Standardized clinical photography considerations in patients across skin tones

Oh, Yuna; Markova, Alina; Noor, Sarah; Rotemberg, Veronica

doi:10.1111/bjd.20766

Cited by 11 publications

(6 citation statements)

References 4 publications

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“…In addition, the inclusion of shiny skin patches due to light reflections affects the estimated metrics. The latter could be effectively addressed by using cross-polarized photography as suggested and verified by previous researchers [35,36].…”

Section: Discussionmentioning

confidence: 57%

Image Perceptual Similarity Metrics for the Assessment of Basal Cell Carcinoma

Spyridonos

Gaitanis

Likas

et al. 2023

Cancers

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Efficient management of basal cell carcinomas (BCC) requires reliable assessments of both tumors and post-treatment scars. We aimed to estimate image similarity metrics that account for BCC’s perceptual color and texture deviation from perilesional skin. In total, 176 clinical photographs of BCC were assessed by six physicians using a visual deviation scale. Internal consistency and inter-rater agreement were estimated using Cronbach’s α, weighted Gwet’s AC2, and quadratic Cohen’s kappa. The mean visual scores were used to validate a range of similarity metrics employing different color spaces, distances, and image embeddings from a pre-trained VGG16 neural network. The calculated similarities were transformed into discrete values using ordinal logistic regression models. The Bray–Curtis distance in the YIQ color model and rectified embeddings from the ‘fc6’ layer minimized the mean squared error and demonstrated strong performance in representing perceptual similarities. Box plot analysis and the Wilcoxon rank-sum test were used to visualize and compare the levels of agreement, conducted on a random validation round between the two groups: ‘Human–System’ and ‘Human–Human.’ The proposed metrics were comparable in terms of internal consistency and agreement with human raters. The findings suggest that the proposed metrics offer a robust and cost-effective approach to monitoring BCC treatment outcomes in clinical settings.

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Section: Discussionmentioning

confidence: 57%

Image Perceptual Similarity Metrics for the Assessment of Basal Cell Carcinoma

Spyridonos

Gaitanis

Likas

et al. 2023

Cancers

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“…To prevent variability in daylight conditions, artificial lighting with similar temperature to natural light (5000–6500 K) could be helpful 42 . A controlled illumination source, combined with an ambient light-blocking feature, can significantly enhance light isolation and improve the signal-to-noise ratio 43 .…”

Section: Resultsmentioning

confidence: 99%

A survey of skin tone assessment in prospective research

Weir,

Dempsey,

Gichoya

et al. 2024

npj Digit. Med.

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Increasing evidence supports reduced accuracy of noninvasive assessment tools, such as pulse oximetry, temperature probes, and AI skin diagnosis benchmarks, in patients with darker skin tones. The FDA is exploring potential strategies for device regulation to improve performance across diverse skin tones by including skin tone criteria. However, there is no consensus about how prospective studies should perform skin tone assessment in order to take this bias into account. There are several tools available to conduct skin tone assessments including administered visual scales (e.g., Fitzpatrick Skin Type, Pantone, Monk Skin Tone) and color measurement tools (e.g., reflectance colorimeters, reflectance spectrophotometers, cameras), although none are consistently used or validated across multiple medical domains. Accurate and consistent skin tone measurement depends on many factors including standardized environments, lighting, body parts assessed, patient conditions, and choice of skin tone assessment tool(s). As race and ethnicity are inadequate proxies for skin tone, these considerations can be helpful in standardizing the effect of skin tone on studies such as AI dermatology diagnoses, pulse oximetry, and temporal thermometers. Skin tone bias in medical devices is likely due to systemic factors that lead to inadequate validation across diverse skin tones. There is an opportunity for researchers to use skin tone assessment methods with standardized considerations in prospective studies of noninvasive tools that may be affected by skin tone. We propose considerations that researchers must take in order to improve device robustness to skin tone bias.

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“…42,43 This mode is generally recognised as optimal for accurate skin tone classification and analysis of persons with darker skin tones. [44][45][46]…”

Section: Face and Hand Imageingmentioning

confidence: 99%

Predicting human chronological age via AI analysis of dorsal hand versus facial images: A study in a cohort of Indian females

Georgievskaya,

Tlyachev,

Kiselev

et al. 2024

Experimental Dermatology

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Predicting a person's chronological age (CA) from visible skin features using artificial intelligence (AI) is now commonplace. Often, convolutional neural network (CNN) models are built using images of the face as biometric data. However, hands hold telltale signs of a person's age. To determine the utility of using only hand images in predicting CA, we developed two deep CNNs based on 1) dorsal hand images (H) and 2) frontal face images (F). Subjects (n = 1454) were Indian women, 20–80 years, across three geographic cohorts (Mumbai, New Delhi and Bangalore) and having a broad variation in skin tones. Images were randomised: 70% of F and 70% of H were used to train CNNs. The remaining 30% of F and H were retained for validation. CNN validation showed mean absolute error for predicting CA using F and H of 4.1 and 4.7 years, respectively. In both cases correlations of predicted and actual age were statistically significant (r(F) = 0.93, r(H) = 0.90). The CNNs for F and H were validated for dark and light skin tones. Finally, by blurring or accentuating visible features on specific regions of the hand and face, we identified those features that contributed to the CNN models. For the face, areas of the inner eye corner and around the mouth were most important for age prediction. For the hands, knuckle texture was a key driver for age prediction. Collectively, for AI estimates of CA, CNNs based solely on hand images are a viable alternative and comparable to CNNs based on facial images.

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Standardized clinical photography considerations in patients across skin tones

Cited by 11 publications

References 4 publications

Image Perceptual Similarity Metrics for the Assessment of Basal Cell Carcinoma

Image Perceptual Similarity Metrics for the Assessment of Basal Cell Carcinoma

A survey of skin tone assessment in prospective research

Predicting human chronological age via AI analysis of dorsal hand versus facial images: A study in a cohort of Indian females

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