AIMC Topic: Middle Aged

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Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: a multicenter randomized controlled trial.

Journal of gastroenterology
BACKGROUND: We have developed the computer-aided detection (CADe) system using an original deep learning algorithm based on a convolutional neural network for assisting endoscopists in detecting colorectal lesions during colonoscopy. The aim of this ...

PassFlow: a multimodal workflow for predicting deep brain stimulation outcomes.

International journal of computer assisted radiology and surgery
PURPOSE: Deep Brain Stimulation (DBS) is a proven therapy for Parkinson's Disease (PD), frequently resulting in an enhancement of motor function. Nonetheless, several undesirable side effects can occur after DBS, which can worsen the quality of life ...

Robot-mediated overground gait training for transfemoral amputees with a powered bilateral hip orthosis: a pilot study.

Journal of neuroengineering and rehabilitation
BACKGROUND: Transfemoral amputation is a serious intervention that alters the locomotion pattern, leading to secondary disorders and reduced quality of life. The outcomes of current gait rehabilitation for TFAs seem to be highly dependent on factors ...

Self-Supervised Feature Learning and Phenotyping for Assessing Age-Related Macular Degeneration Using Retinal Fundus Images.

Ophthalmology. Retina
OBJECTIVE: Diseases such as age-related macular degeneration (AMD) are classified based on human rubrics that are prone to bias. Supervised neural networks trained using human-generated labels require labor-intensive annotations and are restricted to...

A machine learning based exploration of COVID-19 mortality risk.

PloS one
Early prediction of patient mortality risks during a pandemic can decrease mortality by assuring efficient resource allocation and treatment planning. This study aimed to develop and compare prognosis prediction machine learning models based on invas...

Comparison of deep learning, radiomics and subjective assessment of chest CT findings in SARS-CoV-2 pneumonia.

Clinical imaging
PURPOSE: Comparison of deep learning algorithm, radiomics and subjective assessment of chest CT for predicting outcome (death or recovery) and intensive care unit (ICU) admission in patients with severe acute respiratory syndrome coronavirus 2 (SARS-...

Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment.

Computers in biology and medicine
Ultrasound imaging is a patient-friendly and robust technique for studying physiological and pathological muscles. An automatic deep learning (DL) system for the analysis of ultrasound images could be useful to support an expert operator, allowing th...