Developing a Robust Estimator for Remote Optical Erythema Detection

Introduction: Erythema is redness of the skin or mucous membranes, which is symptomatic for any skin injury, infection, or inflammation. In some cases, it can be indicative of certain medical conditions (e.g., nonblanchable erythema in Stage I pressure injuries), and its detection can facilitate intervention at an earlier timepoint. The most common and effective means of erythema detection is a visual inspection of the skin. However, in many cases (especially for people with darkly pigmented skin), erythema can be masked by melanin. Moreover, it would be useful to have an automated delineation and measurement of erythema using consumer-grade devices, e.g., smartphones. It would facilitate automated symptom detection and measuring healing progress in various settings, including the patient's home. Aims: This study aims to evaluate and compare several algorithms that can be used for automated erythema detection using a smartphone's camera in clinical settings. Methods: We have compared three potential estimators, which can be derived from an RGB image: a) log(R/G), b) R-G, and c) a* channel in CIELAB color space. Here, R and G are red and green channels of an RGB image, respectively. Imaged skin was divided into two classes: erythema and nonerythema. The "erythema" class was seeded with pixels with E>mean(E)+z*st.dev(E), where E is the value of the estimator for a particular pixel, z is a model parameter (z-score). The erythema cluster was then grown by gradually adding nearby regions with an estimator E closer to the estimator’s mean of erythema cluster than the mean of the estimator for the normal skin area (K-Mean (K=2)). The segmentation algorithm was tested on a subset of labeled images from the Swift Medical proprietary wound imaging database. To evaluate algorithm performance, the results of segmentation were compared with ground truth, manually labeled images. To quantify results, sensitivity, specificity, and ROC curves were used. Results: We have found that all investigated estimators could provide reasonable sensitivity (>0.8) and specificity (>0.78). However, a* based estimator offers slightly better performance (0.86/0.84). Discussion: The preliminary data shows that smartphone cameras can delineate erythema with reasonable sensitivity and specificity. Further studies are required to correlate the accuracy with the skin type (melanin concentration in the skin).