Evaluating the diagnostic accuracy of neural network models in detecting oral potentially malignant disorders and oral cancer using mobile photographs: An umbrella review

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 (I2 often >85%), reflecting variability in populations, image acquisition
protocols, and model architectures.
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.
Key Words: Artificial intelligence, deep learning, early diagnosis, intraoral photography, mobile
health, oral cancer

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P. D. Madan Kumar: Pubmed,Google Scholar