ArticleCancers2023
AI-Assisted Screening of Oral Potentially Malignant Disorders Using Smartphone-Based Photographic Images.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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
32 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 citations in OpenAlex.
- Detection of oral potentially malignant disorders and oral cancer with white light through deep learning: a systematic review and meta-analysis.Clinical oral investigations · 2026Pooled it
- Article
- Automatic Oral Cancer Detection Using Improved Honey Badger Algorithm-Based Feature Selection.Diagnostics (Basel, Switzerland) · 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
- Comment on "Diagnostic Performance of Multimodal Large Language Models in the Analysis of Oral Pathology".Oral diseases · 2026Article
- A Smartphone-based Comprehensive Dataset of Annotated Oral Cavity Images for Enhanced Oral Disease Diagnosis.Scientific data · 2026Article
- MeMoSA dataset: A multi-country collection of over 30,000 oral mucosa images with clinically labelled lesions.Scientific data · 2026Article
- Application of artificial intelligence in oral potentially malignant disorders: current opinions and future barriers.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Interdisciplinary Strategies for Improving Oral Health in Older Adults: A Comprehensive Review.Geriatrics (Basel, Switzerland) · 2026Review
- 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
- Diagnostic Efficiency of Artificial Intelligence Integrated Intraoral Mobile Photographs in Identification of Oral Potentially Malignant Disorders: An Umbrella Review.International journal of dentistry · 2026Review
- Artificial intelligence and its application in early oral cancer screening: a systematic review.Frontiers in oncology · 2026Review
- Artificial intelligence in the study of oral lichen planus characteristics: a review.BDJ open · 2025Review
- Diagnostic Performance of Multimodal Large Language Models in the Analysis of Oral Pathology.Oral diseases · 2025Article
- ChatGPT in Oral Pathology: Bright Promise or Diagnostic Mirage.Medicina (Kaunas, Lithuania) · 2025Article
- Application of artificial intelligence in medical imaging for tumor diagnosis and treatment: a comprehensive approach.Discover oncology · 2025Review
- Automated classification of oral potentially malignant disorders and oral squamous cell carcinoma using a convolutional neural network framework: a cross-sectional study.Lancet regional health. Americas · 2025Article
- Screening of oral potentially malignant disorders and oral cancer using deep learning models.Scientific reports · 2025Article
- Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo.Biosensors & bioelectronics · 2025Review
- Recurrent and Metastatic Head and Neck Cancer: Mechanisms of Treatment Failure, Treatment Paradigms, and New Horizons.Cancers · 2025Review
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 at 4 institutions in 1 country.
Funding
Abstract
The prevalence of oral potentially malignant disorders (OPMDs) and oral cancer is surging in low- and middle-income countries. A lack of resources for population screening in remote locations delays the detection of these lesions in the early stages and contributes to higher mortality and a poor quality of life. Digital imaging and artificial intelligence (AI) are promising tools for cancer screening. This study aimed to evaluate the utility of AI-based techniques for detecting OPMDs in the Indian population using photographic images of oral cavities captured using a smartphone. A dataset comprising 1120 suspicious and 1058 non-suspicious oral cavity photographic images taken by trained front-line healthcare workers (FHWs) was used for evaluating the performance of different deep learning models based on convolution (DenseNets) and Transformer (Swin) architectures. The best-performing model was also tested on an additional independent test set comprising 440 photographic images taken by untrained FHWs (set I). DenseNet201 and Swin Transformer (base) models show high classification performance with an F1-score of 0.84 (CI 0.79-0.89) and 0.83 (CI 0.78-0.88) on the internal test set, respectively. However, the performance of models decreases on test set I, which has considerable variation in the image quality, with the best F1-score of 0.73 (CI 0.67-0.78) obtained using DenseNet201. The proposed AI model has the potential to identify suspicious and non-suspicious oral lesions using photographic images. This simplified image-based AI solution can assist in screening, early detection, and prompt referral for OPMDs.
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.