Comfort and function remain key factors in upper limb prosthetic abandonment: findings of a scoping review.

PURPOSE: Rates of prosthetic device abandonment are dramatically high; however, the reasons behind abandonment are less understood. A scoping review was conducted to explore the current state of the literature on why individuals abandon upper limb prosthetic devices and consider how these reasons have evolved historically. MATERIALS AND METHODS: A systematic search of the literature identified 123 articles. After reviewing the articles using predetermined inclusion and exclusion criteria, nine relevant articles were included in the final review. The included articles covered passive, body-powered and myoelectric prosthetic devices. RESULTS: Across time, reasons for abandonment could be broadly categorized into comfort and function. Weight, temperature and perspiration were among the most common and persistent comfort-related reasons for abandonment. Regarding function, studies-reported abandonment was attributed to key concerns about control and sensory feedback, whereby participants may feel more functional without their device. CONCLUSIONS: In agreement with the previous literature, lack of comfort and function remain persistent reasons for upper limb prosthesis abandonment. Up-to-date research on reasons for abandonment of upper limb prosthetic devices is lacking, and recent prosthesis advancements have not been included in studies of device use, adoption and abandonment. Therefore, future work should explore reasons for abandonment in contemporary upper limb prosthetic devices. By understanding the reasons for prosthetic device abandonment, clinicians, therapists and researchers can use this information to proactively mitigate future upper limb prosthetic device abandonment. Findings from this review can be used to guide future prosthetic device development to improve these areas of concern and satisfy user needs.IMPLICATIONS FOR REHABILITATIONBy understanding the reasons for prosthetic device abandonment, clinicians, therapists and researchers can use this information to proactively mitigate future upper limb prosthetic device abandonment.The findings from this review can be used to guide future prosthetic device development to improve areas of concern and satisfy user needs.

Using Deep Learning Algorithms to Grade Hydronephrosis Severity: Toward a Clinical Adjunct.

Grading hydronephrosis severity relies on subjective interpretation of renal ultrasound images. Deep learning is a data-driven algorithmic approach to classifying data, including images, presenting a promising option for grading hydronephrosis. The current study explored the potential of deep convolutional neural networks (CNN), a type of deep learning algorithm, to grade hydronephrosis ultrasound images according to the 5-point Society for Fetal Urology (SFU) classification system, and discusses its potential applications in developing decision and teaching aids for clinical practice. We developed a five-layer CNN to grade 2,420 sagittal hydronephrosis ultrasound images [191 SFU 0 (8%), 407 SFU I (17%), 666 SFU II (28%), 833 SFU III (34%), and 323 SFU IV (13%)], from 673 patients ranging from 0 to 116.29 months old (M age = 16.53, SD = 17.80). Five-way (all grades) and two-way classification problems [i.e., II vs. III, and low (0-II) vs. high (III-IV)] were explored. The CNN classified 94% (95% CI, 93-95%) of the images correctly or within one grade of the provided label in the five-way classification problem. Fifty-one percent of these images (95% CI, 49-53%) were correctly predicted, with an average weighted F1 score of 0.49 (95% CI, 0.47-0.51). The CNN achieved an average accuracy of 78% (95% CI, 75-82%) with an average weighted F1 of 0.78 (95% CI, 0.74-0.82) when classifying low vs. high grades, and an average accuracy of 71% (95% CI, 68-74%) with an average weighted F1 score of 0.71 (95% CI, 0.68-0.75) when discriminating between grades II vs. III. Our model performs well above chance level, and classifies almost all images either correctly or within one grade of the provided label. We have demonstrated the applicability of a CNN approach to hydronephrosis ultrasound image classification. Further investigation into a deep learning-based clinical adjunct for hydronephrosis is warranted.