Various scalp and hair disorders distress numerous people. Severe scalp hair disorders have an adverse effect on appearance, self-confidence, and quality of life. Therefore, early and exact diagnosis of various scalp hair disorders is important for timely treatment. However, conventional manual examination method is time-consuming, objective, and labor-intensive. The presented study proposes an intelligent healthcare platform for identifying severity levels of six common scalp hair disorders such as dryness, oiliness, erythema, folliculitis, dandruff, and hair loss. To establish a suitable scalp image classification model, we tested three deep learning models (ResNet-152, EfficientNet-B6, and ViT-B/16). Among the three tested deep learning models, the ViT-B/16 model exhibited the best classification performance with an average accuracy of 78.31%. In addition, the attention rollout method was applied to explain the decision of the trained ViT-B/16 model and highlight approximate lesion areas with no additional annotation procedure. Finally, Scalp checker software was developed based on the trained ViT-B/16 model and the attention rollout method. Accordingly, this proposed platform facilitates objective monitoring states of the scalp and early diagnosis of hairy scalp problems.

Pressure ulcers are serious healthcare concerns, especially for the elderly with reduced mobility. Severe pressure ulcers are accompanied by pain, degrading patients’ quality of life. Thus, speedy and accurate detection and classification of pressure ulcers are vital for timely treatment. The conventional visual examination method requires professional expertise for diagnosing pressure ulcer severity but it is difficult for the lay carer in domiciliary settings. In this study, we present a mobile healthcare platform incorporated with a light-weight deep learning model to exactly detect pressure ulcer regions and classify pressure ulcers into six severities such as stage 1–4, deep tissue pressure injury, and unstageable. YOLOv8 models were trained and tested using 2800 annotated pressure ulcer images. Among the five tested YOLOv8 models, the YOLOv8m model exhibited promising detection performance with overall classification accuracy of 84.6% and a mAP@50 value of 90.8%. The mobile application (app) was also developed applying the trained YOLOv8m model. The mobile app returned the diagnostic result within a short time (≒3 s). Accordingly, the proposed on-device AI app can contribute to early diagnosis and systematic management of pressure ulcers.

Surface irregularities can substantially modify local hemodynamics within coronary arteries. This study experimentally investigated the influence of controlled grooved surface geometries on pulsatile flow characteristics using idealized coronary-type stenosis models. Four transparent models were fabricated, consisting of a smooth control and three grooved surfaces with increasing groove amplitudes. Time-resolved particle image velocimetry was employed to quantify phase-averaged velocity fields, recirculation structures, and turbulent kinetic energy (TKE). The results showed that groove-induced surface irregularities enhanced jet deflection, near-wall recirculation, and in-plane cross-stream motion compared with the smooth stenosis model. As groove amplitude increased, downstream jet deflection became more pronounced, indicating a redistribution of velocity fluctuations in the post-stenotic region. In contrast, the smooth stenosis sustained a longer and more coherent jet structure. Increased groove amplitude also reduced the downstream extent and magnitude of measured planar TKE. These findings demonstrate that even small-scale surface features can significantly alter post-stenotic flow structures and the spatial development of turbulence under coronary-relevant pulsatile flow conditions.