AIMC Topic: Predictive Value of Tests
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Machine Learning Based Prediction of Post-operative Infrarenal Endograft Apposition for Abdominal Aortic Aneurysms.
European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery
Jul 6, 2024
OBJECTIVE: Challenging infrarenal aortic neck characteristics have been associated with an increased risk of type Ia endoleak after endovascular aneurysm repair (EVAR). Short apposition (< 10 mm circumferential shortest apposition length [SAL]) on th...
Aged
Humans
Aortic Aneurysm, Abdominal
Aged, 80 and over
Machine Learning
Male
Aorta, Abdominal
Retrospective Studies
Treatment Outcome
Blood Vessel Prosthesis Implantation
Female
Predictive Value of Tests
Endovascular Procedures
Risk Factors
Blood Vessel Prosthesis
Computed Tomography Angiography
Aortography
Endoleak
Validation of an Electronic Health Record-Based Machine Learning Model Compared With Clinical Risk Scores for Gastrointestinal Bleeding.
Gastroenterology
Jul 5, 2024
BACKGROUND & AIMS: Guidelines recommend use of risk stratification scores for patients presenting with gastrointestinal bleeding (GIB) to identify very-low-risk patients eligible for discharge from emergency departments. Machine learning models may o...
Deep learning prediction of survival in patients with heart failure using chest radiographs.
The international journal of cardiovascular imaging
Jul 5, 2024
Heart failure (HF) is associated with high rates of morbidity and mortality. The value of deep learning survival prediction models using chest radiographs in patients with heart failure is currently unclear. The aim of our study is to develop and val...
External validation of a 2-year all-cause mortality prediction tool developed using machine learning in patients with stage 4-5 chronic kidney disease.
Journal of nephrology
Jul 4, 2024
BACKGROUND: Chronic kidney disease (CKD) is associated with increased mortality. Individual mortality prediction could be of interest to improve individual clinical outcomes. Using an independent regional dataset, the aim of the present study was to ...
Predicting Successful Chronic Total Occlusion Crossing With Primary Antegrade Wiring Using Machine Learning.
JACC. Cardiovascular interventions
Jul 3, 2024
BACKGROUND: There is limited data on predicting successful chronic total occlusion crossing using primary antegrade wiring (AW).
Evaluation of Serum Visfatin as a Biomarker of Lupus Nephritis in Egyptian Patients with Systemic Lupus Erythematosus.
Saudi journal of kidney diseases and transplantation : an official publication of the Saudi Center for Organ Transplantation, Saudi Arabia
Jul 3, 2024
One of the most significant consequences of systemic lupus erythematosus (SLE) is lupus nephritis (LN). Visfatin, an adipokine that is significantly expressed in visceral fat and is a marker of endothelial dysfunction in chronic kidney disease, has m...
Artificial Intelligence-based Segmentation of Residual Pancreatic Cancer in Resection Specimens Following Neoadjuvant Treatment (ISGPP-2): International Improvement and Validation Study.
The American journal of surgical pathology
Jul 2, 2024
Neoadjuvant therapy (NAT) has become routine in patients with borderline resectable pancreatic cancer. Pathologists examine pancreatic cancer resection specimens to evaluate the effect of NAT. However, an automated scoring system to objectively quant...
Machine learning-based Cerebral Venous Thrombosis diagnosis with clinical data.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Jul 2, 2024
OBJECTIVES: Cerebral Venous Thrombosis (CVT) poses diagnostic challenges due to the variability in disease course and symptoms. The prognosis of CVT relies on early diagnosis. Our study focuses on developing a machine learning-based screening algorit...
A machine learning tool for identifying patients with newly diagnosed diabetes in primary care.
Primary care diabetes
Jun 28, 2024
BACKGROUND AND AIM: It is crucial to identify a diabetes diagnosis early. Create a predictive model utilizing machine learning (ML) to identify new cases of diabetes in primary health care (PHC).
Whole-body low-dose computed tomography in patients with newly diagnosed multiple myeloma predicts cytogenetic risk: a deep learning radiogenomics study.
Skeletal radiology
Jun 27, 2024
OBJECTIVE: To develop a whole-body low-dose CT (WBLDCT) deep learning model and determine its accuracy in predicting the presence of cytogenetic abnormalities in multiple myeloma (MM).
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