AIMC Topic: Middle Aged

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Predicting hospital readmission in patients with mental or substance use disorders: A machine learning approach.

International journal of medical informatics
OBJECTIVE: Mental or substance use disorders (M/SUD) are major contributors of disease burden with high risk for hospital readmissions. We sought to develop and evaluate a readmission model using a machine learning (ML) approach.

Deep learning with diffusion basis spectrum imaging for classification of multiple sclerosis lesions.

Annals of clinical and translational neurology
OBJECTIVE: Multiple sclerosis (MS) lesions are heterogeneous with regard to inflammation, demyelination, axonal injury, and neuronal loss. We previously developed a diffusion basis spectrum imaging (DBSI) technique to better address MS lesion heterog...

Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography.

European radiology experimental
BACKGROUND: Automatically detecting and quantifying pneumothorax on chest computed tomography (CT) may impact clinical decision-making. Machine learning methods published so far struggle with the heterogeneity of technical parameters and the presence...

Predicting in-hospital mortality of patients with febrile neutropenia using machine learning models.

International journal of medical informatics
BACKGROUND: Febrile neutropenia (FN) has been associated with high mortality among adults with cancer. Current systems for early detection of inpatient FN mortality are based on scoring indexes that require intensive physicians' subjective evaluation...

Deep learning assessment of breast terminal duct lobular unit involution: Towards automated prediction of breast cancer risk.

PloS one
Terminal duct lobular unit (TDLU) involution is the regression of milk-producing structures in the breast. Women with less TDLU involution are more likely to develop breast cancer. A major bottleneck in studying TDLU involution in large cohort studie...

Image Quality Assessment of Abdominal CT by Use of New Deep Learning Image Reconstruction: Initial Experience.

AJR. American journal of roentgenology
The purpose of this study was to perform quantitative and qualitative evaluation of a deep learning image reconstruction (DLIR) algorithm in contrast-enhanced oncologic CT of the abdomen. Retrospective review (April-May 2019) of the cases of adults...

Deep Learning Single-Frame and Multiframe Super-Resolution for Cardiac MRI.

Radiology
Background Cardiac MRI is limited by long acquisition times, yet faster acquisition of smaller-matrix images reduces spatial detail. Deep learning (DL) might enable both faster acquisition and higher spatial detail via super-resolution. Purpose To ex...

Deep Learning Predicts Total Knee Replacement from Magnetic Resonance Images.

Scientific reports
Knee Osteoarthritis (OA) is a common musculoskeletal disorder in the United States. When diagnosed at early stages, lifestyle interventions such as exercise and weight loss can slow OA progression, but at later stages, only an invasive option is avai...