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Background: Patients with stroke often experience weakened upper limbs, making daily tasks difficult to perform. Although rehabilitation devices are available, patients often relapse post-discharge due to insufficient practice. We present a home-based hand telerehabilitation intervention using the iManus™ platform comprising a sensorized glove, a mobile app for the patients, and a therapist portal for monitoring patient progress., Objectives: This research aimed to examine the feasibility, safety, and effectiveness of a home-based telerehabilitation intervention in improving hand function for individuals with mild stroke. A qualitative approach was also used to explore users' experiences, perceived benefits, and challenges associated with using the platform in a home setting., Methods: In this single-case study, we delivered a hand telerehabilitation intervention to a chronic stroke patient with impaired hand function using the iManus™ platform. The intervention consisted of 40 home sessions over eight weeks. We assessed feasibility through user adherence and feedback obtained using a System Usability Scale (SUS) and a semi-structured interview with the participant and their informal caregiver. Safety was evaluated by monitoring pain levels using the Visual Analog Scale (VAS), and efficacy was determined by observing the changes in the fingers' range of motion using the iManus™ platform and clinical outcomes measures, namely the Fugl-Meyer Assessment (FMA) and Jebsen Taylor Hand Function Test (JTHFT)., Results: Our participant completed all the assigned sessions, with each averaging 20 min. Usability scored 77.5 out of 100 on the SUS. User feedback from the interviews revealed improved mobility and control over therapy as benefits, indicating room for improvement in the intervention's adaptability and functionality. During the intervention, the participant noted no pain increase, and the telerehabilitation platform recorded range of motion improvements for all finger and wrist joints, excluding wrist extension. The FMA scores were 43 at T0, 53 at T1, and 56 at T2, while the JTHFT scores were 223 at T0, 188 at T1, and 240 at T2., Conclusions: This single case study demonstrated the preliminary feasibility, safety, and efficacy of a novel home-based hand intervention for stroke survivors. The participant showed improved hand functions, good adherence to the program, and reported satisfaction with the intervention. However, these results are based on a single-case study, and further large-scale studies are needed before any generalization is recommended., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

The detection of brain metastases (BM) in their early stages could have a positive impact on the outcome of cancer patients. The authors previously developed a framework for detecting small BM (with diameters of <15 mm) in T1-weighted contrast-enhanced 3D magnetic resonance images (T1c). This study aimed to advance the framework with a noisy-student-based self-training strategy to use a large corpus of unlabeled T1c data. Accordingly, a sensitivity-based noisy-student learning approach was formulated to provide high BM detection sensitivity with a reduced count of false positives. This paper (1) proposes student/teacher convolutional neural network architectures, (2) presents data and model noising mechanisms, and (3) introduces a novel pseudo-labeling strategy factoring in the sensitivity constraint. The evaluation was performed using 217 labeled and 1247 unlabeled exams via two-fold cross-validation. The framework utilizing only the labeled exams produced 9.23 false positives for 90% BM detection sensitivity, whereas the one using the introduced learning strategy led to ~9% reduction in false detections (i.e., 8.44). Significant reductions in false positives (>10%) were also observed in reduced labeled data scenarios (using 50% and 75% of labeled data). The results suggest that the introduced strategy could be utilized in existing medical detection applications with access to unlabeled datasets to elevate their performances.

Objective. Smoke, uneven lighting, and color deviation are common issues in endoscopic surgery, which have increased the risk of surgery and even lead to failure. Approach. In this study, we present a new physics model driven semi-supervised learning framework for high-quality pixel-wise endoscopic image enhancement, which is generalizable for smoke removal, light adjustment, and color correction. To improve the authenticity of the generated images, and thereby improve the network performance, we integrated specific physical imaging defect models with the CycleGAN framework. No ground-truth data in pairs are required. In addition, we propose a transfer learning framework to address the data scarcity in several endoscope enhancement tasks and improve the network performance. Main results. Qualitative and quantitative studies reveal that the proposed network outperforms the state-of-the-art image enhancement methods. In particular, the proposed method performs much better than the original CycleGAN, for example, the structural similarity improved from 0.7925 to 0.8648, feature similarity for color images from 0.8917 to 0.9283, and quaternion structural similarity from 0.8097 to 0.8800 in the smoke removal task. Experimental results of the proposed transfer learning method also reveal its superior performance when trained with small datasets of target tasks. Significance. Experimental results on endoscopic images prove the effectiveness of the proposed network in smoke removal, light adjustment, and color correction, showing excellent clinical usefulness., (© 2022 Institute of Physics and Engineering in Medicine.)

The advent of deep learning has pushed medical image analysis to new levels, rapidly replacing more traditional machine learning and computer vision pipelines. However segmenting and labelling anatomical regions remains challenging owing to appearance variations, imaging artifacts, the paucity and variability of annotated data, and the difficulty of fully exploiting domain constraints such as anatomical knowledge about inter-region relationships. We address the last point, improving the network's region-labeling consistency by introducing NonAdjLoss, an adjacency-graph based auxiliary training loss that penalizes outputs containing regions with anatomically-incorrect adjacency relationships. NonAdjLoss supports both fully-supervised training and a semi-supervised extension in which it is applied to unlabeled supplementary training data. The approach substantially reduces segmentation anomalies on the MICCAI-2012, IBSRv2 brain MRI datasets and the Anatomy3 whole body CT dataset, especially when semi-supervised training is included., (Copyright © 2019. Published by Elsevier B.V.)