ReviewJournal of pharmacy & bioallied sciences2025
Radiomics and Machine Learning in Early Detection of Oral Potentially Malignant Disorders.
Review in Journal of pharmacy & bioallied sciences, 2025. 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Oral potentially malignant disorders (OPMDs) are a diverse group of conditions that carry a risk of transformation into oral squamous cell carcinoma (OSCC). Early detection is vital to improving prognosis and survival. Recent advancements in radiomics and machine learning (ML) have shown promise in enhancing the diagnostic accuracy and early detection of OPMDs. Radiomics enables the extraction of quantitative data from medical images, while ML algorithms analyze patterns and correlations for predictive modeling. This review highlights the application of radiomics and ML in early OPMD detection, focusing on imaging modalities, data processing techniques, current research findings, and future challenges.
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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.