AIMC Topic: Humans

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Deep neural network-estimated age using optical coherence tomography predicts mortality.

GeroScience
The concept of biological age has emerged as a measurement that reflects physiological and functional decline with ageing. Here we aimed to develop a deep neural network (DNN) model that predicts biological age from optical coherence tomography (OCT)...

A look at radiation detectors and their applications in medical imaging.

Japanese journal of radiology
The effectiveness and precision of disease diagnosis and treatment have increased, thanks to developments in clinical imaging over the past few decades. Science is developing and progressing steadily in imaging modalities, and effective outcomes are ...

Diagnosis of diabetes mellitus using high frequency ultrasound and convolutional neural network.

Ultrasonics
The incidence of diabetes mellitus has been increasing, prompting the search for non-invasive diagnostic methods. Although current methods exist, these have certain limitations, such as low reliability and accuracy, difficulty in individual patient a...

Evaluation of height prediction models: from traditional methods to artificial intelligence.

Pediatric research
BACKGROUND: Traditional methods for predicting adult height (AHP) rely on manual readings of bone age (BA). However, the incorporation of artificial intelligence has recently improved the accuracy of BA readings and their incorporation into AHP model...

No Benefit of Robotic-Assisted over Computer-Assisted Surgery for Achieving Neutral Coronal Alignment in Total Knee Arthroplasty.

The journal of knee surgery
The use of robotic-assisted surgery (RAS) in total knee arthroplasty (TKA) is becoming increasingly popular due to better precision, potentially superior outcomes and the ability to achieve alternative alignment strategies. The most commonly used ali...

Machine Learning Algorithms Predict Long-Term Postoperative Opioid Misuse: A Systematic Review.

The American surgeon
INTRODUCTION: A steadily rising opioid pandemic has left the US suffering significant social, economic, and health crises. Machine learning (ML) domains have been utilized to predict prolonged postoperative opioid (PPO) use. This systematic review ai...

Potent multi-target natural inhibitors against SARS-CoV-2 from medicinal plants of the Himalaya: a discovery from hybrid machine learning, chemoinformatics, and simulation assisted screening.

Journal of biomolecular structure & dynamics
The emergence and immune evasion ability of SARS-CoV-2 Omicron strains, mainly BA.5.2 and BF.7 and other variants of concern have raised global apprehensions. With this context, the discovery of multitarget inhibitors may be proven more comprehensive...

Mortality prediction using medical time series on TBI patients.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Traumatic Brain Injury (TBI) is one of the leading causes of injury-related mortality in the world, with severe cases reaching mortality rates of 30-40%. It is highly heterogeneous both in causes and consequences making more...

Adaptive machine learning method for photoacoustic computed tomography based on sparse array sensor data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Photoacoustic computed tomography (PACT) is a non-invasive biomedical imaging technology that has developed rapidly in recent decades, especially has shown potential for small animal studies and early diagnosis of human dise...

Tumor Mutation Burden-Related Histopathologic Features for Predicting Overall Survival in Gliomas Using Graph Deep Learning.

The American journal of pathology
Tumor mutation burden (TMB) is a potential biomarker for evaluating the prognosis and response to immune checkpoint inhibitors, but its costly and time-consuming method of measurement limits its widespread application. This study aimed to identify th...