AIMC Topic: Retrospective Studies

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Enhancing Pulmonary Disease Prediction Using Large Language Models With Feature Summarization and Hybrid Retrieval-Augmented Generation: Multicenter Methodological Study Based on Radiology Report.

Journal of medical Internet research
BACKGROUND: The rapid advancements in natural language processing, particularly the development of large language models (LLMs), have opened new avenues for managing complex clinical text data. However, the inherent complexity and specificity of medi...

Extremity Soft Tissue Sarcoma Reconstruction Nomograms: A Clinicoradiomic, Machine Learning-Powered Predictor of Postoperative Outcomes.

JCO clinical cancer informatics
PURPOSE: The choice of wound closure modality after limb-sparing extremity soft-tissue sarcoma (eSTS) resection is fraught with uncertainty. Leveraging machine learning and clinicoradiomic data, we developed Sarcoma Reconstruction Nomograms (SARCON),...

Deep learning-based post-hoc noise reduction improves quarter-radiation-dose coronary CT angiography.

European journal of radiology
PURPOSE: To evaluate the impact of deep learning-based post-hoc noise reduction (DLNR) on image quality, coronary artery disease reporting and data system (CAD-RADS) assessment, and diagnostic performance in quarter-dose versus full-dose coronary CT ...

Differentiating Bacterial and Non-Bacterial Pneumonia on Chest CT Using Multi-Plane Features and Clinical Biomarkers.

Academic radiology
RATIONALE AND OBJECTIVES: Timely and accurate classification of bacterial pneumonia (BP) is essential for guiding antibiotic therapy. However, distinguishing BP from non-bacterial pneumonia (NBP) using computed tomography (CT) is challenging due to o...

Interpretable web-based machine learning model for predicting intravenous immunoglobulin resistance in Kawasaki disease.

Italian journal of pediatrics
BACKGROUND: Kawasaki disease (KD) is a leading cause of acquired heart disease in children that is treated with intravenous immunoglobulin (IVIG). However, 10-20% of cases exhibit IVIG resistance, which increases the risk of coronary complications. E...

Comparison of lesion segmentation performance in diffusion-weighted imaging and apparent diffusion coefficient images of stroke by artificial neural networks.

PloS one
Stroke is the second leading cause of death, accounting for 11% of deaths worldwide. Comparing diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images is important for stroke diagnosis, but most studies have focused on lesion...

Simulating workload reduction with an AI-based prostate cancer detection pathway using a prediction uncertainty metric.

European radiology
OBJECTIVES: This study compared two uncertainty quantification (UQ) metrics to rule out prostate MRI scans with a high-confidence artificial intelligence (AI) prediction and investigated the resulting potential radiologist's workload reduction in a c...

Contribution of Labrum and Cartilage to Joint Surface in Different Hip Deformities: An Automatic Deep Learning-Based 3-Dimensional Magnetic Resonance Imaging Analysis.

The American journal of sports medicine
BACKGROUND: Multiple 2-dimensional magnetic resonance imaging (MRI) studies have indicated that the size of the labrum adjusts in response to altered joint loading. In patients with hip dysplasia, it tends to increase as a compensatory mechanism for ...

AI-guided spatiotemporal dispersion mapping for individualized ablation in an all-comer cohort with atrial fibrillation.

Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing
BACKGROUND: Artificial intelligence (AI)-guided spatiotemporal dispersion (stD) mapping has been shown to improve outcomes in patients with persistent atrial fibrillation (AF). However, the relationship between stD mapping and markers of atrial cardi...