Automatic SCOring of Atopic Dermatitis Using Deep Learning: A Pilot Study

Development of High-Quality Artificial Intelligence in Dermatology: Guidelines, Pitfalls, and Potential

“…(2022) , this is one method that may reduce inter-rater reliability. Although the results by Medela et al. (2022) show potential, with lesion segmentation annotation generally consistent across datasets, some visual signs such as edema and dryness were difficult to assess in photos, particularly in patients with darker skin tones.…”

“…Recently JID Innovations published a report by Medela et al (2022) describing an AI algorithm that was created to automate the SCORAD assessment using photographs. The question over inter-and intra-rater reliability using these assessments makes the question of ground truth and data labeling difficult.…”

“…Rigorous clinical trials are much needed to evaluate AI algorithms to understand whether they translate to the clinical settings in which they will be used ( Daneshjou, 2022a ; Han et al., 2022 ). In addition to evaluating algorithms that aid in diagnostic support, researchers are trying to study whether AI is able to delineate body surface area and localize lesions of several skin diseases, including vitiligo, psoriasis, and atopic dermatitis ( Guo et al., 2022b ; Han et al., 2022 ; Hurault et al., 2022 ; Medela et al., 2022 ; Schaap et al., 2022 ).…”

“…Recently, JID Innovations published a report by Medela et al. (2022) describing an AI algorithm that was created to automate the SCORAD assessment using photographs.…”

“…The study by Medela et al. (2022) utilized three image datasets, two comprised patients with Fitzpatrick skin types I‒III and one from patients with Fitzpatrick skin types IV‒VI.…”