SynthesisBMC oral health2024
Deep learning in oral cancer- a systematic review.
Synthesis in BMC oral health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 3 of them syntheses 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
32 citing papers in PubMed, 3 syntheses or guidelines pooled it, 54 citations in OpenAlex.
- Emerging applications of artificial intelligence for risk stratification in head and neck cancer: a scoping review.Frontiers in oncology · 2026Pooled it
- Diagnosis of Oral Cancer With Deep Learning. A Comparative Test Accuracy Systematic Review.Oral diseases · 2025Pooled it
- Exploring a decade of deep learning in dentistry: A comprehensive mapping review.Clinical oral investigations · 2025Pooled it
- Biofluid based biochemical and clinicopathological profiling for the detection and stratification of oral potentially malignant disorders.Clinical oral investigations · 2026Article
- A narrative review of artificial intelligence in dental imaging: from dataset design to clinical translation.Oral radiology · 2026Review
- Deep Learning in Dental Imaging: Advances, Challenges, and Future.Oral radiology · 2026Review
- Deep learning-based automated detection of oral squamous cell carcinoma in histopathological images: a comparative study of five CNN architectures.Odontology · 2026Article
- Performance of Machine Learning Models for Prognosis Prediction in Oral Cavity Squamous Cell Carcinoma: A Systematic Review.Cancers · 2026Review
- Lessons From Astaxanthin Therapy to Combat Oral Premalignant and Malignant Lesions: A Narrative Review.Health science reports · 2026Article
- Deep learning-based automated masseter muscle area on routine CT stratifies survival in oral cancer.Oral radiology · 2026Article
- Multimodal oral cancer detection with embedding-level oversampling of fused image and clinical data.Scientific reports · 2026Article
- Risk classification of oral potentially malignant disorders (OPMDs) using explainable machine learning through a community-based cross-sectional study in rural India.BMC public health · 2026Article
- Artificial intelligence for diagnosis and triage in oral cancer: a clinician‑centered narrative review.International journal of clinical oncology · 2026Review
- An adaptive multiscale local mesh ternary pattern technique with extensive pre-processing and Grey Wolf optimisation based classifiers for oral cancer image classification.Scientific reports · 2026Article
- AI-Powered Clinical Decision Support in Dentistry: Comparative Evaluation of Large Language Models for Oral Medicine and Periodontal Diagnosis.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- A High Magnifications Histopathology Image Dataset for Oral Squamous Cell Carcinoma Diagnosis and Prognosis.Scientific data · 2026Article
- Evaluating the diagnostic accuracy of neural network models in detecting oral potentially malignant disorders and oral cancer using mobile photographs: An umbrella review.Dental research journal · 2026Review
- From remote screening to precision prevention: responsible multimodal AI for risk prediction and equitable oral healthcare.Frontiers in oral health · 2026Review
- Domain-generalizable oral cancer classification: bridging multi-center histopathological datasets.Frontiers in medicine · 2026Article
- Multimodal large language models for oral lesion diagnosis: a systematic review of diagnostic performance and clinical utility.Frontiers in oral health · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOral cancer is a life-threatening malignancy, which affects the survival rate and quality of life of patients. The aim of this systematic review was to review deep learning (DL) studies in the diagnosis and prognostic prediction of oral cancer.
methodsThis systematic review was conducted following the PRISMA guidelines. Databases (Medline via PubMed, Google Scholar, Scopus) were searched for relevant studies, from January 2000 to June 2023.
resultsFifty-four qualified for inclusion, including diagnostic (n = 51), and prognostic prediction (n = 3). Thirteen studies showed a low risk of biases in all domains, and 40 studies low risk for concerns regarding applicability. The performance of DL models was reported of the accuracy of 85.0-100%, F1-score of 79.31 - 89.0%, Dice coefficient index of 76.0 - 96.3% and Concordance index of 0.78-0.95 for classification, object detection, segmentation, and prognostic prediction, respectively. The pooled diagnostic odds ratios were 2549.08 (95% CI 410.77-4687.39) for classification studies.
conclusionsThe number of DL studies in oral cancer is increasing, with a diverse type of architectures. The reported accuracy showed promising DL performance in studies of oral cancer and appeared to have potential utility in improving informed clinical decision-making of oral cancer.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.