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

Abstract

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.

Keywords: PASI; Psoriasis Area and Severity Index; artificial intelligence; chronic disease; deep learning system; dermatology; design; inflammation; management; mobile app; model; psoriasis; psoriasis severity assessment; tools; users.

Figure 1

The structure of the proposed image–artificial intelligence (AI)–based Psoriasis Area and Severity Index (PASI) assessment model, which refers to the PASI score rating module. Avg: average; C: channel; conv: convolution; FC: fully connected; H: height; ROI: region of interest; SE: squeeze and excitation; W: width.

Figure 2

Overview of the workflow of the proposed model and the SkinTeller mobile app. (a) The image–artificial intelligence (AI)–based Psoriasis Area and Severity Index (PASI)–estimating model. (b) The workflow of the SkinTeller app that is integrated with the proposed model. (c) The clinical significance for both doctors and patients. CNN: convolutional neural network; F: female; M: male; N: no; Y: yes.

Figure 3

The practical application of the proposed image-AI–based PASI-estimating model. (a) The PASI scores between the AI and 3 doctors for a patient with psoriasis at different treatment phases. (b) The clinical images of the patient at different treatment phases. AI: artificial intelligence; PASI: Psoriasis Area and Severity Index.

Figure 4

Page introduction and service module of the mobile app SkinTeller. (A) On the first page, mobile app SkinTeller includes the function of severity score (severity rating and psoriasis diagnosis), screening (intelligent diagnosis), patient list (managing the patients who the current dermatologist is response of), statistics (providing data analysis of patients who the current dermatologist is response of), psoriasis knowledge (recommending the articles of psoriasis self-management), calendar reminder (reminding the patients of hospital revisits and medicine dosage), daily recording (recording the vital signs, such as blood pressure, pulse and the medical dosage history), questionnaire scale (Self-rating Anxiety Scale, Self-rating Depression Scale, Health-Related Quality of Life, etc) and nearly hospitals (providing information about nearby hospitals to facilitate patient treatment). (B) On the second page, multimodal data input page (including metadata and images). (C) On the third page, the example of photo guide which instruct the patient how to take pictures of each part of the body. (D) on the last page, the result page includes the overall PASI score as well as all 16 subscores. PASI: Psoriasis Area and Severity Index.