AIMC Topic: Humans

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On lightmyography based muscle-machine interfaces for the efficient decoding of human gestures and forces.

Scientific reports
Conventional muscle-machine interfaces like Electromyography (EMG), have significant drawbacks, such as crosstalk, a non-linear relationship between the signal and the corresponding motion, and increased signal processing requirements. In this work, ...

Hospital crowdedness evaluation and in-hospital resource allocation based on image recognition technology.

Scientific reports
How to allocate the existing medical resources reasonably, alleviate hospital congestion and improve the patient experience are problems faced by all hospitals. At present, the combination of artificial intelligence and the medical field is mainly in...

Predicting decompression surgery by applying multimodal deep learning to patients' structured and unstructured health data.

BMC medical informatics and decision making
BACKGROUND: Low back pain (LBP) is a common condition made up of a variety of anatomic and clinical subtypes. Lumbar disc herniation (LDH) and lumbar spinal stenosis (LSS) are two subtypes highly associated with LBP. Patients with LDH/LSS are often s...

A fully automatic target detection and quantification strategy based on object detection convolutional neural network YOLOv3 for one-step X-ray image grading.

Analytical methods : advancing methods and applications
Methods for automatic image analysis are demanded for dealing with the explosively increased imaging data in clinics. Osteoarthritis (OA) is a typical disease diagnosed based on X-ray imaging. Herein, we propose a novel modeling strategy based on YOL...

Decoding Deep Learning applications for diagnosis and treatment planning.

Dental press journal of orthodontics
INTRODUCTION: Artificial Intelligence (AI), Machine Learning and Deep Learning are playing an increasingly significant role in the medical field in the 21st century. These recent technologies are based on the concept of creating machines that have th...

Early experience with low-pass filtered images facilitates visual category learning in a neural network model.

PloS one
Humans are born with very low contrast sensitivity, meaning that inputs to the infant visual system are both blurry and low contrast. Is this solely a byproduct of maturational processes or is there a functional advantage for beginning life with poor...

Prostatic urinary tract visualization with super-resolution deep learning models.

PloS one
In urethra-sparing radiation therapy, prostatic urinary tract visualization is important in decreasing the urinary side effect. A methodology has been developed to visualize the prostatic urinary tract using post-urination magnetic resonance imaging ...

Deep learning prediction of pathological complete response, residual cancer burden, and progression-free survival in breast cancer patients.

PloS one
The goal of this study was to employ novel deep-learning convolutional-neural-network (CNN) to predict pathological complete response (PCR), residual cancer burden (RCB), and progression-free survival (PFS) in breast cancer patients treated with neoa...

Classification of BCI Multiclass Motor Imagery Task Based on Artificial Neural Network.

Clinical EEG and neuroscience
Motor imagery (MI) signals recorded by electroencephalography provide the most practical basis for conceiving brain-computer interfaces (BCI). These interfaces offer a high degree of freedom. This helps people with motor disabilities communicate with...

A review of research on eligibility criteria for clinical trials.

Clinical and experimental medicine
The purpose of this paper is to systematically sort out and analyze the cutting-edge research on the eligibility criteria of clinical trials. Eligibility criteria are important prerequisites for the success of clinical trials. It directly affects the...