Artificial Intelligence-Based Psoriasis Severity Assessment: Real-world Study and Application.

BACKGROUND: Psoriasis is one of the most frequent inflammatory skin conditions and could be treated via tele-dermatology, provided that the current lack of reliable tools for objective severity assessments is overcome. Psoriasis Area and Severity Index (PASI) has a prominent level of subjectivity and is rarely used in real practice, although it is the most widely accepted metric for measuring psoriasis severity currently. OBJECTIVE: This study aimed to develop an image-artificial intelligence (AI)-based validated system for severity assessment with the explicit intention of facilitating long-term management of patients with psoriasis. METHODS: A deep learning system was trained to estimate the PASI score by using 14,096 images from 2367 patients with psoriasis. We used 1962 patients from January 2015 to April 2021 to train the model and the other 405 patients from May 2021 to July 2021 to validate it. A multiview feature enhancement block was designed to combine vision features from different perspectives to better simulate the visual diagnostic method in clinical practice. A classification header along with a regression header was simultaneously applied to generate PASI scores, and an extra cross-teacher header after these 2 headers was designed to revise their output. The mean average error (MAE) was used as the metric to evaluate the accuracy of the predicted PASI score. By making the model minimize the MAE value, the model becomes closer to the target value. Then, the proposed model was compared with 43 experienced dermatologists. Finally, the proposed model was deployed into an app named SkinTeller on the WeChat platform. RESULTS: The proposed image-AI-based PASI-estimating model outperformed the average performance of 43 experienced dermatologists with a 33.2% performance gain in the overall PASI score. The model achieved the smallest MAE of 2.05 at 3 input images by the ablation experiment. In other words, for the task of psoriasis severity assessment, the severity score predicted by our model was close to the PASI score diagnosed by experienced dermatologists. The SkinTeller app has been used 3369 times for PASI scoring in 1497 patients from 18 hospitals, and its excellent performance was confirmed by a feedback survey of 43 dermatologist users. CONCLUSIONS: An image-AI-based psoriasis severity assessment model has been proposed to automatically calculate PASI scores in an efficient, objective, and accurate manner. The SkinTeller app may be a promising alternative for dermatologists' accurate assessment in the real world and chronic disease self-management in patients with psoriasis.

Validation of a simple measure of psoriasis severity based on a longitudinal study of Chinese patients.

BACKGROUND: The product of Physician Global Assessment (PGA) and Body Surface Area (BSA) (PGA × BSA) has been proposed as a simple and sensitive instrument for measuring psoriasis severity. OBJECTIVES: To assess the simple measure, PGA × BSA, with respect to criterion validity, reproducibility, responsiveness, and interpretability among Chinese patients with psoriasis. MATERIALS & METHODS: Assessments of psoriasis severity were performed by two dermatologists independently for the baseline survey and by one dermatologist during follow-up. Criterion validity and reproducibility were assessed using Spearman correlation coefficients (r). Responsiveness was assessed by comparing the percentage changes in PGA × BSA (PGA × ΔBSA [%]) between patients grouped by disease improvement. The receiver operating characteristic (ROC) curve was used to determine the threshold of PGA × ΔBSA for disease improvement, anchored by 50% and 75% reduction in Psoriasis Area Severity Index (PASI). RESULTS: A total of 276 patients participated in the baseline survey, of whom 93 were followed. PGA × BSA highly correlated with PASI (r = 0.94), Simplified PASI (SPASI, r = 0.93), and Psoriasis Log-based Area and Severity Index (PLASI, r = 0.90) measured at baseline, indicating good criterion validity. The between-evaluator consistency of PGA × BSA was r = 0.95, indicating high reproducibility. PGA × ΔBSA highly correlated with both ΔPASI (r = 0.86) and ΔPLASI (r = 0.85), suggesting good responsiveness. The threshold of ΔPGA × BSA for disease improvement was 57% and 73%, as indicated by 50% and 75% reduction in PASI, respectively. CONCLUSION: PGA × BSA demonstrates good biometric properties and may be used to measure the severity of psoriasis among Chinese patients.