ArticleCancer prevention research (Philadelphia, Pa.)2025
Development and Evaluation of an Automated Multimodal Mobile Detection of Oral Cancer Imaging System to Aid in Risk-Based Management of Oral Mucosal Lesions.
Article in Cancer prevention research (Philadelphia, Pa.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Multimodal oral cancer detection with embedding-level oversampling of fused image and clinical data.Scientific reports · 2026Article
- Clinical Applications and Diagnostic Performance of Adjunctive Light-Based Optical Technologies in Oral Potentially Malignant Disorders and Squamous Cell Carcinoma: A Systematic Review.Journal of clinical medicine · 2026Review
- Digital Dentistry in Clinical Practice: A Scoping Review of Current Capabilities and Future Directions.International dental journal · 2026Article
- Autofluorescence and deep learning in early disease detection: biological foundations, clinical applications, and future directions.Frontiers in artificial intelligence · 2026Review
- Optimization of a mobile imaging system to aid in evaluating patients with oral lesions in a dental care setting.Biophotonics discovery · 2025Article
- Oral cancer detection via Vanilla CNN optimized by improved artificial protozoa optimizer.Scientific reports · 2025Article
- Emerging Trends in Point-of-Care Technology Development for Oncology in Low- and Middle-Income Countries.JCO global oncology · 2025Review
- Evaluation of Noninvasive Adjuncts for Early Detection of Oral Cancer in Oral Potentially Malignant Disorders and Development of Risk-Based Management Strategies: Protocol for a Prospective Longitudinal Study.JMIR research protocols · 2025Article
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Authors and funding
18 authors.
Funding
Abstract
Oral cancer is a major global health problem. It is commonly diagnosed at an advanced stage, although often preceded by clinically visible oral mucosal lesions, termed oral potentially malignant disorders, which are associated with an increased risk of oral cancer development. There is an unmet clinical need for effective screening tools to assist front-line healthcare providers to determine which patients should be referred to an oral cancer specialist for evaluation. This study reports the development and evaluation of the mobile detection of oral cancer (mDOC) imaging system and an automated algorithm that generates a referral recommendation from mDOC images. mDOC is a smartphone-based autofluorescence and white light imaging tool that captures images of the oral cavity. Data were collected using mDOC from a total of 332 oral sites in a study of 29 healthy volunteers and 120 patients seeking care for an oral mucosal lesion. A multimodal image classification algorithm was developed to generate a recommendation of "refer" or "do not refer" from mDOC images using expert clinical referral decision as the ground truth label. A referral algorithm was developed using cross-validation methods on 80% of the dataset and then retrained and evaluated on a separate holdout test set. Referral decisions generated in the holdout test set had a sensitivity of 93.9% and a specificity of 79.3% with respect to expert clinical referral decisions. The mDOC system has the potential to be utilized in community physicians' and dentists' offices to help identify patients who need further evaluation by an oral cancer specialist. Prevention Relevance: Our research focuses on improving the early detection of oral precancers/cancers in primary dental care settings with a novel mobile platform that can be used by front-line providers to aid in assessing whether a patient has an oral mucosal condition that requires further follow-up with an oral cancer specialist.
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