ArticleJournal of biomedical optics2021
Mobile-based oral cancer classification for point-of-care screening.
Article in Journal of biomedical optics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Deep learning in oral cancer- a systematic review.BMC oral health · 2024Pooled it
- Artificial intelligence for classification and detection of oral mucosa lesions on photographs: a systematic review and meta-analysis.Clinical oral investigations · 2024Pooled it
- Diagnostic performance of artificial intelligence in detecting oral potentially malignant disorders and oral cancer using medical diagnostic imaging: a systematic review and meta-analysis.Frontiers in oral health · 2024Pooled it
- Article
- Imaging protoporphyrin IX photoproduct accumulation as a dosimetry reporter for monitoring photodynamic therapy of oral cancer.Journal of biomedical optics · 2025Article
- Enabling point of care optical diagnostics and treatment of oral lesions in resource-limited settings: Preclinical development and evaluation of a low-cost theranostic intraoral device for image-guided photodynamic therapy.Biophotonics discovery · 2025Article
- Review
- Development and Evaluation of an Automated Multimodal Mobile Detection of Oral Cancer Imaging System to Aid in Risk-Based Management of Oral Mucosal Lesions.Cancer prevention research (Philadelphia, Pa.) · 2025Article
- Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo.Biosensors & bioelectronics · 2025Review
- Artificial intelligence for image recognition in diagnosing oral and oropharyngeal cancer and leukoplakia.Scientific reports · 2025Article
- Article
- Attention-guided convolutional network for bias-mitigated and interpretable oral lesion classification.Scientific reports · 2024Article
- Revolutionizing Oral Cancer Screening: New Approaches and Emerging Technologies.Asian Pacific journal of cancer prevention : APJCP · 2023Article
- AI-Assisted Screening of Oral Potentially Malignant Disorders Using Smartphone-Based Photographic Images.Cancers · 2023Article
- Clinical assessment of a low-cost, hand-held, smartphone-attached intraoral imaging probe for 5-aminolevulinic acid photodynamic therapy monitoring and guidance.Journal of biomedical optics · 2023Article
- COV-MobNets: a mobile networks ensemble model for diagnosis of COVID-19 based on chest X-ray images.BMC medical imaging · 2023Article
- Survivorship Care for People With Cancer in the Indo-Pacific: The Imperative to Harness Political Determinants, International Exchange, and Technological Innovation.JCO global oncology · 2023Article
- Compliance with Specialist Referral for Increased Cancer Risk in Low-Resource Settings: In-Person vs. Telehealth Options.Cancers · 2023Article
- Interpretable and Reliable Oral Cancer Classifier with Attention Mechanism and Expert Knowledge Embedding via Attention Map.Cancers · 2023Article
- The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review.Journal of medical Internet research · 2022Article
Corrections and comments
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Authors and funding
23 authors.
Funding
Abstract
significanceOral cancer is among the most common cancers globally, especially in low- and middle-income countries. Early detection is the most effective way to reduce the mortality rate. Deep learning-based cancer image classification models usually need to be hosted on a computing server. However, internet connection is unreliable for screening in low-resource settings.
aimTo develop a mobile-based dual-mode image classification method and customized Android application for point-of-care oral cancer detection. APPROACH: The dataset used in our study was captured among 5025 patients with our customized dual-modality mobile oral screening devices. We trained an efficient network MobileNet with focal loss and converted the model into TensorFlow Lite format. The finalized lite format model is ∼16.3 MB and ideal for smartphone platform operation. We have developed an Android smartphone application in an easy-to-use format that implements the mobile-based dual-modality image classification approach to distinguish oral potentially malignant and malignant images from normal/benign images.
resultsWe investigated the accuracy and running speed on a cost-effective smartphone computing platform. It takes ∼300 ms to process one image pair with the Moto G5 Android smartphone. We tested the proposed method on a standalone dataset and achieved 81% accuracy for distinguishing normal/benign lesions from clinically suspicious lesions, using a gold standard of clinical impression based on the review of images by oral specialists.
conclusionsOur study demonstrates the effectiveness of a mobile-based approach for oral cancer screening in low-resource settings.
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