ArticleFrontiers in medicine2022
A deep learning-based approach toward differentiating scalp psoriasis and seborrheic dermatitis from dermoscopic images.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 3 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Diagnostic accuracy of artificial intelligence compared to family physicians and dermatologists for skin conditions: a systematic review and meta-analysis.BMC primary care · 2025Pooled it
- Advancements and challenges of artificial intelligence in dermatology: a review of applications and perspectives in China.Frontiers in digital health · 2025Pooled it
- A review of psoriasis image analysis based on machine learning.Frontiers in medicine · 2024Pooled it
- Excimer light versus topical methotrexate 1% hydrogel in treatment of scalp psoriasis: clinical and dermoscopic study.Archives of dermatological research · 2025Trial
- Scalp Rosacea: Current Evidence.Dermatology and therapy · 2026Review
- 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
- Artificial intelligence-enabled precision medicine for inflammatory skin diseases.The Journal of investigative dermatology · 2026Review
- Image Processing for Diagnosing Psoriasis: A Machine Learning Approach to Classify Skin Lesions into Psoriasis Subtypes.Medical journal of the Islamic Republic of Iran · 2026Article
- Evaluation of multimodal large language models for psoriasis diagnosis, severity grading, and treatment recommendations from clinical photographs: ChatGPT shows superior performance compared to other large language models.Frontiers in medicine · 2026Article
- Artificial intelligence-based automated grading and severity prediction of radiation dermatitis: a review.Radiation oncology (London, England) · 2025Review
- Artificial Intelligence Applications in Decision-Making for Disease Management:Sultan Qaboos University medical journal · 2025Article
- Enhancing dermatological diagnosis for differentiating actinic from seborrheic keratosis using deep learning model.Frontiers in medicine · 2025Article
- CAD-PsorNet: deep transfer learning for computer-assisted diagnosis of skin psoriasis.Scientific reports · 2024Article
- Advancing Psoriasis Care through Artificial Intelligence: A Comprehensive Review.Current dermatology reports · 2024Article
- A bibliometric and visual analysis of the research status and hotspots of seborrheic dermatitis based on web of science.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) · 2024Article
- The Use of Artificial Intelligence for Skin Disease Diagnosis in Primary Care Settings: A Systematic Review.Healthcare (Basel, Switzerland) · 2024Review
- Enhancing assisted diagnostic accuracy in scalp psoriasis: A Multi-Network Fusion Object Detection Framework for dermoscopic pattern diagnosis.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) · 2024Article
- Principles, applications, and future of artificial intelligence in dermatology.Frontiers in medicine · 2023Review
- Review
- Pediatric Dermatitis Seborrhoica - A Clinical and Therapeutic Review.Indian dermatology online journalReview
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: This study aims to develop a new diagnostic method for discriminating scalp psoriasis and seborrheic dermatitis based on a deep learning (DL) model, which uses the dermatoscopic image as input and achieved higher accuracy than dermatologists trained with dermoscopy. Methods: A total of 1,358 pictures (obtained from 617 patients) with pathological and diagnostic confirmed skin diseases (508 psoriases, 850 seborrheic dermatitides) were randomly allocated into the training, validation, and testing datasets (1,088/134/136) in this study. A DL model concerning dermatoscopic images was established using the transfer learning technique and trained for diagnosing two diseases. Results: The developed DL model exhibits good sensitivity, specificity, and Area Under Curve (AUC) (96.1, 88.2, and 0.922%, respectively), it outperformed all dermatologists in the diagnosis of scalp psoriasis and seborrheic dermatitis when compared to five dermatologists with various levels of experience. Furthermore, non-proficient doctors with the assistance of the DL model can achieve comparable diagnostic performance to dermatologists proficient in dermoscopy. One dermatology graduate student and two general practitioners significantly improved their diagnostic performance, where their AUC values increased from 0.600, 0.537, and 0.575 to 0.849, 0.778, and 0.788, respectively, and their diagnosis consistency was also improved as the kappa values went from 0.191, 0.071, and 0.143 to 0.679, 0.550, and 0.568, respectively. DL enjoys favorable computational efficiency and requires few computational resources, making it easy to deploy in hospitals. Conclusions: The developed DL model has favorable performance in discriminating two skin diseases and can improve the diagnosis, clinical decision-making, and treatment of dermatologists in primary hospitals.
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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.