Differential diagnosis of eczema and psoriasis using routine clinical data and machine learning: development of a web-based tool in a multicenter outpatient cohort

Aug 5, 2026

Eczema and psoriasis are common chronic inflammatory skin diseases that often show overlapping clinical features, making early differentiation difficult in routine practice. Using retrospective multicenter outpatient data from 57,518 patients, a machine learning workflow was trained and evaluated with performance metrics including AUC, sensitivity, specificity, PPV, NPV, F1 score, and confusion matrices. The best XGBoost model was externally validated across independent hospital cohorts and interpreted with SHAP to identify key laboratory predictors. An OCR-enabled web interface provides noninvasive decision support for real-time probability estimation in outpatient settings.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Background

Methods

Results

Conclusion

Introduction

Eczema and psoriasis overlap in clinical features

Atopic versus non-atopic eczema

Diagnostic challenges in outpatient settings

Key predictive features and model development

OPEN ACCESS EDITED BY Joel Correa Da Rosa, Icahn School of Medicine at Mount Sinai, United States REVIEWED BY Kunju Zhu, University of Pittsburgh, United States Pichit Boonkrong, Rangsit University, Thailand *CORRESPONDENCE Ying Wang [wydn20232023@163.com](wydn20232023@163.com) RECEIVED 22 July 2025 ACCEPTED 03 October 2025 PUBLISHED 17 October 2025 CITATION Ding N, Li Y, Zhao Z, Meng X, Sun M, Ren X and Wang Y (2025) Differential diagnosis of eczema and psoriasis using routine clinical data and machine learning: development of a web-based tool in amulticenter outpatient cohort. Front. Med. 12:1667794 . doi: 10.3389/fmed.2025.1667794 COPYRIGHT © 2025 Ding, Li, Zhao, Meng, Sun, Ren and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Original Research PUBLISHED 17 October 2025 DOI 10.3389/fmed.2025.1667794 Differential diagnosis of eczema and psoriasis using routine clinical data and machine learning: development of a web-based tool in a multicenter outpatient cohort Ning Ding 1, Yinhao Li 2, Zheng Zhao 2, Xiangfu Meng 2, Mingqiang Sun3, Xueqing Ren4 and Ying Wang 1* 1 Department of Dermatology, Shengjing Hospital, China Medical University, Shenyang, China, 2College of Electronics and Information Engineering, Liaoning Technical University, Huludao, China, 3 Department of Dermatology, Dermatology Hospital, Shenyang, China, 4 Department of Dermatology, The First Affiliated Hospital, Dalian Medical University, Dalian, China Background: Eczema and psoriasis are common chronic dermatoses with overlapping features, making early differential diagnosis difficult. While biopsy is the gold standard, its invasiveness and dependence on clinician expertise restrict routine application, especially in primary care. To overcome these limitations, we developed a machine learning-based diagnostic tool using routine laboratory data, enabling non-invasive, accurate, and practical differentiation between eczema and psoriasis in outpatient settings. Methods: We retrospectively analyzed clinical and routine laboratory data from 57,518 patients with eczema and psoriasis across three medical centers. Patients with confirmed diagnoses and complete laboratory records were included, while those with missing key data were excluded. Eight machine learning models were trained using data from Shengjing Hospital. Model performance was evaluated using accuracy, AUC, sensitivity, specificity, PPV, NPV, F1 score, and confusion matrix. The best-performing model, XGBoost, was externally validated on independent cohorts from two other hospitals. SHapley Additive exPlanation (SHAP) were applied to assess feature importance. Finally, a webbased tool was developed integrating the optimal model with optical character recognition (OCR) for automatic data input. Results: XGBoost demonstrated the best performance, with AUCs of 0. 891, 0. 830, and 0.812 for the training, internal test, and external test sets, respectively. Key predictive features included dNLR, neutrophil count, SIRI, RDW, and eosinophil count, which were consistent with known clinical patterns. The final model was deployed as an interactive web tool, allowing manual or OCR-based data input to provide real-time prediction probabilities. Conclusion: This machine learning-based diagnostic tool showed strong performance and interpretability in differentiating eczema from psoriasis using routine laboratory data. The user-friendly web interface enables rapid, noninvasive decision support in outpatient clinical settings. KEYWORDS eczema, psorias

FAQs

Why is differential diagnosis between eczema and psoriasis challenging in routine outpatient care?

Eczema and psoriasis share overlapping signs such as erythema, scaling, and pruritus, which complicates accurate differentiation. Biopsy is invasive and relies on clinician expertise, limiting routine use.

What data and modeling approach were used to build the diagnostic tool?

The study retrospectively analyzed routine clinical laboratory data and trained eight machine learning models. The best model, XGBoost, was selected based on metrics including AUC and sensitivity/specificity and then externally validated.

How was the model interpreted and what features were most important?

SHAP (SHapley Additive exPlanation) was applied to assess feature importance. Key predictive features included dNLR, neutrophil count, SIRI, RDW, and eosinophil count.

How does the web-based tool support clinicians in practice?

The deployed interactive web tool accepts manual input or OCR-based automatic data input. It outputs real-time prediction probabilities to support noninvasive clinical decision-making in outpatient settings.

Aug 5, 2026

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