Artificial intelligence for the automated single-shot assessment of psoriasis severity.

ABSTRACT

BACKGROUND: PASI score is globally used to assess disease activity of psoriasis. However, it is relatively complicated and time-consuming, and the score will vary due to the inconsistent subjectivity between dermatologists. Therefore, an AI system capable of assessing psoriasis severity will be useful. OBJECTIVES: To propose a simplified PASI system (Single-Shot PASI) and associated AI models capable of assessing psoriasis severity. METHODS: Overall, 705 psoriasis images of the trunk's front and back were used in our research. Considering the relatively small number of images, we used data augmentation techniques to expand the data. A psoriasis expert's scores were used as teacher data. Various convolutional neural network models and hyperparameters were adjusted using a fivefold cross-validation. From these adjustments, we discovered that fine-tuning Imagenet2012-pretrained InceptionV3 whose last linear layer was replaced by a two-layer perceptron (30 hidden units and five output units) exhibited the best performance. RESULTS: To validate our deep learning system, 10 images were selected as test sets and were excluded from the training sets. The AI assessment of Single-Shot PASI was almost consistent with the clinical severity. We examined whether AI assistance would affect human scoring. In this study, 13 dermatologists and 9 medical students were invited as evaluators. Mean absolute differences from AI scores and standard deviation among evaluators reduced with AI assistance. In addition, the evaluator's scores got close to the teacher's score with AI's assistance. CONCLUSIONS: We proposed a Single-Shot PASI system and developed an associated AI system capable of assessing psoriasis severity simply by uploading a single clinical image. An easy-to-use scoring system and our freely available AI software would help dermatologists and patients with psoriasis.