AIMC Topic: Retrospective Studies

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Detecting lumbar lesions in Tc-MDP SPECT by deep learning: Comparison with physicians.

Medical physics
PURPOSE: Tc-MDP single-photon emission computed tomography (SPECT) is an established tool for diagnosing lumbar stress, a common cause of low back pain (LBP) in pediatric patients. However, detection of small stress lesions is complicated by the low...

Prediction model for thyrotoxic atrial fibrillation: a retrospective study.

BMC endocrine disorders
BACKGROUND: Thyrotoxic atrial fibrillation (TAF) is a recognized significant complication of hyperthyroidism. Early identification of the individuals predisposed to TAF would improve thyrotoxic patients' management. However, to our knowledge, an inst...

Machine learning for selecting patients with Crohn's disease for abdominopelvic computed tomography in the emergency department.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND: Patients with Crohn's disease (CD) frequently undergo abdominopelvic computed tomography (APCT) in the emergency department (ED). It's essential to diagnose clinically actionable findings (CAF) as they may need immediate intervention, fre...

Deep-learning based super-resolution for 3D isotropic coronary MR angiography in less than a minute.

Magnetic resonance in medicine
PURPOSE: To develop and evaluate a novel and generalizable super-resolution (SR) deep-learning framework for motion-compensated isotropic 3D coronary MR angiography (CMRA), which allows free-breathing acquisitions in less than a minute.

Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph.

Scientific reports
Triaging and prioritising patients for RT-PCR test had been essential in the management of COVID-19 in resource-scarce countries. In this study, we applied machine learning (ML) to the task of detection of SARS-CoV-2 infection using basic laboratory ...

A human-computer collaboration for COVID-19 differentiation: combining a radiomics model with deep learning and human auditing.

Annals of palliative medicine
BACKGROUND: This study aimed to build a radiomics model with deep learning (DL) and human auditing and examine its diagnostic value in differentiating between coronavirus disease 2019 (COVID-19) and community-acquired pneumonia (CAP).

Using machine learning to predict severe hypoglycaemia in hospital.

Diabetes, obesity & metabolism
AIM: To predict the risk of hypoglycaemia using machine-learning techniques in hospitalized patients.