SynthesisOral diseases2025
Automated Detection of Oral Malignant Lesions Using Deep Learning: Scoping Review and Meta-Analysis.
Synthesis in Oral diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnosis of Oral Cancer With Deep Learning. A Comparative Test Accuracy Systematic Review.Oral diseases · 2025Pooled it
- The Feasibility of One-Stage Instance Segmentation for Detecting Oral Potentially Malignant Disorders in White-Light Clinical Photographs: A Proof-of-Concept Study.Dentistry journal · 2026Article
- Multimodal large language models for oral lesion diagnosis: a systematic review of diagnostic performance and clinical utility.Frontiers in oral health · 2026Review
- Quantitative assessment of biomechanical changes in oral lesions at different cancerous stages using optical coherence elastography.Quantitative imaging in medicine and surgery · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveOral diseases, specifically malignant lesions, are serious global health concerns requiring early diagnosis for effective treatment. In recent years, deep learning (DL) has emerged as a powerful tool for the automated detection and classification of oral lesions. This research, by conducting a scoping review and meta-analysis, aims to provide an overview of the progress and achievements in the field of automated detection of oral lesions using DL. MATERIALS AND
methodsA scoping review was conducted to identify relevant studies published in the last 5 years (2018-2023). A comprehensive search was conducted using several electronic databases, including PubMed, Web of Science, and Scopus. Two reviewers independently assessed the studies for eligibility and extracted data using a standardized form, and a meta-analysis was conducted to synthesize the findings.
resultsFourteen studies utilizing various DL algorithms were identified and included for the detection and classification of oral lesions from clinical images. Among these, three were included in the meta-analysis. The estimated pooled sensitivity and specificity were 0.86 (95% confidence interval [CI] = 0.80-0.91) and 0.67 (95% CI = 0.58-0.75), respectively.
conclusionsThe results of meta-analysis indicate that DL algorithms improve the diagnosis of oral lesions. Future research should develop validated algorithms for automated diagnosis.
trial registrationOpen Science Framework (https://osf.io/4n8sm).
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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.