AIMC Topic: Middle Aged

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Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events.

Scientific reports
Radiomics, quantitative feature extraction from radiological images, can improve disease diagnosis and prognostication. However, radiomic features are susceptible to image acquisition and segmentation variability. Ideally, only features robust to the...

Automatic deep learning-driven label-free image-guided patch clamp system.

Nature communications
Patch clamp recording of neurons is a labor-intensive and time-consuming procedure. Here, we demonstrate a tool that fully automatically performs electrophysiological recordings in label-free tissue slices. The automation covers the detection of cell...

A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation.

Journal of medical Internet research
BACKGROUND: Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk f...

Computer-aided Detection of Subsolid Nodules at Chest CT: Improved Performance with Deep Learning-based CT Section Thickness Reduction.

Radiology
Background Studies on the optimal CT section thickness for detecting subsolid nodules (SSNs) with computer-aided detection (CAD) are lacking. Purpose To assess the effect of CT section thickness on CAD performance in the detection of SSNs and to inve...

Classification of COVID-19 by Compressed Chest CT Image through Deep Learning on a Large Patients Cohort.

Interdisciplinary sciences, computational life sciences
Corona Virus Disease (COVID-19) has spread globally quickly, and has resulted in a large number of causalities and medical resources insufficiency in many countries. Reverse-transcriptase polymerase chain reaction (RT-PCR) testing is adopted as biops...

Machine learning prediction models for prognosis of critically ill patients after open-heart surgery.

Scientific reports
We aimed to build up multiple machine learning models to predict 30-days mortality, and 3 complications including septic shock, thrombocytopenia, and liver dysfunction after open-heart surgery. Patients who underwent coronary artery bypass surgery, a...

Using blood data for the differential diagnosis and prognosis of motor neuron diseases: a new dataset for machine learning applications.

Scientific reports
Early differential diagnosis of several motor neuron diseases (MNDs) is extremely challenging due to the high number of overlapped symptoms. The routine clinical practice is based on clinical history and examination, usually accompanied by electrophy...

Analysis of the nonperfused volume ratio of adenomyosis from MRI images based on fewshot learning.

Physics in medicine and biology
The nonperfused volume (NPV) ratio is the key to the success of high intensity focused ultrasound (HIFU) ablation treatment of adenomyosis. However, there are no qualitative interpretation standards for predicting the NPV ratio of adenomyosis using m...

Association Between Coffee Intake and Incident Heart Failure Risk: A Machine Learning Analysis of the FHS, the ARIC Study, and the CHS.

Circulation. Heart failure
BACKGROUND: Coronary heart disease, heart failure (HF), and stroke are complex diseases with multiple phenotypes. While many risk factors for these diseases are well known, investigation of as-yet unidentified risk factors may improve risk assessment...