ArticleNature medicine2023
A deep-learning algorithm to classify skin lesions from mpox virus infection.
Article in Nature medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it, 103 citations in OpenAlex.
- Use of AI in Identification of Sexually Transmitted Infections and Anogenital Dermatoses: A Systematic Review and Meta-Analysis.JAMA network open · 2025Pooled it
- Demographic Reporting and the Absence of Hispanic/Latino Representation in Dermatology Artificial Intelligence: A Scoping Review.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2026Article
- Mpox and the one health approach.Dialogues in health · 2026Review
- Artificial intelligence in infection surveillance: Data integration, applications and future directions.Biomedical journal · 2026Review
- AI-driven diagnosis of mpox using deep learning models.PloS one · 2026Article
- Severe viral rash progression prediction and surveillance early warning based on local-global attention and temporal convolutional network.Frontiers in public health · 2026Article
- Deep Learning-Based Mpox Skin Lesion Detection and Real-Time Monitoring in a Smart Healthcare System.Diagnostics (Basel, Switzerland) · 2025Article
- Enhanced detection of Mpox using federated learning with hybrid ResNet-ViT and adaptive attention mechanisms.Scientific reports · 2025Article
- Breaking Diagnostic Barriers: Vision Transformers Redefine Monkeypox Detection.Diagnostics (Basel, Switzerland) · 2025Article
- The dynamic case-based learning model: conducive to enhancing the practical learning effectiveness of dermatology for medical students.BMC medical education · 2025Article
- Unveiling Immune Response Mechanisms in Mpox Infection Through Machine Learning Analysis of Time Series Gene Expression Data.Life (Basel, Switzerland) · 2025Article
- DEEP AUTOMATIC ALIGNMENT OF MPOX DERMATOLOGICAL HAND PHOTOGRAPHY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- Explainable AI for Symptom-Based Detection of Monkeypox: a machine learning approach.BMC infectious diseases · 2025Article
- Deep Multi-Modal Skin-Imaging-Based Information-Switching Network for Skin Lesion Recognition.Bioengineering (Basel, Switzerland) · 2025Article
- Integrating Humidity-Resistant and Colorimetric COF-on-MOF Sensors with Artificial Intelligence Assisted Data Analysis for Visualization of Volatile Organic Compounds Sensing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Robustly detecting mpox and non-mpox using a deep learning framework based on image inpainting.Scientific reports · 2025Article
- Unlocking Responsive and Unresponsive Signatures: A Transfer Learning Approach for Automated Classification in Cutaneous Leishmaniasis Lesions.Transboundary and emerging diseases · 2025Article
- Skin disease diagnosis using decision and feature level fusion of deep features.Frontiers in digital health · 2025Article
- Urogenital Manifestations in Mpox (Monkeypox) Infection: A Comprehensive Review of Epidemiology, Pathogenesis, and Therapeutic Approaches.Infection and drug resistance · 2025Review
- Intelligent Diagnosis of Hypopigmented Dermatoses and Intelligent Evaluation of Vitiligo Severity on the Basis of Deep Learning.Dermatology and therapy · 2024Article
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Authors and funding
25 authors at 14 institutions in 5 countries.
Funding
Abstract
Undetected infection and delayed isolation of infected individuals are key factors driving the monkeypox virus (now termed mpox virus or MPXV) outbreak. To enable earlier detection of MPXV infection, we developed an image-based deep convolutional neural network (named MPXV-CNN) for the identification of the characteristic skin lesions caused by MPXV. We assembled a dataset of 139,198 skin lesion images, split into training/validation and testing cohorts, comprising non-MPXV images (n = 138,522) from eight dermatological repositories and MPXV images (n = 676) from the scientific literature, news articles, social media and a prospective cohort of the Stanford University Medical Center (n = 63 images from 12 patients, all male). In the validation and testing cohorts, the sensitivity of the MPXV-CNN was 0.83 and 0.91, the specificity was 0.965 and 0.898 and the area under the curve was 0.967 and 0.966, respectively. In the prospective cohort, the sensitivity was 0.89. The classification performance of the MPXV-CNN was robust across various skin tones and body regions. To facilitate the usage of the algorithm, we developed a web-based app by which the MPXV-CNN can be accessed for patient guidance. The capability of the MPXV-CNN for identifying MPXV lesions has the potential to aid in MPXV outbreak mitigation.
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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.