AIMC Topic: Retrospective Studies

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A weakly supervised deep learning model integrating noncontrasted computed tomography images and clinical factors facilitates haemorrhagic transformation prediction after intravenous thrombolysis in acute ischaemic stroke patients.

Biomedical engineering online
BACKGROUND: Haemorrhage transformation (HT) is a serious complication of intravenous thrombolysis (IVT) in acute ischaemic stroke (AIS). Accurate and timely prediction of the risk of HT before IVT may change the treatment decision and improve clinica...

Clinical evaluation of a deep-learning model for automatic scoring of the Alberta stroke program early CT score on non-contrast CT.

Journal of neurointerventional surgery
BACKGROUND: Automated measurement of the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) can support clinical decision making. Based on a deep learning algorithm, we developed an automated ASPECTS scoring system (Heuron ASPECTS) and ...

Artificial Intelligence-Based Emphysema Quantification in Routine Chest Computed Tomography: Correlation With Spirometry and Visual Emphysema Grading.

Journal of computer assisted tomography
OBJECTIVE: The aim of the study is to assess the correlation between artificial intelligence (AI)-based low attenuation volume percentage (LAV%) with forced expiratory volume in the first second to forced vital capacity (FEV1/FVC) and visual emphysem...

Semiautomatic Assessment of Facet Tropism From Lumbar Spine MRI Using Deep Learning: A Northern Finland Birth Cohort Study.

Spine
STUDY DESIGN: This is a retrospective, cross-sectional, population-based study that automatically measured the facet joint (FJ) angles from T2-weighted axial magnetic resonance imagings (MRIs) of the lumbar spine using deep learning (DL).

A Novel Technique of Arthroscopic-Assisted Four-Corner Fusion and Robot-Assisted Fixation for Scaphoid Nonunion Advanced Collapse Wrist: A Single Case Study.

Orthopaedic surgery
OBJECTIVE: Scaphoid nonunion advanced collapse (SNAC) is a relatively common and debilitating wrist disorder yet its treatment remains challenging and controversial. We aim to describe a novel technique of a dual arthroscopic and robotic assisted fou...

A convenient machine learning model to predict full stomach and evaluate the safety and comfort improvements of preoperative oral carbohydrate in patients undergoing elective painless gastrointestinal endoscopy.

Annals of medicine
BACKGROUND AND AIMS: Assessment of the patient's gastric contents is the key to avoiding aspiration incidents, however, there is no effective method to determine whether elective painless gastrointestinal endoscopy (GIE) patients have a full stomach ...

Early-stage neutralizing antibody level associated with the re-positive risk of Omicron SARS-CoV-2 RNA in patients recovered from COVID-19.

Diagnostic microbiology and infectious disease
Post-discharge re-positivity of Omicron SARS-CoV-2 is challenging for the sufficient control of this pandemic. However, there are few studies about the risk of re-positivity. We aimed to explore the association of neutralizing antibodies (nAbs, AU/mL...

Expanding from unilateral to bilateral: A robust deep learning-based approach for predicting radiographic osteoarthritis progression.

Osteoarthritis and cartilage
OBJECTIVE: To develop and validate a deep learning (DL) model for predicting osteoarthritis (OA) progression based on bilateral knee joint views.

Deep learning radiomics on shear wave elastography and b-mode ultrasound videos of diaphragm for weaning outcome prediction.

Medical engineering & physics
PURPOSE: We proposed an automatic method based on deep learning radiomics (DLR) on shear wave elastography (SWE) and B-mode ultrasound videos of diaphragm for two classification tasks, one for differentiation between the control and patient groups, a...