AI Medical Compendium Journal:
Medical engineering & physics

Showing 41 to 50 of 110 articles

Aided diagnosis of cervical spondylotic myelopathy using deep learning methods based on electroencephalography.

Medical engineering & physics
Cervical spondylotic myelopathy (CSM) is the most severe type of cervical spondylosis. It is challenging to achieve early diagnosis with current clinical diagnostic tools. In this paper, we propose an end-to-end deep learning approach for early diagn...

Mechanomyography signals pattern recognition in hand movements using swarm intelligence algorithm optimized support vector machine based on acceleration sensors.

Medical engineering & physics
On the basis of extracting mechanomyography (MMG) signal features, the classification of hand movements has certain application values in human-machine interaction systems and wearable devices. In this paper, pattern recognition of hand movements bas...

Chromosome classification via deep learning and its application to patients with structural abnormalities of chromosomes.

Medical engineering & physics
BACKGROUND AND OBJECTIVE: Karyotyping is an important technique in cytogenetic practice for the early diagnosis of genetic diseases. Clinical karyotyping is tedious, time-consuming, and error-prone. The objective of our study was to develop a single-...

Application of cluster repeated mini-batch training method to classify electroencephalography for grab and lift tasks.

Medical engineering & physics
Modern deep neural network training is based on mini-batch stochastic gradient optimization. While using extensive mini-batches improves the computational parallelism, the small batch training proved that it delivers improved generalization performan...

Non-invasive hemoglobin estimation from conjunctival images using deep learning.

Medical engineering & physics
Hemoglobin, a crucial protein found in erythrocytes, transports oxygen throughout the body. Deviations from optimal hemoglobin levels in the blood are linked to medical conditions, serving as diagnostic markers for certain diseases. The hemoglobin le...

Exploring the influence of nasal vestibule structure on nasal obstruction using CFD and Machine Learning method.

Medical engineering & physics
Motivated by clinical findings about the nasal vestibule, this study analyzes the aerodynamic characteristics of the nasal vestibule and attempt to determine anatomical features which have a large influence on airflow through a combination of Computa...

A hybrid orthosis combining functional electrical stimulation and soft robotics for improved assistance of drop-foot.

Medical engineering & physics
Drop-foot is characterised by an inability to lift the foot, and affects an estimated 3 million people worldwide. Current treatment methods include rigid splints, electromechanical systems, and functional electrical stimulation (FES). However, these ...

Accurate detection of arrhythmias on raw electrocardiogram images: An aggregation attention multi-label model for diagnostic assistance.

Medical engineering & physics
BACKGROUND: The low rate of detection of abnormalities has been a major problem with current artificial intelligence-based electrocardiogram diagnostic algorithms, particularly when applied under real-world clinical scenarios.

Glenohumeral joint trajectory tracking for improving the shoulder compliance of the upper limb rehabilitation robot.

Medical engineering & physics
BACKGROUND: Exoskeletons have become an important tool to help patients with upper extremity motor dysfunction in rehabilitation training and life assistance. In the study of the upper limb exoskeleton, the human glenohumeral joint will produce accom...

Heart disease prediction using IoT based framework and improved deep learning approach: Medical application.

Medical engineering & physics
Heart disease is the biggest cause of death in the globe. The method of predicting cardiac disease is exceedingly complex. It can only be done properly if the doctor has a lot of expertise and is well-versed in the condition. IoT-based illness predic...