application-bedside-lower-extremity-rehabilitation-robots-stroke

Application of Bedside Lower Extremity Rehabilitation Robots in Stroke Rehabilitation: A Randomized Controlled Trial

Stroke remains one of the leading causes of disability worldwide. Bedside lower limb rehabilitation robots are increasingly applied to enhance early motor ...

soft-hip-exoskeleton-reduces-physiological-cost-perceived-exertion

Soft Hip Exoskeleton Reduces Physiological Cost and Perceived Exertion In Older Adults During Uphill Walking

Mountain hiking and mountaineering are common activities for active older adults to maintain cardiovascular fitness; however, the high physiological ...

Zhejiang University School of Medicine

The First Affiliated Hospital, Zhejiang University School of Medicine

As the demand for health grows, the increase in medical waste generation is gradually outstripping the load. In this paper, we propose a deep learning approach for identification and classification of medical waste. Deep learning is currently the most popular technique in image classification, but its need for large amounts of data limits its usage. In this scenario, we propose a deep learning-based classification method, in which ResNeXt is a suitable deep neural network for practical implementation, followed by transfer learning methods to improve classification results. We pay special attention to the problem of medical waste classification, which needs to be solved urgently in the current environmental protection context. We applied the technique to 3480 images and succeeded in correctly identifying 8 kinds of medical waste with an accuracy of 97.2%; the average F1-score of five-fold cross-validation was 97.2%. This study provided a deep learning-based method for automatic detection and classification of 8 kinds of medical waste with high accuracy and average precision. We believe that the power of artificial intelligence could be harnessed in products that would facilitate medical waste classification and could become widely available throughout China.

A deep learning approach for medical waste classification.

Carpal tunnel syndrome (CTS) is a common peripheral nerve disease in adults; it can cause pain, numbness, and even muscle atrophy and will adversely affect patients' daily life and work. There are no standard diagnostic criteria that go against the early diagnosis and treatment of patients. MRI as a novel imaging technique can show the patient's condition more objectively, and several characteristics of carpal tunnel syndrome have been found. However, various image sequences, heavy artifacts, small lesion characteristics, high volume of imagine reading, and high difficulty in MRI interpretation limit its application in clinical practice. With the development of automatic image segmentation technology, the algorithm has great potential in medical imaging. The challenge is that the segmentation target is too small, and there are two categories of images with the proximal border of the carpal tunnel as the boundary. To meet the challenge, we propose an end-to-end deep learning framework called Deep CTS to segment the carpal tunnel from the MR image. The Deep CTS consists of the shape classifier with a simple convolutional neural network and the carpal tunnel region segmentation with simplified U-Net. With the specialized structure for the carpal tunnel, Deep CTS can segment the carpal tunnel region efficiently and improve the intersection over union of results. The experimental results demonstrated that the performance of the proposed deep learning framework is better than other segmentation networks for small objects. We trained the model with 333 images, tested it with 82 images, and achieved 0.63 accuracy of intersection over union and 0.17 s segmentation efficiency, which indicate great promise for the clinical application of this algorithm.

ArticleTotal : 2	Year
Publication title	2025
Publication title	2026

Article	Year
A deep learning approach for medical waste classification. Scientific reports\| PubMed ID: 35140263	2022
Deep CTS: a Deep Neural Network for Identification MRI of Carpal Tunnel Syndrome. Journal of digital imaging\| PubMed ID: 35661280	2022

Binjie Qi

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