AIMC Topic: Humans

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Applying Machine Learning Techniques in Nomogram Prediction and Analysis for SMILE Treatment.

American journal of ophthalmology
PURPOSE: To analyze the outcome of machine learning technique for prediction of small incision lenticule extraction (SMILE) nomogram.

Accurate and robust segmentation of neuroanatomy in T1-weighted MRI by combining spatial priors with deep convolutional neural networks.

Human brain mapping
Neuroanatomical segmentation in magnetic resonance imaging (MRI) of the brain is a prerequisite for quantitative volume, thickness, and shape measurements, as well as an important intermediate step in many preprocessing pipelines. This work introduce...

Precision screening for familial hypercholesterolaemia: a machine learning study applied to electronic health encounter data.

The Lancet. Digital health
BACKGROUND: Cardiovascular outcomes for people with familial hypercholesterolaemia can be improved with diagnosis and medical management. However, 90% of individuals with familial hypercholesterolaemia remain undiagnosed in the USA. We aimed to accel...

What microsurgeon, orthopaedic and plastic surgeon should know about bionic hand.

Injury
Hand loss is a catastrophic event that generates significant demands for orthopedics and prosthetics. In the course of history, prostheses evolved from passive esthetic replacements to sophisticated robotic hands. Yet, their actuation and particularl...

Deep learning in the detection of high-grade glioma recurrence using multiple MRI sequences: A pilot study.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
The identification of high-grade glioma (HGG) progression may pose a diagnostic dilemma due to similar appearances of treatment-related changes (TRC) (e.g. pseudoprogression or radionecrosis). Deep learning (DL) may be able to assist with this task. ...

Preparing for Artificial Intelligence: Systems-Level Implications for the Medical Imaging and Radiation Therapy Professions.

Journal of medical imaging and radiation sciences
Innovations in artificial intelligence (AI) are driving a new industrial revolution, and as a result, the medical radiation sciences is experiencing transformational, open, beneficial, yet disruptive changes. Many studies have already been published ...

Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning.

Proceedings of the National Academy of Sciences of the United States of America
Computed tomography (CT) of the head is used worldwide to diagnose neurologic emergencies. However, expertise is required to interpret these scans, and even highly trained experts may miss subtle life-threatening findings. For head CT, a unique chall...

Discrimination of the behavioural dynamics of visually impaired infants via deep learning.

Nature biomedical engineering
Sensory loss is associated with behavioural changes, but how behavioural dynamics change when a sensory modality is impaired remains unclear. Here, by recording under a designed standardized scenario, the behavioural phenotypes of 4,196 infants who e...

A Machine Learning-Based Prediction Platform for P-Glycoprotein Modulators and Its Validation by Molecular Docking.

Cells
P-glycoprotein (P-gp) is an important determinant of multidrug resistance (MDR) because its overexpression is associated with increased efflux of various established chemotherapy drugs in many clinically resistant and refractory tumors. This leads to...

The FluPRINT dataset, a multidimensional analysis of the influenza vaccine imprint on the immune system.

Scientific data
Machine learning has the potential to identify novel biological factors underlying successful antibody responses to influenza vaccines. The first attempts have revealed a high level of complexity in establishing influenza immunity, and many different...