ArticleNPJ digital medicine2025
Next-generation AI framework for comprehensive oral leukoplakia evaluation and management.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed.
- Malignant Transformation Risk in Patients With Oral Leukoplakia and Type 2 Diabetes Mellitus.International dental journal · 2026Article
- Image-Based Artificial Intelligence for Predicting Malignant Transformation of Oral Potentially Malignant Disorders: A Scoping Review.Journal of clinical medicine · 2026Review
- Metabolomic analysis for diagnosis of oral squamous cell carcinoma using machine learning.Discover oncology · 2026Article
- Biopsy for Suspicious Oral Lesions: A Review From the American Head and Neck Society-Cancer Prevention Service.Head & neck · 2026Review
- Heterogeneous mechanisms, risk stratification, and translational advances in the malignant transformation of oral potentially malignant disorders to oral squamous cell carcinoma.Frontiers in molecular biosciences · 2026Review
- c-Met-targeted NIR-II imaging for precision management of oral squamous cell carcinoma and premalignant lesions.Theranostics · 2026Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Oral potentially malignant disorder poses a significant risk of malignant transformation, particularly in cases with epithelial dysplasia (OED). Current OED assessment methods are invasive and lack reliable decision-support tools for cancer risk evaluation and follow-up optimization. This study developed and validated OMMT-PredNet, a fully automated multimodal deep learning framework requiring no manual ROI annotation, for non-invasive OED identification and time-dependent cancer risk prediction. Utilizing data from 649 histopathologically confirmed leukoplakia cases across multiple institutions (2003-2024), including 598 cases in the primary cohort and 51 in the external validation set, the model integrated paired high-resolution clinical images and medical records. OMMT-PredNet achieved an AUC of 0.9592 (95% CI: 0.9491-0.9693) for cancer risk prediction and 0.9219 (95% CI: 0.9088-0.9349) for OED identification, with high specificity (MT: 0.9490; OED: 0.9182) and precision (MT: 0.9442; OED: 0.9303). Calibration and decision curve analyses confirmed clinical applicability, while external validation demonstrated robustness. This multidimensional model effectively predicts OED and cancer risk, highlighting its global applicability in enhancing oral cancer screening and improving patient outcomes.
Identifiers
What Socratic holds
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 Socratic graph.