AIMC Topic: Humans

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Deep learning-enhanced T mapping with spatial-temporal and physical constraint.

Magnetic resonance in medicine
PURPOSE: To propose a reconstruction framework to generate accurate T maps for a fast MR T mapping sequence.

A win-win situation: Does familiarity with a social robot modulate feedback monitoring and learning?

Cognitive, affective & behavioral neuroscience
Social species rely on the ability to modulate feedback-monitoring in social contexts to adjust one's actions and obtain desired outcomes. When being awarded positive outcomes during a gambling task, feedback-monitoring is attenuated when strangers a...

Mycophenolic Acid Exposure Prediction Using Machine Learning.

Clinical pharmacology and therapeutics
Therapeutic drug monitoring of mycophenolic acid (MPA) based on area under the curve (AUC) is well-established and machine learning (ML) approaches could help to estimate AUC. The aim of this work is to estimate the AUC of MPA in organ transplant pat...

Coming to Terms with the Black Box Problem: How to Justify AI Systems in Health Care.

The Hastings Center report
The use of opaque, uninterpretable artificial intelligence systems in health care can be medically beneficial, but it is often viewed as potentially morally problematic on account of this opacity-because the systems are black boxes. Alex John London ...

Multibeat echocardiographic phase detection using deep neural networks.

Computers in biology and medicine
BACKGROUND: Accurate identification of end-diastolic and end-systolic frames in echocardiographic cine loops is important, yet challenging, for human experts. Manual frame selection is subject to uncertainty, affecting crucial clinical measurements, ...

Deep learning-based auto-segmentation of organs at risk in high-dose rate brachytherapy of cervical cancer.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Delineation of organs at risk (OARs), such as the bladder, rectum and sigmoid, plays an important role in the delivery of optimal absorbed dose to the target owing to the steep gradient in high-dose rate brachytherapy (HDR-BT)...

Identification of pediatric respiratory diseases using a fine-grained diagnosis system.

Journal of biomedical informatics
Respiratory diseases, including asthma, bronchitis, pneumonia, and upper respiratory tract infection (RTI), are among the most common diseases in clinics. The similarities among the symptoms of these diseases precludes prompt diagnosis upon the patie...

Detecting Digoxin Toxicity by Artificial Intelligence-Assisted Electrocardiography.

International journal of environmental research and public health
Although digoxin is important in heart rate control, the utilization of digoxin is declining due to its narrow therapeutic window. Misdiagnosis or delayed diagnosis of digoxin toxicity is common due to the lack of awareness and the time-consuming lab...

Limits to visual representational correspondence between convolutional neural networks and the human brain.

Nature communications
Convolutional neural networks (CNNs) are increasingly used to model human vision due to their high object categorization capabilities and general correspondence with human brain responses. Here we evaluate the performance of 14 different CNNs compare...

Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data.

Nature communications
Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features ...