ArticleNarra J2023
Evaluation of atopic dermatitis severity using artificial intelligence.
Article in Narra J, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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.
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Who cites it
10 citing papers in PubMed.
- Current research landscape and future prospects of in silico modeling approaches for atopic dermatitis.JID innovations : skin science from molecules to population health · 2026Review
- Multi-Kingdom Synergy ofLife (Basel, Switzerland) · 2026Review
- Artificial Intelligence-Assisted Diagnosis of Atopic Eczema in Darker Skin Types: A CNN-Based Study.Dermatology practical & conceptual · 2026Article
- Artificial intelligence-based automated grading and severity prediction of radiation dermatitis: a review.Radiation oncology (London, England) · 2025Review
- Advancements in artificial intelligence for atopic dermatitis: diagnosis, treatment, and patient management.Annals of medicine · 2025Review
- Atopic dermatitis: a comprehensive updated review of this intriguing disease with futuristic insights.Inflammopharmacology · 2025Review
- Optimized nanostructured lipid carriers from aceh traditional coconut (Pliek) oil: a promising topical formulations for atopic dermatitis.Archives of dermatological research · 2025Article
- Application and research progress of artificial intelligence in allergic diseases.International journal of medical sciences · 2025Review
- Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis.F1000Research · 2025Article
- Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atopic dermatitis is a prevalent and persistent chronic inflammatory skin disorder that poses significant challenges when it comes to accurately assessing its severity. The aim of this study was to evaluate deep learning models for automated atopic dermatitis severity scoring using a dataset of Aceh ethnicity individuals in Indonesia. The dataset of clinical images was collected from 250 patients at Dr. Zainoel Abidin Hospital, Banda Aceh, Indonesia and labeled by dermatologists as mild, moderate, severe, or none. Five pretrained convolutional neural networks (CNN) architectures were evaluated: ResNet50, VGGNet19, MobileNetV3, MnasNet, and EfficientNetB0. The evaluation metrics, including accuracy, precision, sensitivity, specificity, and F1-score, were employed to assess the models. Among the models, ResNet50 emerged as the most proficient, demonstrating an accuracy of 89.8%, precision of 90.00%, sensitivity of 89.80%, specificity of 96.60%, and an F1-score of 89.85%. These results highlight the potential of incorporating advanced, data-driven models into the field of dermatology. These models can serve as invaluable tools to assist dermatologists in making early and precise assessments of atopic dermatitis severity and therefore improve patient care and outcomes.
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Registered trials
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