ArticlePloS one2018
Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers, 8 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
89 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- 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
- Automated Detection of Oral Malignant Lesions Using Deep Learning: Scoping Review and Meta-Analysis.Oral diseases · 2025Pooled 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
- Machine learning in point-of-care automated classification of oral potentially malignant and malignant disorders: a systematic review and meta-analysis.Scientific reports · 2022Pooled it
- Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review.JMIR mHealth and uHealth · 2022Pooled it
- Clinical assessment for the detection of oral cavity cancer and potentially malignant disorders in apparently healthy adults.The Cochrane database of systematic reviews · 2021Pooled it
- Diagnostic tests for oral cancer and potentially malignant disorders in patients presenting with clinically evident lesions.The Cochrane database of systematic reviews · 2021Pooled it
- Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review.Frontiers in oral health · 2021Pooled it
- Clinical evaluation of a mobile, low-cost system for fluorescence guided photodynamic therapy of early oral cancer in India.Photodiagnosis and photodynamic therapy · 2022Trial
- Article
- A Pilot Study of the Diagnosis of Oral Cancer Through the Development of an AI Application.Dentistry journal · 2026Article
- Evaluation of the Detectability of Oral Potentially Malignant Diseases with a Deep Learning Approach: A Retrospective Pilot Study.Journal of imaging informatics in medicine · 2026Article
- Quantitative Autofluorescence Imaging of Oral Mucosa and Lesions: A Proof-of-Concept Study.Diagnostics (Basel, Switzerland) · 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
- Article
- Imaging protoporphyrin IX photoproduct accumulation as a dosimetry reporter for monitoring photodynamic therapy of oral cancer.Journal of biomedical optics · 2025Article
- AI in Early Diagnosis and Prevention of Oral Cancer: A Systematic Review and Meta-analysis.Journal of pharmacy & bioallied sciences · 2025Review
- First Decade of the National Cancer Institute's Affordable Cancer Technologies Program: Accelerating Translational Technology Research and Development for Cancer Globally.JCO global oncology · 2025Review
- 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
- Machine reading and recovery of colors for hemoglobin-related bioassays and bioimaging.Science advances · 2025Article
29 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Oral cancer is a growing health issue in a number of low- and middle-income countries (LMIC), particularly in South and Southeast Asia. The described dual-modality, dual-view, point-of-care oral cancer screening device, developed for high-risk populations in remote regions with limited infrastructure, implements autofluorescence imaging (AFI) and white light imaging (WLI) on a smartphone platform, enabling early detection of pre-cancerous and cancerous lesions in the oral cavity with the potential to reduce morbidity, mortality, and overall healthcare costs. Using a custom Android application, this device synchronizes external light-emitting diode (LED) illumination and image capture for AFI and WLI. Data is uploaded to a cloud server for diagnosis by a remote specialist through a web app, with the ability to transmit triage instructions back to the device and patient. Finally, with the on-site specialist's diagnosis as the gold-standard, the remote specialist and a convolutional neural network (CNN) were able to classify 170 image pairs into 'suspicious' and 'not suspicious' with sensitivities, specificities, positive predictive values, and negative predictive values ranging from 81.25% to 94.94%.
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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.