ReviewDental research journal2026
Evaluating the diagnostic accuracy of neural network models in detecting oral potentially malignant disorders and oral cancer using mobile photographs: An umbrella review.
Review in Dental research journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Oral cancer (OC) and oral potentially malignant disorders (OPMDs) remain major global public health challenges, particularly in low-and middle-income countries. Although early detection substantially improves prognosis, limited healthcare infrastructure restricts timely diagnosis. Artificial intelligence (AI) enabled, mobile phone-based diagnostic systems offer a promising, accessible solution, and multiple systematic reviews have demonstrated their potential. However, uncertainty persists regarding the comparative performance of AI models across diverse real-world settings. Aim: An umbrella review was aimed at evaluating the comparative performance of different AI models in detecting OC and OPMD. Materials and Methods: This research identified six systematic reviews from databases such as Medline (via PubMed), Web of Science, Scopus, and EMBASE through October 2024 which were checked at the title, abstract, and full-text levels. The risk of bias (ROB) was then assessed using the Joanna Briggs Institute's ROB assessment tool. Results: Across included reviews, pooled sensitivity and specificity for AI-based detection ranged from 88% to 92%, with reported diagnostic odds ratios ranging from 114 to 2549, indicating strong discriminatory performance. Deep learning architectures such as EfficientNet and ResNet consistently demonstrated high diagnostic accuracy, while hybrid approaches (e.g., MLSO + SVM) showed promising performance in selected analyses. However, substantial heterogeneity was observed across studies ( Conclusion: Deep learning models like EfficientNet and ResNet are favored in clinical diagnostics for their exceptional performance and adaptability. Hybrid approaches, such as MLSO + SVM, also show great potential by combining the strengths of traditional and modern methods effectively.
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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.