A deep learning-based hybrid artificial intelligence model for the detection and severity assessment of vitiligo lesions

Abstract

Background: We aimed to establish and validate a deep learning-based hybrid artificial intelligence (AI) model for the objective morphometric and colorimetric assessment of vitiligo lesions. Methods: Two main datasets containing curated images of vitiligo lesions from Chinese patients (Fitzpatrick skin types III or IV) were established, including one with 2,720 images for lesion localization study and the other with 1,262 images for lesion segmentation study. Besides, an additional test set containing 145 images of vitiligo lesions from other Fitzpatrick skin types (I, II, or V) was also generated. A 3-stage hybrid model was constructed. YOLO v3 (You Only Look Once, v3) architecture was trained and validated to classify and localize vitiligo lesions, with sensitivity and error rate as primary performance outcomes. Then a segmentation study comparing 3 deep convolutional neural networks (DCNNs), Pyramid Scene Parsing Network (PSPNet), UNet, and UNet++, was carried out based on the Jaccard index (JI). The architecture with the best performance was integrated into the model. Three add-on metrics, namely VAreaA, VAreaR, and VColor were finally developed to measure absolute, relative size changes and pigmentation, respectively.Agreement between the AI model and dermatologist evaluators were assessed. Results:The sensitivity of the YOLO v3 architecture to detect vitiligo lesions was 92.91% with an error rate of 14.98%. The UNet++ architecture outperformed the others in the segmentation study (JI, 0.79) and was integrated into the model. On the additional test set, however, the model achieved a lower detection sensitivity (72.41%) and a lower segmentation score (JI, 0.69). With respect to size changes, no difference was observed between the AI model, trained dermatologists (W=0.812, P<0.05), and Photoshop analysis (P=0.075, P=0.212 respectively), which all displayed good concordance. Conclusions:We developed a novel, convenient, objective, and quantitative deep learning-based hybrid model which simultaneously evaluated both morphometric and colorimetric vitiligo lesions from patients with Fitzpatrick skin types III or IV, rendering it suitable for the assessment of severity of vitiligo lesions in Asians in both clinic and research scenarios. More work is also warranted for its use in other ethnic skin groups.

Optimizing vitiligo diagnosis with ResNet and Swin transformer deep learning models: a study on performance and interpretability

“…Furthermore, to ensure easy replication and validation of the research methodology, we analyze the proposed methodology in comparison with some state-of-the-art (SOTA) methods that have performed well 29 , 30 . Considering that most of the recent advances in the use of dermoscopic images have been aimed at bridging the gap between clinical and dermoscopic images 31 , 32 .…”

What happens when simulations get real and cosmetic dermatology goes virtual?

“…These facts leave no doubt about the necessity of more accurate prevention, diagnosis, and education in the field of dermatology. 6…”

“…Studies have shown that 30% of diagnosed diseases globally are dermatological and a significant portion of these processes have led to misdiagnoses. These facts leave no doubt about the necessity of more accurate prevention, diagnosis, and education in the field of dermatology 6 . AI is revolutionizing dermatology by creating a comprehensive model based on various dermatological architectures worldwide.…”

Phototherapy in the artificial intelligence era

“…13 This approach could help clinicians assess the severity of vitiligo more accurately, including during treatment. 14 Additionally, using AI may allow ultraviolet light treatment to be pointed only to the vitiligo (avoiding the side-effect of regular skin tanning) during ultraviolet light treatment, leading to better cosmetic results. 13 AI plays a tremendous role in dermatology.…”