AIMC Topic: Retrospective Studies

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Artificial Intelligence-Assisted Matching of Human Postmortem Donors to Ocular Research Projects.

Advances in experimental medicine and biology
The scarcity of human ocular samples with short postmortem intervals (PMIs) is a significant issue in ophthalmic research and drug discovery. A contributing factor is that eye banks must manually match donor data to prospective research project crite...

Deep-Learning Generated Synthetic Material Decomposition Images Based on Single-Energy CT to Differentiate Intracranial Hemorrhage and Contrast Staining Within 24 Hours After Endovascular Thrombectomy.

CNS neuroscience & therapeutics
AIMS: To develop a transformer-based generative adversarial network (trans-GAN) that can generate synthetic material decomposition images from single-energy CT (SECT) for real-time detection of intracranial hemorrhage (ICH) after endovascular thrombe...

Deep Learning-assisted Diagnosis of Extrahepatic Common Bile Duct Obstruction Using MRCP Imaging and Clinical Parameters.

Current medical imaging
BACKGROUND: Extrahepatic Common Bile Duct Obstruction (EHBDO) is a serious condition that requires accurate diagnosis for effective treatment. Magnetic Resonance Cholangiopancreatography (MRCP) is a widely used noninvasive imaging technique for visua...

Automated Deep Learning-Based Detection and Segmentation of Lung Tumors at CT.

Radiology
Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer progression, evaluating treatment responses, and planning radiation therapy; however, manual delineation is labor-intensive and subject to physician ...

Deep Learning to Simulate Contrast-Enhanced MRI for Evaluating Suspected Prostate Cancer.

Radiology
Background Multiparametric MRI, including contrast-enhanced sequences, is recommended for evaluating suspected prostate cancer, but concerns have been raised regarding potential contrast agent accumulation and toxicity. Purpose To evaluate the feasib...

A Machine Learning Model Using Cardiac CT and MRI Data Predicts Cardiovascular Events in Obstructive Coronary Artery Disease.

Radiology
Background Multimodality imaging is essential for personalized prognostic stratification in suspected coronary artery disease (CAD). Machine learning (ML) methods can help address this complexity by incorporating a broader spectrum of variables. Purp...

Development and Validation of a Deep Learning Model Based on MRI and Clinical Characteristics to Predict Risk of Prostate Cancer Progression.

Radiology. Imaging cancer
Purpose To validate a deep learning (DL) model for predicting the risk of prostate cancer (PCa) progression based on MRI and clinical parameters and compare it with established models. Materials and Methods This retrospective study included 1607 MRI ...

Prediction of Cisplatin-Induced Acute Kidney Injury Using an Interpretable Machine Learning Model and Electronic Medical Record Information.

Clinical and translational science
Predicting cisplatin-induced acute kidney injury (Cis-AKI) before its onset is important. We aimed to develop a predictive model for Cis-AKI using patient clinical information based on an interpretable machine learning algorithm. This single-center r...

Point-of-Care Potassium Measurement vs Artificial Intelligence-Enabled Electrocardiography for Hyperkalemia Detection.

American journal of critical care : an official publication, American Association of Critical-Care Nurses
BACKGROUND: Hyperkalemia can be detected by point-of-care (POC) blood testing and by artificial intelligence- enabled electrocardiography (ECG). These 2 methods of detecting hyperkalemia have not been compared.