AIMC Topic: Humans

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Clinician perceptions of a novel wearable robotic hand orthosis for post-stroke hemiparesis.

Disability and rehabilitation
PURPOSE: Wearable robotic devices are currently being developed to improve upper limb function for individuals with hemiparesis after stroke. Incorporating the views of clinicians during the development of new technologies can help ensure that end pr...

AI-assisted Segmentation Tool for Brain Tumor MR Image Analysis.

Journal of imaging informatics in medicine
TumorPrism3D software was developed to segment brain tumors with a straightforward and user-friendly graphical interface applied to two- and three-dimensional brain magnetic resonance (MR) images. The MR images of 185 patients (103 males, 82 females)...

CFINet: Cross-Modality MRI Feature Interaction Network for Pseudoprogression Prediction of Glioblastoma.

Journal of computational biology : a journal of computational molecular cell biology
Pseudoprogression (PSP) is a related reaction of glioblastoma treatment, and misdiagnosis can lead to unnecessary intervention. Magnetic resonance imaging (MRI) provides cross-modality images for PSP prediction studies. However, how to effectively us...

Surface electromyography vs clinical outcome measures after robot-assisted gait training in patients with spinal cord injury after post-acute phase of rehabilitation.

Annals of agricultural and environmental medicine : AAEM
INTRODUCTION AND OBJECTIVE: Surface electromyography (sEMG) measurements are a valid method for sublesional muscle activity following spinal cord injury (SCI). In the literature there are few reports evaluating the effect of robotic assisted gait tra...

Machine learning: implications and applications for ambulatory anesthesia.

Current opinion in anaesthesiology
PURPOSE OF REVIEW: This review explores the timely and relevant applications of machine learning in ambulatory anesthesia, focusing on its potential to optimize operational efficiency, personalize risk assessment, and enhance patient care.

Generative Pre-trained Transformer for Pediatric Stroke Research: A Pilot Study.

Pediatric neurology
BACKGROUND: Pediatric stroke is an important cause of morbidity in children. Although research can be challenging, large amounts of data have been captured through collaborative efforts in the International Pediatric Stroke Study (IPSS). This study e...

Broad learning system based on maximum multi-kernel correntropy criterion.

Neural networks : the official journal of the International Neural Network Society
The broad learning system (BLS) is an effective machine learning model that exhibits excellent feature extraction ability and fast training speed. However, the traditional BLS is derived from the minimum mean square error (MMSE) criterion, which is h...

PreDBP-PLMs: Prediction of DNA-binding proteins based on pre-trained protein language models and convolutional neural networks.

Analytical biochemistry
The recognition of DNA-binding proteins (DBPs) is the crucial step to understanding their roles in various biological processes such as genetic regulation, gene expression, cell cycle control, DNA repair, and replication within cells. However, conven...

Prognostic prediction model for salivary gland carcinoma based on machine learning.

International journal of oral and maxillofacial surgery
Although rare overall, salivary gland carcinomas (SGCs) are among the most common oral and maxillofacial malignancies. The aim of this study was to develop a machine learning-based model to predict the survival of patients with SGC. Patients in whom ...