ArticleScientific reports2022
Field validation of deep learning based Point-of-Care device for early detection of oral malignant and potentially malignant disorders.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 46 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
- Role of artificial intelligence in head and neck surgery - A narrative review.Turkish journal of surgery · 2026Article
- Effectiveness of a capacity-building training for knowledge and practices of tobacco control and oral cancer prevention: a pre-post study from rural northern India.BMC oral health · 2026Article
- Oral Cancer Diagnosis Using an Optimized InceptionV3 Model Powered by the Aquila Metaheuristic Algorithm.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- Quantitative Autofluorescence Imaging of Oral Mucosa and Lesions: A Proof-of-Concept Study.Diagnostics (Basel, Switzerland) · 2026Article
- Digital Dentistry in Clinical Practice: A Scoping Review of Current Capabilities and Future Directions.International dental journal · 2026Article
- Optimization of a mobile imaging system to aid in evaluating patients with oral lesions in a dental care setting.Biophotonics discovery · 2025Article
- Targeted Screening Strategies for Head and Neck Cancer: A Global Review of Evidence, Technologies, and Cost-Effectiveness.Diagnostics (Basel, Switzerland) · 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
- Screening of oral potentially malignant disorders and oral cancer using deep learning models.Scientific reports · 2025Article
- 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 in Oral Cancer: A Comprehensive Scoping Review of Diagnostic and Prognostic Applications.Diagnostics (Basel, Switzerland) · 2025Review
- Formative research and design for a mobile health platform for oral cancer screening and detection (OC-DETECT)-a mixed methods study.Frontiers in digital health · 2025Article
- Article
- Holistic Approach for the Early Detection of Oral Cancer: A Comprehensive Training Module.Journal of maxillofacial and oral surgery · 2024Article
- An Update on the Use of Artificial Intelligence in Digital Pathology for Oral Epithelial Dysplasia Research.Head and neck pathology · 2024Review
- Classification of Mobile-Based Oral Cancer Images Using the Vision Transformer and the Swin Transformer.Cancers · 2024Article
- Use of digital strategies in the diagnosis of oral squamous cell carcinoma: a scoping review.PeerJ · 2024Article
- Optical imaging for screening and early cancer diagnosis in low-resource settings.Nature reviews bioengineering · 2024Article
Corrections and comments
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Authors and funding
27 authors at 9 institutions in 2 countries.
Funding
Abstract
Early detection of oral cancer in low-resource settings necessitates a Point-of-Care screening tool that empowers Frontline-Health-Workers (FHW). This study was conducted to validate the accuracy of Convolutional-Neural-Network (CNN) enabled m(mobile)-Health device deployed with FHWs for delineation of suspicious oral lesions (malignant/potentially-malignant disorders). The effectiveness of the device was tested in tertiary-care hospitals and low-resource settings in India. The subjects were screened independently, either by FHWs alone or along with specialists. All the subjects were also remotely evaluated by oral cancer specialist/s. The program screened 5025 subjects (Images: 32,128) with 95% (n = 4728) having telediagnosis. Among the 16% (n = 752) assessed by onsite specialists, 20% (n = 102) underwent biopsy. Simple and complex CNN were integrated into the mobile phone and cloud respectively. The onsite specialist diagnosis showed a high sensitivity (94%), when compared to histology, while telediagnosis showed high accuracy in comparison with onsite specialists (sensitivity: 95%; specificity: 84%). FHWs, however, when compared with telediagnosis, identified suspicious lesions with less sensitivity (60%). Phone integrated, CNN (MobileNet) accurately delineated lesions (n = 1416; sensitivity: 82%) and Cloud-based CNN (VGG19) had higher accuracy (sensitivity: 87%) with tele-diagnosis as reference standard. The results of the study suggest that an automated mHealth-enabled, dual-image system is a useful triaging tool and empowers FHWs for oral cancer screening in low-resource settings.
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Registered trials
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