A deep learning-based hybrid artificial intelligence model for the detection and severity assessment of vitiligo lesions
Open accessAbstract
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.
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A hybrid AI model using YOLOv3 for lesion detection and UNet++ for segmentation performed better on Fitzpatrick skin types III/IV for vitiligo.
“Guo et al (2022) developed a hybrid AI model using YOLOv3 for lesion detection and UNet++ for segmentation, with better performance on Fitzpatrick skin types III/IV.”
From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Guo et al, 2022
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Self-citationTanvir, S., Syed, S. A., Sheikh, J., Raziq, M., Swee, T. T., Shafique, M. J., Maroof, R., Olmedo, J. J. S., & Salim, M. I. M. (2026) · Jurnal Kejuruteraan · doi:10.17576/jkukm-2026-38(3)-16
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- Authors
- Guo, L., Yang, Y., Ding, H., Zheng, H., Yang, H., Xie, J., Li, Y., Lin, T., & Ge, Y.
- Affiliations
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Corresponding author
- Lin, Tong
- Ge, Yiping
- Venue
- Annals of Translational Medicine
- Publisher
- AME Publishing Company
- Published
- 2022-05
- Volume
- 10
- Issue
- 10
- Pages
- 590-590
- ISSN
- 2305-5839 (ISSN-L)
- 2305-5847
- PMC
- PMC9201159
- OpenAlex
- W4283210562
- Cited by
- 62
- References
- 43
- Citations by year
- 2026: 19 · 2025: 18 · 2024: 7 · 2023: 16 · 2022: 2
- FWCI
- 5.75
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Annals of Translational Medicine (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Chinese Academy of Medical Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › melanin and skin pigmentation
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- vitiligo, lesion segmentation, lesion severity assessment, YOLOv3, U-Net, PSPNet, morphometric assessment, colorimetric evaluation, lesion size change, pigmentation, Fitzpatrick skin type, Asian patients, deep convolutional neural networks, hybrid AI models
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