Software for quantifying psoriasis and vitiligo from digital clinical photographs

Kislal, Ellen Eide; Halasz, Charles L.

doi:10.3109/09546634.2011.596184

Cited by 6 publications

(6 citation statements)

References 3 publications

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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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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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“…K‐means is a popular clustering method 37 which have been used for image segmentation, 16 too. K‐means partitions data into non‐overlapping K clusters with the objective function calculated as Equation (5) 38 :

J = {false\sum}_{j = 1}^{n} {false\sum}_{i = 1}^{m} |(||, {(x_{j . i} ‐ c_{j . i})}^{2})|

…”

Section: Methodsmentioning

confidence: 99%

“…Kislal and Halasz have segmented the skin images with K‐means clustering after highlighting the region of interest with specialists. Then, the clusters with high values of redness and/or whiteness have been classified as psoriasis and the remaining clusters have been assigned as background skin 16 . Gupta et al have used agglomerative hierarchical clustering method based on KL divergence as an unsupervised method for image segmentation and vitiligo lesion detection 17 …”

Section: Related Workmentioning

confidence: 99%

Proposing a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) method for localizing vitiligo lesions in skin images

Khatibi

Rezaei

Fashtami

et al. 2020

Skin Research and Technology

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Background Vitiligo is an acquired pigmentary skin disorder characterized by depigmented macules and patches which brings many challenges for the patients suffering from. For vitiligo severity assessment, several scoring methods have been proposed based on morphometry and colorimetry. But, all methods suffer from much inter‐ and intra‐observer variations for estimating the depigmented area. For all mentioned assessment methods of vitiligo disorder, accurate segmentation of the skin images for lesion detection and localization is required. The image segmentation for localizing vitiligo skin lesions has many challenges because of illumination variation, different shapes and sizes of vitiligo lesions, vague lesion boundaries and skin hairs and vignette effects. The manual image segmentation is a tedious and time‐consuming task. Therefore, using automatic image segmentation methods for lesion detection is necessarily required. Materials and methods In this study, a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) methods is proposed for localizing vitiligo lesions in skin images. Unsupervised segmentation methods do not require prior manual segmentation of vitiligo lesions which is a tedious and time‐consuming task with intra‐ and inter‐observer variations. Results Our collected dataset includes 877 images taken from 21 patients with the resolution of 5760*3840 pixels suffering from vitiligo disorder. Experimental results show that SEDCIS outperforms the compared methods with accuracy of 97%, sensitivity of 98%, specificity of 96%, area overlapping of 94%, and Dice index of 97%. Conclusion The proposed method can segment vitiligo lesions with highly reasonable performance and can be used for assessing the vitiligo lesion surface.

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“…As for psoriasis severity rating, only a few attempts have been conducted. Existing works either only calculate a subscore of PASI rather than the full PASI score or are limited to the offline evaluation while lacking the comparison with experienced dermatologists and collection of feedback from clinical application [ 15 - 18 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence–Based Psoriasis Severity Assessment: Real-world Study and Application

Huang¹,

Wang²,

Li³

et al. 2023

J Med Internet Res

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Background Psoriasis is one of the most frequent inflammatory skin conditions and could be treated via tele-dermatology, provided that the current lack of reliable tools for objective severity assessments is overcome. Psoriasis Area and Severity Index (PASI) has a prominent level of subjectivity and is rarely used in real practice, although it is the most widely accepted metric for measuring psoriasis severity currently. Objective This study aimed to develop an image–artificial intelligence (AI)–based validated system for severity assessment with the explicit intention of facilitating long-term management of patients with psoriasis. Methods A deep learning system was trained to estimate the PASI score by using 14,096 images from 2367 patients with psoriasis. We used 1962 patients from January 2015 to April 2021 to train the model and the other 405 patients from May 2021 to July 2021 to validate it. A multiview feature enhancement block was designed to combine vision features from different perspectives to better simulate the visual diagnostic method in clinical practice. A classification header along with a regression header was simultaneously applied to generate PASI scores, and an extra cross-teacher header after these 2 headers was designed to revise their output. The mean average error (MAE) was used as the metric to evaluate the accuracy of the predicted PASI score. By making the model minimize the MAE value, the model becomes closer to the target value. Then, the proposed model was compared with 43 experienced dermatologists. Finally, the proposed model was deployed into an app named SkinTeller on the WeChat platform. Results The proposed image-AI–based PASI-estimating model outperformed the average performance of 43 experienced dermatologists with a 33.2% performance gain in the overall PASI score. The model achieved the smallest MAE of 2.05 at 3 input images by the ablation experiment. In other words, for the task of psoriasis severity assessment, the severity score predicted by our model was close to the PASI score diagnosed by experienced dermatologists. The SkinTeller app has been used 3369 times for PASI scoring in 1497 patients from 18 hospitals, and its excellent performance was confirmed by a feedback survey of 43 dermatologist users. Conclusions An image-AI–based psoriasis severity assessment model has been proposed to automatically calculate PASI scores in an efficient, objective, and accurate manner. The SkinTeller app may be a promising alternative for dermatologists’ accurate assessment in the real world and chronic disease self-management in patients with psoriasis.

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Software for quantifying psoriasis and vitiligo from digital clinical photographs

Abstract: In this paper, we describe a method for quantifying the extent of psoriasis and vitiligo by image processing of digital photographs using machine-learning techniques. By calculating the area of involvement of these conditions, we enable the quantification of treatment response.

Cited by 6 publications

References 3 publications

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

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

Proposing a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) method for localizing vitiligo lesions in skin images

Artificial Intelligence–Based Psoriasis Severity Assessment: Real-world Study and Application

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