Clinical and Epidemiological Characteristics of Vitiligo at Different Ages: An Analysis of 571 Patients in Northwest China

Zhang, Jing-Zhan; Luo, Dan; An, Caixia; Xu, Chao; Zhao, Yanan; Li, Jinxi; Yu, Shi-Rong; Zhang, Dezhi; Kang, Xiaojian

doi:10.1097/jd9.0000000000000028

Cited by 2 publications

(2 citation statements)

References 14 publications

(16 reference statements)

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“…Vitiligo merupakan penyakit autoimun yang ditandai dengan depigmentasi pada kulit yang tidak merata [5]. Sama seperti halnya psoriasis, vitiligo juga berpengaruh terhadap kesehatan mental dan aktivitas sosial pasiennya [6]. Prevalensi vitiligo di dunia diperkirakan mencapai 0,5-2% [7].…”

Section: Pendahuluanunclassified

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Syafaat¹

2021

JETT

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Psoriasis dan vitiligo merupakan kelainan pada kulit yang ditandai dengan berkembangnya plak pada kulit untuk psoriasis dan dipigmentasi kulit untuk vitiligo. Fototerapi NB-UVB dianggap sebagai metode pengobatan psoriasis dan vitiligo yang lebih efektif dan lebih aman dibanding metode yang lain. Penggunaan alat fototerapi konvensional yang ada di rumah sakit atau pusat dermatologi memiliki banyak kelemahan di antaranya biaya yang mahal dan tidak efisien. Untuk mengatasi hal tersebut dibutuhkan sebuat alat fototerapi NB-UVB portabel sehingga terapi lebih efisien dan lebih murah. Tujuan penelitian ini yaitu merancang alat fototerapi NB-UVB 311 nm portabel yang dilengkapi kontrol jarak dan waktu agar terapi menjadi lebih efektif. Penelitian ini dilakukan melalui beberapa tahap yaitu pembuatan blok diagram, perancangan rangkaian perangkat keras (pengaturan waktu, catu daya, driver relay, display), perencanaan program arduino dan pengujian sistem. Alat fototerapi NB-UVB portabel untuk penyakit psoriasis dan vitiligo yang dibuat sesuai dengan perancangan.

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Section: Pendahuluanunclassified

Untitled

Syafaat¹

2021

JETT

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“…Vitiligo is a common, acquired, patchy, depigmented skin disease that can be localized or widespread. The prevalence of vitiligo varies worldwide, with previous studies reporting a rate of 0.2% to 1.8% (1,2). Despite its benign nature, the burden of vitiligo is comparable to other skin diseases such as psoriasis and eczema (3).…”

Section: Introductionmentioning

confidence: 99%

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

Guo¹,

Yang²,

Ding³

et al. 2022

Ann Transl Med

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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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Clinical and Epidemiological Characteristics of Vitiligo at Different Ages: An Analysis of 571 Patients in Northwest China

Cited by 2 publications

References 14 publications

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

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