ABSTRACT
Accurate and reproducible color capture is vital in medical photography. Camera distance and angle are particularly important as they are highly variable in a clinical setting. To account for variability in illumination, camera technology, and geometric effects, color standards are often used for color correction. To explore how geometry affects color, we quantified the change in CIELAB color value of a color standard for diverse skin tones at varying smartphone camera distances and angles. Whereas both chromaticity (a* and b*) and lightness (L*) were affected by angle, distance only affected L* (standard error of measurement, SEM > 1 CIELAB unit). Flash usage did not generally reduce distance and angle associated variability. Compared to compressed (JPG) format, raw (DNG) images had decreased median variability across different distances and angles. These findings suggest that in medical photography, inconsistent camera distance and angle can increase variability in photographed skin appearance over time.
Subject(s)
Skin Pigmentation , Smartphone , Color , LightingABSTRACT
Background: Improved techniques for lymphedema detection and monitoring of disease progression are needed. This study aims to use the noninvasive MyotonPRO Device to detect differences in biomechanical skin characteristics in patients with breast cancer-related lymphedema (BCRL). Methods: The handheld Myoton device was used to measure skin parameters including dynamic skin stiffness, oscillation frequency (tone), mechanical stress relaxation time, and creep in 11 women diagnosed with BCRL. Seven anatomical sites were measured bilaterally for each participant. The average values in the affected arms were compared with those in the contralateral unaffected arms. Results: Among the 11 female participants with unilateral BCRL Stages 0 to II, the combined averages for dynamic skin stiffness and frequency measurements were decreased in the affected arms when compared with those for the contralateral control arms (ratio < 1). The median ratio of stiffness (affected to unaffected control arm) was 0.91 (interquartile range [IQR] 0.78-1.03) while frequency was 0.94 (IQR 0.89-1.0). Skin relaxation time and creep averages were increased in the affected arms. The relaxation time median ratio (affected to unaffected control arm) was 1.07 (IQR 1.02-1.14) and the median ratio of creep was 1.06 (IQR 1.03-1.16). Conclusions: This study suggests the Myoton can detect differences in skin biomechanical parameters of the affected and unaffected arms in patients with BCRL. Larger studies are needed to draw strong conclusions.
ABSTRACT
Skin biomechanical parameters (dynamic stiffness, frequency, relaxation time, creep, and decrement) measured using a myotonometer (MyotonPRO) could inform management of sclerotic disease. To determine which biomechanical parameter(s) can accurately differentiate sclerotic chronic graft-versus-host disease (cGVHD) patients from post-hematopoietic cell transplant (post-HCT) controls, 15 sclerotic cGVHD patients and 11 post-HCT controls were measured with the myotonometer on 18 anatomic sites. Logistic regression and two machine learning algorithms, LASSO regression and random forest, were developed to classify subjects. In univariable analysis, frequency had the highest overfit-corrected area under the receiver operating characteristic curve (AUC 0.91). Backward stepwise selection and random forest machine learning identified frequency and relaxation time as the optimal parameters for differentiating sclerotic cGVHD patients from post-HCT controls. LASSO regression selected the combination of frequency and relaxation time (overfit-corrected AUC 0.87). Discriminatory ability was maintained when only the sites accessible while the patient is supine (12 sites) were used. We report the distribution of values for these highly discriminative biomechanical parameters, which could inform assessment of disease severity in future quantitative biomechanical studies of sclerotic cGVHD.
ABSTRACT
Cutaneous erythema is used in diagnosis and response assessment of cutaneous chronic graft-versus-host disease (cGVHD). The development of objective erythema evaluation methods remains a challenge. We used a pre-trained neural network to segment cGVHD erythema by detecting changes relative to a patient's registered baseline photo. We fixed this change detection algorithm on human annotations from a single photo pair, by using either a traditional approach or by marking definitely affected ("Do Not Miss", DNM) and definitely unaffected skin ("Do Not Include", DNI). The fixed algorithm was applied to each of the remaining 47 test photo pairs from six follow-up sessions of one patient. We used both the Dice index and the opinion of two board-certified dermatologists to evaluate the algorithm performance. The change detection algorithm correctly assigned 80% of the pixels, regardless of whether it was fixed on traditional (median accuracy: 0.77, interquartile range 0.62-0.87) or DNM/DNI segmentations (0.81, 0.65-0.89). When the algorithm was fixed on markings by different annotators, the DNM/DNI achieved more consistent outputs (median Dice indices: 0.94-0.96) than the traditional method (0.73-0.81). Compared to viewing only rash photos, the addition of baseline photos improved the reliability of dermatologists' scoring. The inter-rater intraclass correlation coefficient increased from 0.19 (95% confidence interval lower bound: 0.06) to 0.51 (lower bound: 0.35). In conclusion, a change detection algorithm accurately assigned erythema in longitudinal photos of cGVHD. The reliability was significantly improved by exclusively using confident human segmentations to fix the algorithm. Baseline photos improved the agreement among two dermatologists in assessing algorithm performance.
