AIMC Topic: Retrospective Studies
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Interpretable machine learning model for early morbidity risk prediction in patients with sepsis-induced coagulopathy: a multi-center study.
Frontiers in immunology
Mar 3, 2025
BACKGROUND: Sepsis-induced coagulopathy (SIC) is a complex condition characterized by systemic inflammation and coagulopathy. This study aimed to develop and validate a machine learning (ML) model to predict SIC risk in patients with sepsis.
Development of a deep learning radiomics model combining lumbar CT, multi-sequence MRI, and clinical data to predict high-risk cage subsidence after lumbar fusion: a retrospective multicenter study.
Biomedical engineering online
Mar 2, 2025
BACKGROUND: To develop and validate a model that integrates clinical data, deep learning radiomics, and radiomic features to predict high-risk patients for cage subsidence (CS) after lumbar fusion.
Use of deep learning-based high-resolution magnetic resonance to identify intracranial and extracranial symptom-related plaques.
Neuroscience
Mar 1, 2025
This study aims to develop a deep learning model using high-resolution vessel wall imaging (HR-VWI) to differentiate symptom-related intracranial and extracranial plaques, which is crucial for stroke treatment and prevention. We retrospectively analy...
Predicting 30-day mortality in hemophagocytic lymphohistiocytosis: clinical features, biochemical parameters, and machine learning insights.
Annals of hematology
Mar 1, 2025
This study aims to evaluate the clinical characteristics and biochemical parameters of hemophagocytic lymphohistiocytosis (HLH) patients to predict 30-day mortality. Parameters analyzed include lymphocyte count (L), platelet count (PLT), total protei...
Predicting responsiveness to fixed-dose methylene blue in adult patients with septic shock using interpretable machine learning: a retrospective study.
Scientific reports
Mar 1, 2025
This study aimed to develop an interpretable machine learning model to predict methylene blue (MB) responsiveness in adult patients with refractory septic shock and to identify key factors influencing MB responsiveness using the SHapley Additive exPl...
Machine-learning models for the prediction of ideal surgical outcomes in patients with adult spinal deformity.
The bone & joint journal
Mar 1, 2025
AIMS: Adult spinal deformity (ASD) surgery can reduce pain and disability. However, the actual surgical efficacy of ASD in doing so is far from desirable, with frequent complications and limited improvement in quality of life. The accurate prediction...
Intelligent biofilm detection with ensemble of deep learning networks.
Medicina oral, patologia oral y cirugia bucal
Mar 1, 2025
BACKGROUND: Dental biofilm is traditionally identified visually, which can be challenging and time-consuming due to its color similarity with the tooth. The aim of this study was to evaluate the performance of U-Net neural networks for the automatic ...
Artificial intelligence-assisted precise preoperative prediction of lateral cervical lymph nodes metastasis in papillary thyroid carcinoma via a clinical-CT radiomic combined model.
International journal of surgery (London, England)
Mar 1, 2025
OBJECTIVES: This study aimed to develop an artificial intelligence-assisted model for the preoperative prediction of lateral cervical lymph node metastasis (LCLNM) in papillary thyroid carcinoma (PTC) using computed tomography (CT) radiomics, providi...
Clinical value of aortic arch morphology in transfemoral TAVR: artificial intelligence evaluation.
International journal of surgery (London, England)
Mar 1, 2025
BACKGROUND: The impact of aortic arch (AA) morphology on the management of the procedural details and the clinical outcomes of the transfemoral artery (TF)-transcatheter aortic valve replacement (TAVR) has not been evaluated. The goal of this study w...
Artificial intelligence quantification and experienced reader computed tomography analysis for differentiating normal from minimally and mildly diseased coronary arteries: an early real-world compatibility study.
The international journal of cardiovascular imaging
Feb 28, 2025
Differentiating normal from minimally and mildly diseased coronary arteries on coronary computed tomographic angiography (CCTA) is crucial, impacting treatment decisions due to the extremely low coronary artery event risk associated with the former. ...
Middle Aged
Severity of Illness Index
Predictive Value of Tests
Coronary Angiography
Plaque, Atherosclerotic
Female
Humans
Radiographic Image Interpretation, Computer-Assisted
Vascular Calcification
Artificial Intelligence
Coronary Artery Disease
Asymptomatic Diseases
Reproducibility of Results
Retrospective Studies
Observer Variation
Computed Tomography Angiography
Aged
Coronary Vessels
Male
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