Automated scoring of vitiligo using superpixel-generated computerized digital image analysis of clinical photographs: a novel and consistent way to score vitiligo

Toh, Joseph Jia Hong; Bhoi, Suman; Tan, Virlynn; Chuah, Sai Yee; Jhingan, Anjali; Kong, Adams Wai-Kin; Thng, Steven Tien Guan

doi:10.1111/bjd.16563

Cited by 10 publications

(18 citation statements)

References 3 publications

(6 reference statements)

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“…To meet the clinical needs, several studies employed imaging analysis to evaluate the changes of the depigmentation area, but restricted by factors like large depigmentation, curved body surface and complex repigmentation patterns 242–245 . A recent publication introduced a delicate method to generate VASI automatically, through superpixel‐generated computerized digital image analysis of clinical photographs with ImageJ 246 . Another study proposed a protocol for calculating the true depigmentation area which could serve as a “gold standard” for validation of other digital tools 247 .…”

Section: Adaptive Immune Activationmentioning

confidence: 99%

Mechanisms of melanocyte death in vitiligo

Chen

2020

Medicinal Research Reviews

159

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Vitiligo is an autoimmune depigment disease results from extensive melanocytes destruction. The destruction of melanocyte is thought to be of multifactorial causation. Genome‐wide associated studies have identified single‐nucleotide polymorphisms in a panel of susceptible loci as risk factors in melanocyte death. But vitiligo onset can't be solely attributed to a susceptive genetic background. Oxidative stress triggered by elevated levels of reactive oxygen species accounts for melanocytic molecular and organelle dysfunction, a minority of melanocyte demise, and melanocyte‐specific antigens exposure. Of note, the self‐responsive immune function directly contributes to the bulk of melanocyte deaths in vitiligo. The aberrantly heightened innate immunity, type‐1‐skewed T helper, and incompetent regulatory T cells tip the balance toward autoreaction and CD8+ cytotoxic T lymphocytes finally execute the killing of melanocytes, possibly alarmed by resident memory T cells. In addition to the well‐established apoptosis and necrosis, we discuss several death modalities like oxeiptosis, ferroptosis, and necroptosis that are probably employed in melanocyte destruction. This review focuses on the various mechanisms of melanocytic death in vitiligo pathogenesis to demonstrate a panorama of that. We hope to provide new insights into vitiligo pathogenesis and treatment strategies by the review.

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Section: Adaptive Immune Activationmentioning

confidence: 99%

Mechanisms of melanocyte death in vitiligo

Chen

2020

Medicinal Research Reviews

159

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“…A standardized, reproducible way to assess acne and their response to treatment would allow patients to self-monitor through mobile applications, physicians to track progress remotely through tele-dermatology, and research studies to be compared with each other in meta-analyses. 6,7 The use of digital image analysis has proven useful in the monitoring of several other skin conditions such as melasma 8 and vitiligo, 9 and deep learning has already had an impact in the automated diagnosis of skin cancers. 10,11 This study leverages on the interdisciplinary collaboration between clinician dermatologists and bioinformatics researchers to develop an algorithm to automatically calculate IGA using digital image analysis [12][13][14] for the assessment of acne severity and monitoring of treatment outcomes.…”

Section: Introductionmentioning

confidence: 99%

Automated grading of acne vulgaris by deep learning with convolutional neural networks

Lim

Akram

Ngo

et al. 2019

Skin Research and Technology

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Background The visual assessment and severity grading of acne vulgaris by physicians can be subjective, resulting in inter‐ and intra‐observer variability. Objective To develop and validate an algorithm for the automated calculation of the Investigator's Global Assessment (IGA) scale, to standardize acne severity and outcome measurements. Materials and Methods A total of 472 photographs (retrieved 01/01/2004‐04/08/2017) in the frontal view from 416 acne patients were used for training and testing. Photographs were labeled according to the IGA scale in three groups of IGA clear/almost clear (0‐1), IGA mild (2), and IGA moderate to severe (3‐4). The classification model used a convolutional neural network, and models were separately trained on three image sizes. The photographs were then subjected to analysis by the algorithm, and the generated automated IGA scores were compared to clinical scoring. The prediction accuracy of each IGA grade label and the agreement (Pearson correlation) of the two scores were computed. Results The best classification accuracy was 67%. Pearson correlation between machine‐predicted score and human labels (clinical scoring and researcher scoring) for each model and various image input sizes was 0.77. Correlation of predictions with clinical scores was highest when using Inception v4 on the largest image size of 1200 × 1600. Two sets of human labels showed a high correlation of 0.77, verifying the repeatability of the ground truth labels. Confusion matrices show that the models performed sub‐optimally on the IGA 2 label. Conclusion Deep learning techniques harnessing high‐resolution images and large datasets will continue to improve, demonstrating growing potential for automated clinical image analysis and grading.

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“…Validity was evaluated in 14 studies and was based on a comparison to a second instrument. Five were classified as adequate (contact planimetry: n = 3; 2D DIAS: n = 2), while 8 studies (57%) received an inadequate score (Aydin et al, 2007; Toh et al, 2018; Uitentuis et al, 2020; Van Geel et al, 2004; van Geel, Vandendriessche, et al, 2019) (Hayashi et al, 2016; Kanthraj et al, 1997; Khatibi et al, 2021; Kislal & Halasz, 2013; Kohli et al, 2015; Marrakchi et al, 2008; Nugroho et al, 2013; Sheth et al, 2015). Inadequate ratings were due to low statistical evidence and/or weak reliability of the comparator (no measurement properties evaluated for the comparison instrument).…”

Section: Resultsmentioning

confidence: 99%

Image analysis systems to calculate the surface area of vitiligo lesions: A systematic review of measurement properties

Geel

Saeys

Causenbroeck

et al. 2022

Pigment Cell Melanoma Res

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Several digital image analysis systems have been developed for surface calculation of vitiligo lesions. Critical assessment of their measurement properties is crucial to support evidence‐based recommendations on the most suitable instruments and will reveal the need for future research. A systematic review was performed to systematically summarize, compare, and critically assess the measurement properties of digital and analogue analysis systems for surface calculation of vitiligo lesions following the Consensus‐Based Standards for the Selection of Health Measurement Instruments (COSMIN) recommendations. Nineteen clinical trials were selected including 25 different instruments. Manual tracing on transparent sheets (contact planimetry) combined with digital measurement or point counting can be considered as the best validated method for the evaluation of target lesions taking into account the skin curvatures. Two‐dimensional digital imaging analysis on photographs seems also robust although confirmatory data of different research groups using the same digital instrument in a wide range of skin types are missing. Analysis based on 3D photography is still in its early stage but is promising for whole‐body analysis. However, the reported data on the quality of the instruments for surface area calculation of vitiligo lesions were in general rather limited. Therefore, future high‐quality validation studies are required also including full body evaluations.

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Automated scoring of vitiligo using superpixel-generated computerized digital image analysis of clinical photographs: a novel and consistent way to score vitiligo

Cited by 10 publications

References 3 publications

Mechanisms of melanocyte death in vitiligo

Mechanisms of melanocyte death in vitiligo

Automated grading of acne vulgaris by deep learning with convolutional neural networks

Image analysis systems to calculate the surface area of vitiligo lesions: A systematic review of measurement properties

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