Development and clinical validation of a novel photography‐based skin erythema evaluation system: a comparison with the calculated consensus of dermatologists

Cho, M.; Lee, D.-H.; Doh, E. J.; Kim, Y.; Chung, Jin Ho; Kim, Hyun-Chang; Kim, S.

doi:10.1111/ics.12393

Cited by 5 publications

(4 citation statements)

References 41 publications

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“…Furthermore, taking the CADESI-04based scale as a reference, the erythema detection accuracy reached 89.5%, which is comparable to a similar study in humans (93.2%). 9 When the overall classification accuracy was studied, our system (71.1%) exceeded that of a system with a digital camera in humans with psoriasis (48.8%); 7 however, the erythema severity scale in the Psoriasis Area and Severity Index (PASI) included one additional erythema grading category (i.e. 5).…”

Section: Discussionmentioning

confidence: 83%

“…In humans, various imaging devices [7][8][9] objectively estimate the erythema severity by calculating the ratio between spectral bands, the so-called erythema index (EI). In veterinary medicine, to the best of our knowledge, there has been only one attempt to use cameras or spectrometers for estimating canine erythema, where the acquired data were converted to the standard CIELAB colour space, which corresponds to the spectrum of normal human vision.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of skin erythema severity by dermatoscopy in dogs with atopic dermatitis

Cugmas

Olivry

2021

Veterinary Dermatology

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Background To estimate the extent and severity of atopic dermatitis (AD)‐related skin lesions, clinical trials enrolling dogs with AD often use categorical scales such as the Canine Atopic Dermatitis Extent and Severity Index, 4th iteration (CADESI‐04) and Canine Atopic Dermatitis Lesion Index (CADLI). Despite recent progress in the standardization of these AD‐grading scales, the evaluation of the severity of skin lesions (including erythema) remains subjective. Objectives To validate an optical set‐up with a smartphone and a dermatoscope for the objective estimation of skin erythema severity in atopic dogs. Animals Forty‐three dogs with AD. Methods and materials An erythema index (EI) was calculated from calibrated skin images and compared to the dermatologist’s erythema severity estimate using the erythema grading scale used in the CADESI‐04, as well as an ad hoc Visual Analog Scale (VAS) with a continuous palette of red shades. Results We found a strong correlation based on the Spearman rank correlation coefficient between all erythema valuations: CADESI‐04 and VAS: 0.93 [95% CI: (0.85, 0.96)]; CADESI‐04 and EI: 0.85 (0.72, 0.92); VAS and EI: 0.82 (0.67, 0.91). There was a good agreement between the objective EI and CADESI‐04‐based estimates because 71% of samples were classified in the same erythema severity category. When comparing the EI and the VAS, the standard deviation of misestimates was 12% (maximum 100%). Conclusions and clinical relevance The proposed optical set‐up has the potential to make erythema severity estimation objective, thus leading to more reliable AD severity scales for the use in experimental canine AD models or in clinical trials enrolling atopic dogs.

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Section: Discussionmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Evaluation of skin erythema severity by dermatoscopy in dogs with atopic dermatitis

Cugmas

Olivry

2021

Veterinary Dermatology

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“…In our colonoscopy video, a 0.5 s period yielded optimal results. As the standard setting, observing the colonoscopy video at a speed of 0.7 times to avoid misreading directions, participation of five fellow doctors, and selecting values more than three made the accuracy of the gold standard more reliable (Cho et al, 2016; Fallah & Niakan Kalhori, 2017; Cho et al, 2017)…”

Section: Discussionmentioning

confidence: 99%

Identification of cecum time-location in a colonoscopy video by deep learning analysis of colonoscope movement

Cho

Kim

Hong

et al. 2019

PeerJ

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Background Cecal intubation time is an important component for quality colonoscopy. Cecum is the turning point that determines the insertion and withdrawal phase of the colonoscope. For this reason, obtaining information related with location of the cecum in the endoscopic procedure is very useful. Also, it is necessary to detect the direction of colonoscope’s movement and time-location of the cecum. Methods In order to analysis the direction of scope’s movement, the Horn–Schunck algorithm was used to compute the pixel’s motion change between consecutive frames. Horn–Schunk-algorithm applied images were trained and tested through convolutional neural network deep learning methods, and classified to the insertion, withdrawal and stop movements. Based on the scope’s movement, the graph was drawn with a value of +1 for insertion, −1 for withdrawal, and 0 for stop. We regarded the turning point as a cecum candidate point when the total graph area sum in a certain section recorded the lowest. Results A total of 328,927 frame images were obtained from 112 patients. The overall accuracy, drawn from 5-fold cross-validation, was 95.6%. When the value of “t” was 30 s, accuracy of cecum discovery was 96.7%. In order to increase visibility, the movement of the scope was added to summary report of colonoscopy video. Insertion, withdrawal, and stop movements were mapped to each color and expressed with various scale. As the scale increased, the distinction between the insertion phase and the withdrawal phase became clearer. Conclusion Information obtained in this study can be utilized as metadata for proficiency assessment. Since insertion and withdrawal are technically different movements, data of scope’s movement and phase can be quantified and utilized to express pattern unique to the colonoscopist and to assess proficiency. Also, we hope that the findings of this study can contribute to the informatics field of medical records so that medical charts can be transmitted graphically and effectively in the field of colonoscopy.

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“…Currently, specialists sample skin images manually by selecting specific pixels or small areas [17,19,21]. On the other hand, there are a few semi-automated approaches [6,28], where the user would select erythematous and native skin areas with the representative erythematous skin being determined by the redness gradient-or fuzzy entropy-based algorithm.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Erythema Severity in Dermatoscopic Images of Canine Skin: Erythema Index Assessment and Image Sampling Reliability

Cugmas

Viškere

Štruc³

et al. 2021

Sensors

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The regular monitoring of erythema, one of the most important skin lesions in atopic (allergic) dogs, is essential for successful anti-allergic therapy. The smartphone-based dermatoscopy enables a convenient way to acquire quality images of erythematous skin. However, the image sampling to evaluate erythema severity is still done manually, introducing result variability. In this study, we investigated the correlation between the most popular erythema indices (EIs) and dermatologists’ erythema perception, and we measured intra- and inter-rater variability of the currently-used manual image-sampling methods (ISMs). We showed that the EIBRG, based on all three RGB (red, green, and blue) channels, performed the best with an average Spearman coefficient of 0.75 and a typical absolute disagreement of less than 14% with the erythema assessed by clinicians. On the other hand, two image-sampling methods, based on either selecting specific pixels or small skin areas, performed similarly well. They achieved high intra- and inter-rater reliability with the intraclass correlation coefficient (ICC) and Krippendorff’s alpha well above 0.90. These results indicated that smartphone-based dermatoscopy could be a convenient and precise way to evaluate skin erythema severity. However, better outlined, or even automated ISMs, are likely to improve the intra- and inter-rater reliability in severe erythematous cases.

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Development and clinical validation of a novel photography‐based skin erythema evaluation system: a comparison with the calculated consensus of dermatologists

Abstract: A new erythema evaluation system was developed and validated through CCAE, suggesting that the system can support dermatologists' objective diagnoses of erythema.

Cited by 5 publications

References 41 publications

Evaluation of skin erythema severity by dermatoscopy in dogs with atopic dermatitis

Evaluation of skin erythema severity by dermatoscopy in dogs with atopic dermatitis

Identification of cecum time-location in a colonoscopy video by deep learning analysis of colonoscope movement

Evaluation of Erythema Severity in Dermatoscopic Images of Canine Skin: Erythema Index Assessment and Image Sampling Reliability

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