AIMC Topic: Retrospective Studies

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PSMA PET Evaluation with a Deep Learning Platform Compared with a Standard Image Viewer and Histopathology.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Standardized prostate-specific membrane antigen (PSMA) PET/CT evaluation and reporting was introduced to aid interpretation, reproducibility, and communication. Artificial intelligence may enhance these efforts. This study aimed to evaluate the perfo...

Development and validation of a machine learning model for critical progression risk in pediatric severe community-acquired pneumonia.

Scientific reports
This study aimed to utilize various machine learning algorithms to develop a predictive model for the progression of severe community-acquired pneumonia (SCAP) in children to critical severe community-acquired pneumonia (cSCAP). Retrospective analysi...

Radiomics-based MRI models for predicting breast cancer axillary lymph node involvement in comparison with Node-RADS: a proof-of-concept study.

European radiology experimental
BACKGROUND: Detection of axillary lymph node (LN) involvement is essential for staging breast cancer and optimizing treatment. This proof-of-concept two-center study explored the feasibility of magnetic resonance imaging (MRI) radiomics-based machine...

Deep-learning prediction of breast cancer hormone receptor status from CEM: a preliminary study.

European radiology experimental
BACKGROUND: Hormone receptor (HR) status guides breast cancer therapy. Deep learning (DL) applied to contrast-enhanced mammography (CEM) might offer a noninvasive means for HR status prediction, but class imbalance challenges model development and as...

Novel artificial intelligence model predicts the need for reduction of distal radius fractures.

European journal of orthopaedic surgery & traumatology : orthopedie traumatologie
PURPOSE: Distal radius fractures are among the most common upper extremity injuries. While convolutional neural networks (CNNs) have shown promise in fracture detection, no models have specifically addressed the need for reduction, which is a critica...

Neural network-driven direct CBCT-based dose calculation for head-and-neck proton treatment planning.

Physics in medicine and biology
Accurate dose calculation on cone beam computed tomography (CBCT) images is essential for modern proton treatment planning workflows, particularly when accounting for inter-fractional anatomical changes in adaptive treatment scenarios. Traditional CB...

Development of explainable machine learning models to predict side effects in patients with rheumatoid arthritis taking methotrexate treatment: a nationwide multicentre cohort study.

BMJ open
OBJECTIVES: Methotrexate (MTX) effectively controls rheumatoid arthritis (RA) but often leads to side effects (SE) such as gastrointestinal (GI) issues, liver toxicity and bone marrow suppression. To develop clinically interpretable machine learning ...

Development and validation of an interpretable predictive machine learning model for successful weaning of continuous renal replacement therapy.

Scientific reports
Continuous renal replacement therapy (CRRT) is a vital intervention for critically ill patients with severe acute kidney injury, yet no standardized criteria exist to determine the optimal time for its discontinuation. We developed and validated mach...