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Offloading plantar pressure in a diabetic foot with neuropathy is challenging in conventional clinical practice. Custom-made insole (CMI) materials play an important role in plantar pressure reduction, but the assessment is costly and time-consuming. Finite element analysis (FEA) can provide an efficient evaluation of different insoles on the plantar pressure distribution. This study investigated the effect of CMI materials and their combinations on plantar pressure reduction for the diabetic foot with neuropathy using FEA. The study was conducted by constructing a three-dimensional foot model along with CMI to study the peak contact pressure between the foot and CMI. The softer material (E = 5 MPa) resulted in a better reduction of peak contact pressure compared with the stiffer material (E = 11 MPa). The plantar pressure was well redistributed with softer material compared with the stiffer material and its combination. In addition, the single softer material resulted in reduced frictional stress under the first metatarsal head compared with the stiffer material and the combination of materials. The softer material and its combination have a beneficial effect on plantar pressure reduction and redistribution for a diabetic foot with neuropathy. This study provided an effective approach for CMI material selection using FEA.

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Wound assessment is essential for evaluating wound healing. One cornerstone of wound care practice is the use of clinical guidelines that mandate regular documentation, including wound size and wound tissue composition, to determine the rate of wound healing. The traditional method requires wound care professionals to manually measure the wound area and tissue composition, which is time-consuming, costly, and difficult to reproduce. In this work, we propose an approach for automatic wound assessment that incorporates automatic color and measurement calibration and artificial intelligence algorithms. Our approach enables the comparison of images taken at different times, even if they were taken under different lighting conditions, distances, lenses, and camera sensors. We designed a calibration chart and developed automatic algorithms for color and measurement calibration. The wound area and wound composition on the images were annotated by three physicians with more than ten years of experience. Deep learning models were then developed to mimic what the physicians did on the images. We examined two network variants, U-Net with EfficientNet and U-Net with MobileNetV2, on wound images with a size of 1024 × 1024 pixels. Our best-performing algorithm achieved a mean intersection over union (IoU) of 0.6964, 0.3957, 0.6421, and 0.1552 for segmenting a wound area, epithelialization area, granulation tissue, and necrotic tissue, respectively. Our approach was able to accurately segment the wound area and granulation tissue but was inconsistent with respect to the epithelialization area and necrotic tissue. The calibration chart, which helps calibrate colors and scales, improved the performance of the algorithm. The approach could provide a thorough assessment of the wound, which could help clinicians tailor treatment to the patient’s condition.

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