ArticleJournal of cancer research and clinical oncology2022
Non-melanoma skin cancer diagnosis: a comparison between dermoscopic and smartphone images by unified visual and sonification deep learning algorithms.
Article in Journal of cancer research and clinical oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Evaluating the Performance of Artificial Intelligence in Accurately Detecting Skin Cancer: An Umbrella Review of Systematic Reviews and Meta-analyses.American journal of clinical dermatology · 2026Pooled it
- A brain-inspired computational framework for image-based risk assessment.Scientific reports · 2026Article
- Analysis of hybrid CNN models optimized with metaheuristic algorithms for melanoma detection.Scientific reports · 2026Article
- Multimodal artificial intelligence for enhanced skin cancer diagnosis and prognosis.Discover oncology · 2026Review
- Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review.JMIR AI · 2025Review
- Using the power of artificial intelligence to improve the diagnosis and management of nonmelanoma skin cancer.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2025Review
- Automated Prediction of Malignant Melanoma using Two-Stage Convolutional Neural Network.Archives of dermatological research · 2024Article
- From diagnosis to intervention: a review of telemedicine's role in skin cancer care.Archives of dermatological research · 2024Review
- Current smartphone-assisted point-of-care cancer detection: Towards supporting personalized cancer monitoring.Trends in analytical chemistry : TRAC · 2024Article
- Mapping cutaneous field carcinogenesis of nonmelanoma skin cancer using mesoscopic imaging of pro-inflammation cues.Experimental dermatology · 2024Review
- Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Review
- Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-Based Noninvasive Digital System.International journal of biomedical imaging · 2024Article
- SMiT: symmetric mask transformer for disease severity detection.Journal of cancer research and clinical oncology · 2023Article
- Detection and Classification of Melanoma Skin Cancer Using Image Processing Technique.Diagnostics (Basel, Switzerland) · 2023Article
- Review
- Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges.Journal of medical Internet research · 2023Review
- Clinical Application of Artificial Intelligence for Non-melanoma Skin Cancer.Current treatment options in oncology · 2023Review
- A novel approach toward skin cancer classification through fused deep features and neutrosophic environment.Frontiers in public health · 2023Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeNon-melanoma skin cancer (NMSC) is the most frequent keratinocyte-origin skin tumor. It is confirmed that dermoscopy of NMSC confers a diagnostic advantage as compared to visual face-to-face assessment. COVID-19 restrictions diagnostics by telemedicine photos, which are analogous to visual inspection, displaced part of in-person visits. This study evaluated by a dual convolutional neural network (CNN) performance metrics in dermoscopic (DI) versus smartphone-captured images (SI) and tested if artificial intelligence narrows the proclaimed gap in diagnostic accuracy.
methodsA CNN that receives a raw image and predicts malignancy, overlaid by a second independent CNN which processes a sonification (image-to-sound mapping) of the original image, were combined into a unified malignancy classifier. All images were histopathology-verified in a comparison between NMSC and benign skin lesions excised as suspected NMSCs. Study criteria outcomes were sensitivity and specificity for the unified output.
resultsImages acquired by DI (n = 132 NMSC, n = 33 benign) were compared to SI (n = 170 NMSC, n = 28 benign). DI and SI analysis metrics resulted in an area under the curve (AUC) of the receiver operator characteristic curve of 0.911 and 0.821, respectively. Accuracy was increased by DI (0.88; CI 81.9-92.4) as compared to SI (0.75; CI 68.1-80.6, p < 0.005). Sensitivity of DI was higher than SI (95.3%, CI 90.4-98.3 vs 75.3%, CI 68.1-81.6, p < 0.001), but not specificity (p = NS).
conclusionTelemedicine use of smartphone images might result in a substantial decrease in diagnostic performance as compared to dermoscopy, which needs to be considered by both healthcare providers and patients.
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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.