AIMC Topic: Retrospective Studies

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Artificial intelligence-assisted quantitative CT parameters in predicting the degree of risk of solitary pulmonary nodules.

Annals of medicine
INTRODUCTION: Artificial intelligence (AI) shows promise for evaluating solitary pulmonary nodules (SPNs) on computed tomography (CT). Accurately determining cancer invasiveness can guide treatment. We aimed to investigate quantitative CT parameters ...

OxcarNet: sinc convolutional network with temporal and channel attention for prediction of oxcarbazepine monotherapy responses in patients with newly diagnosed epilepsy.

Journal of neural engineering
Monotherapy with antiepileptic drugs (AEDs) is the preferred strategy for the initial treatment of epilepsy. However, an inadequate response to the initially prescribed AED is a significant indicator of a poor long-term prognosis, emphasizing the imp...

The application and clinical translation of the self-evolving machine learning methods in predicting diabetic retinopathy and visualizing clinical transformation.

Frontiers in endocrinology
OBJECTIVE: This study aims to analyze the application and clinical translation value of the self-evolving machine learning methods in predicting diabetic retinopathy and visualizing clinical outcomes.

Text mining of verbal autopsy narratives to extract mortality causes and most prevalent diseases using natural language processing.

PloS one
Verbal autopsy (VA) narratives play a crucial role in understanding and documenting the causes of mortality, especially in regions lacking robust medical infrastructure. In this study, we propose a comprehensive approach to extract mortality causes a...

Enhanced machine learning models for predicting one-year mortality in individuals suffering from type A aortic dissection.

The Journal of thoracic and cardiovascular surgery
OBJECTIVE: The study objective was to develop and validate an interpretable machine learning model to predict 1-year mortality in patients with type A aortic dissection, improving risk classification and aiding clinical decision-making.

Quality assessment of expedited AI generated reformatted images for ED acquired CT abdomen and pelvis imaging.

Abdominal radiology (New York)
PURPOSE: Retrospectively compare image quality, radiologist diagnostic confidence, and time for images to reach PACS for contrast enhanced abdominopelvic CT examinations created on the scanner console by technologists versus those generated automatic...

Deep Learning-Based Denoising Enables High-Quality, Fully Diagnostic Neuroradiological Trauma CT at 25% Radiation Dose.

Academic radiology
RATIONALE AND OBJECTIVES: Traumatic neuroradiological emergencies necessitate rapid and accurate diagnosis, often relying on computed tomography (CT). However, the associated ionizing radiation poses long-term risks. Modern artificial intelligence re...

Non-traumatic brachial plexopathy identification from routine MRIs: Retrospective studies with deep learning networks.

European journal of radiology
PURPOSE: This study aims to seek an optimized deep learning model for differentiating non-traumatic brachial plexopathy from routine MRI scans.

Evaluation and analysis of risk factors for adverse events of the fractured vertebra post-percutaneous kyphoplasty: a retrospective cohort study using multiple machine learning models.

Journal of orthopaedic surgery and research
BACKGROUND: Adverse events of the fractured vertebra (AEFV) post-percutaneous kyphoplasty (PKP) can lead to recurrent pain and neurological damage, which considerably affect the prognosis of patients and the quality of life. This study aimed to analy...

The Potential Clinical Utility of an Artificial Intelligence Model for Identification of Vertebral Compression Fractures in Chest Radiographs.

Journal of the American College of Radiology : JACR
PURPOSE: To assess the ability of the Annalise Enterprise CXR Triage Trauma (Annalise AI Pty Ltd, Sydney, NSW, Australia) artificial intelligence model to identify vertebral compression fractures on chest radiographs and its potential to address undi...