20Z","timestamp":1793830880783,"version":"4.6.1"},"reference-count":68,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Deep convolutional neural networks (DCNNs) are increasingly used in computer-aided dental diagnostics. However, the relative diagnostic performance of commonly applied architectures, particularly Faster R-CNN and Mask R-CNN, has not been systematically synthesized across imaging modalities. This systematic review and meta-analysis compared the diagnostic accuracy of Faster R-CNN and Mask R-CNN for dental caries detection using radiographic and photographic images. Methods: PubMed (MEDLINE), EMBASE, Web of Science, and Scopus were systematically searched for studies published up to 15 June 2025. Studies applying Faster R-CNN and\/or Mask R-CNN to dental caries detection were included. Binary diagnostic data were extracted, and pooled sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were estimated using a bivariate random-effects model. Study quality was assessed with QUADAS-AI, and radiomics-based radiographic studies were additionally evaluated using the Radiomics Quality Score (RQS). The protocol was registered in PROSPERO (CRD420251074443). Results: Seventeen studies met the inclusion criteria. Across all imaging modalities, Mask R-CNN showed significantly higher pooled sensitivity (85.6% vs. 71.7%, p = 0.0244), specificity (94.2% vs. 81.4%, p = 0.00089), and AUC (0.95 vs. 0.84, p = 0.0053) than Faster R-CNN. In radiographic images, Mask R-CNN consistently outperformed Faster R-CNN in sensitivity (86.3% vs. 67.2%, p = 0.0497), specificity (96.5% vs. 85.0%, p = 0.00105), and AUC (0.97 vs. 0.86, p = 0.0067). In photographic images, Mask R-CNN achieved a higher AUC (0.91 vs. 0.83, p = 0.048), whereas differences in pooled sensitivity (83.5% vs. 77.3%, p = 0.435) and specificity (86.0% vs. 75.1%, p = 0.156) were not statistically significant. Conclusions: Faster R-CNN and Mask R-CNN both show potential for dental caries detection, but current evidence is limited by substantial heterogeneity, predominantly retrospective designs, and variability in imaging and labeling. Across the included studies, Mask R-CNN showed higher pooled performance estimates than Faster R-CNN, with the clearest differences in radiographic applications; however, this comparison is indirect and should be considered suggestive rather than definitive given study-level heterogeneity and uncertainty in the reference standard in a sizable proportion of studies. Prospective, multi-center studies with standardized imaging protocols, rigorous annotation, and independent external validation are required to support reliable clinical implementation.<\/jats:p>","DOI":"10.3390\/diagnostics16050731","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T15:22:21Z","timestamp":1772551341000},"page":"731","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Deep Learning-Based Dental Caries Diagnosis: A Modality-Stratified Systematic Review and Meta-Analysis of Faster R-CNN and Mask R-CNN"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5305-2266","authenticated-orcid":false,"given":"Quang Tuan","family":"Lam","sequence":"first","affiliation":[{"name":"School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei 11031, Taiwan"},{"name":"AIBioMed Research Group, Taipei Medical University, Taipei 11031, Taiwan"},{"name":"Faculty of Odonto-Stomatology, Da Nang University of Medical Technology and Pharmacy, Da Nang 550000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7728-1539","authenticated-orcid":false,"given":"Minh Huu Nhat","family":"Le","sequence":"additional","affiliation":[{"name":"AIBioMed Research Group, Taipei Medical University, Taipei 11031, Taiwan"},{"name":"International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5176-4102","authenticated-orcid":false,"given":"Fang-Yu","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Dental Technology, College of Oral Medicine, Taipei Medical University, Taipei 11031, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4896-7926","authenticated-orcid":false,"given":"Nguyen Quoc Khanh","family":"Le","sequence":"additional","affiliation":[{"name":"AIBioMed Research Group, Taipei Medical University, Taipei 11031, Taiwan"},{"name":"Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan"},{"name":"Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 11031, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8451-0280","authenticated-orcid":false,"given":"I-Ta","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei 11031, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7614","DOI":"10.1038\/s41467-022-35337-8","article-title":"An umbrella review of the evidence linking oral health and systemic noncommunicable diseases","volume":"13","author":"Botelho","year":"2022","journal-title":"Nat. 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