Muscle strength and foot pressure vary depending on the type of foot pain.

This study compared muscle strength and foot pressure among patients with metatarsalgia, patients with plantar fasciitis, and healthy controls. A total of 31 patients with foot pain (14 metatarsalgia and 17 plantar fasciitis) and 29 healthy controls participated in the study. The strengths of the plantar flexor and hip muscles were measured using isokinetic and handheld dynamometers, respectively. Foot pressure parameters, including the pressure-time integral (PTI) and foot arch index (AI), were assessed using pedobarography. Compared with the healthy control group, plantar flexor strength was significantly reduced in the affected feet of the metatarsalgia and plantar fasciitis groups (F = 0.083, all p < 0.001); however, hip strength was significantly decreased only in the affected feet of the metatarsalgia group (F = 20.900, p < 0.001). Plantar flexor (p < 0.001) and hip (p = 0.004) strength were significantly lower in the metatarsalgia group than in the plantar fasciitis group. The PTI was lower in the forefeet of the affected feet in the metatarsalgia (p < 0.001) and plantar fasciitis (p = 0.004) groups. Foot AI (p < 0.001) was significantly reduced only in the metatarsalgia group. These results suggest the need to consider the evaluation of muscle strength and foot pressure in both feet for the diagnosis and treatment of foot pain.

Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver.

PURPOSE: Volumetry is used in polycystic kidney and liver diseases (PKLDs), including autosomal dominant polycystic kidney disease (ADPKD), to assess disease progression and drug efficiency. However, since no rapid and accurate method for volumetry has been developed, volumetry has not yet been established in clinical practice, hindering the development of therapies for PKLD. This study presents an artificial intelligence (AI)-based volumetry method for PKLD. MATERIALS AND METHODS: The performance of AI was first evaluated in comparison with ground-truth (GT). We trained a V-net-based convolutional neural network on 175 ADPKD computed tomography (CT) segmentations, which served as the GT and were agreed upon by 3 experts using images from 214 patients analyzed with volumetry. The dice similarity coefficient (DSC), interobserver correlation coefficient (ICC), and Bland-Altman plots of 39 GT and AI segmentations in the validation set were compared. Next, the performance of AI on the segmentation of 50 random CT images was compared with that of 11 PKLD specialists based on the resulting DSC and ICC. RESULTS: The DSC and ICC of the AI were 0.961 and 0.999729, respectively. The error rate was within 3% for approximately 95% of the CT scans (error<1%, 46.2%; 1%≤error<3%, 48.7%). Compared with the specialists, AI showed moderate performance. Furthermore, an outlier in our results confirmed that even PKLD specialists can make mistakes in volumetry. CONCLUSIONS: PKLD volumetry using AI was fast and accurate. AI performed comparably to human specialists, suggesting its use may be practical in clinical settings.

Evaluation on Air Purifier's Performance in Reducing the Concentration of Fine Particulate Matter for Occupants according to its Operation Methods.

Fine particulate matter entering the body through breathing cause serious damage to humans. In South Korea, filter-type air purifiers are used to eliminate indoor fine particulate matter, and there has been a broad range of studies on the spread of fine particulate matter and air purifiers. However, earlier studies have not evaluated an operating method of air purifiers considering the inflow of fine particulate matter into the body or reduction performance of the concentration of fine particulate matter. There is a limit to controlling the concentration of fine particulate matter of the overall space where an air purifier is fixed in one spot as the source of indoor fine particulate matter is varied. Accordingly, this study analyzed changes in the concentration of indoor fine particulate matter through an experiment according to the discharging method and location of a fixed air purifier considering the inflow route of fine particulate matter into the body and their harmfulness. The study evaluated the purifiers' performance in reducing the concentration of fine particulate matter in the occupants' breathing zone according to the operation method in which a movable air purifier responds to the movement of occupants. The results showed the concentration of fine particulate matter around the breathing zone of the occupants had decreased by about 51 µg/m3 compared to the surrounding concentration in terms of the operating method in which an air purifier tracks occupants in real-time, and a decrease of about 68 µg/m3 in terms of the operating method in which an air purifier controls the zone. On the other hand, a real-time occupant tracking method may face a threshold due to the moving path of an air purifier and changes in the number of occupants. A zone controlling method is deemed suitable as an operating method of a movable air purifier to reduce the concentration of fine particulate matter in the breathing zone of occupants.