AIMC Topic: Follow-Up Studies
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Computed Tomography-Based Radiomics with Machine Learning Outperforms Radiologist Assessment in Estimating Colorectal Liver Metastases Pathologic Response After Chemotherapy.
Annals of surgical oncology
Oct 5, 2024
OBJECTIVES: This study was designed to assess computed tomography (CT)-based radiomics of colorectal liver metastases (CRLM), extracted from posttreatment scans in estimating pathologic treatment response to neoadjuvant therapy, and to compare treatm...
Follow-Up Studies
Radiologists
Response Evaluation Criteria in Solid Tumors
Female
Humans
Aged
Machine Learning
Adult
Prognosis
Colorectal Neoplasms
Retrospective Studies
Radiomics
Tomography, X-Ray Computed
Male
Middle Aged
Liver Neoplasms
Antineoplastic Combined Chemotherapy Protocols
Neoadjuvant Therapy
Machine learning and statistical models to predict all-cause mortality in type 2 diabetes: Results from the UK Biobank study.
Diabetes & metabolic syndrome
Oct 5, 2024
AIMS: This study aims to compare the performance of contemporary machine learning models with statistical models in predicting all-cause mortality in patients with type 2 diabetes mellitus and to develop a user-friendly mortality risk prediction tool...
Deep Learning-based 12-Lead Electrocardiogram for Low Left Ventricular Ejection Fraction Detection in Patients.
The Canadian journal of cardiology
Sep 27, 2024
BACKGROUND: Reduced left ventricular ejection fraction (LVEF) initiates heart failure, and promptly identifying low ejection fraction is crucial for managing progression and averting mortality. In this study we developed an artificial intelligence-en...
Explainable machine learning and online calculators to predict heart failure mortality in intensive care units.
ESC heart failure
Sep 19, 2024
AIMS: This study aims to develop explainable machine learning models and clinical tools for predicting mortality in patients in the intensive care unit (ICU) with heart failure (HF).
Clinical impact of deep learning-derived intravascular ultrasound characteristics in patients with deferred coronary artery.
International journal of cardiology
Sep 13, 2024
Prognostic markers for long-term outcomes are lacking in patients with deferred (nonculprit) coronary artery lesions. This study aimed to identify the morphological criteria for predicting adverse outcomes and validate their clinical impact. Using de...
Machine learning-based model for worsening heart failure risk in Chinese chronic heart failure patients.
ESC heart failure
Sep 7, 2024
AIMS: This study aims to develop and validate an optimal model for predicting worsening heart failure (WHF). Multiple machine learning (ML) algorithms were compared, and the results were interpreted using SHapley Additive exPlanations (SHAP). A clini...
Long-term Major Adverse Cardiac Event Prediction by Computed Tomography-derived Plaque Measures and Clinical Parameters Using Machine Learning.
Internal medicine (Tokyo, Japan)
Sep 4, 2024
Objective The present study evaluated the usefulness of machine learning (ML) models with the coronary computed tomography imaging and clinical parameters for predicting major adverse cardiac events (MACEs). Methods The Nationwide Gender-specific Ath...
Machine-learning-based prediction by stacking ensemble strategy for surgical outcomes in patients with degenerative cervical myelopathy.
Journal of orthopaedic surgery and research
Sep 4, 2024
BACKGROUND: Machine learning (ML) is extensively employed for forecasting the outcome of various illnesses. The objective of the study was to develop ML based classifiers using a stacking ensemble strategy to predict the Japanese Orthopedic Associati...
Artificial intelligence-based extraction of quantitative ultra-widefield fluorescein angiography parameters in retinal vein occlusion.
Canadian journal of ophthalmology. Journal canadien d'ophtalmologie
Aug 31, 2024
OBJECTIVE: To examine the association between quantitative vascular parameters extracted from intravenous fluorescein angiography (IVFA) and baseline clinical characteristics of patients with retinal vein occlusion (RVO).
Predictors of mortality by an artificial intelligence enhanced electrocardiogram model for cardiac amyloidosis.
ESC heart failure
Aug 31, 2024
AIMS: We aim to determine if our previously validated, diagnostic artificial intelligence (AI) electrocardiogram (ECG) model is prognostic for survival among patients with cardiac amyloidosis (CA).
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