AIMC Topic: Retrospective Studies

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Machine learning prediction model of prolonged delay to loop ileostomy closure after rectal cancer surgery: a retrospective study.

World journal of surgical oncology
BACKGROUND: Delayed closure of a temporary ileostomy in patients with rectal cancer may cause psychological, physiological, and socioeconomic burdens to patients.

Optimizing breast lesions diagnosis and decision-making with a deep learning fusion model integrating ultrasound and mammography: a dual-center retrospective study.

Breast cancer research : BCR
BACKGROUND: This study aimed to develop a BI-RADS network (DL-UM) via integrating ultrasound (US) and mammography (MG) images and explore its performance in improving breast lesion diagnosis and management when collaborating with radiologists, partic...

In-depth analysis of the risk factors for persistent severe acute respiratory syndrome coronavirus 2 infection and construction of predictive models: an exploratory research study.

BMC infectious diseases
BACKGROUND: Persistent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection differs from long coronavirus disease (COVID-19) (acute symptoms ≥ 12 weeks post-clearance). The Omicron BA.5 variant has a shorter median clearance time (1...

Deep learning based on ultrasound images to predict platinum resistance in patients with epithelial ovarian cancer.

Biomedical engineering online
BACKGROUND: The study aimed at developing and validating a deep learning (DL) model based on the ultrasound imaging for predicting the platinum resistance of patients with epithelial ovarian cancer (EOC).

Improving prediction accuracy of hospital arrival vital signs using a multi-output machine learning model: a retrospective study of JSAS-registry data.

BMC emergency medicine
BACKGROUND: Critically ill patients can deteriorate rapidly; therefore, prompt prehospital interventions and seamless transition to in-hospital care upon arrival are crucial for improving survival. In Japan, helicopter emergency medical services (HEM...

Constructing a prediction model for acute pancreatitis severity based on liquid neural network.

Scientific reports
Acute pancreatitis (AP) is a common disease, and severe acute pancreatitis (SAP) has a high morbidity and mortality rate. Early recognition of SAP is crucial for prognosis. This study aimed to develop a novel liquid neural network (LNN) model for pre...

Multicenter target trial emulation to evaluate corticosteroids for sepsis stratified by predicted organ dysfunction trajectory.

Nature communications
Corticosteroids decrease the duration of organ dysfunction in sepsis and a range of overlapping and complementary infectious critical illnesses, including septic shock, pneumonia and the acute respiratory distress syndrome (ARDS). The risk and benefi...

Application of BOLDMRIbased radiomics in differentiating malignant from benign renal tumors.

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences
OBJECTIVES: Blood oxygen level dependent magnetic resonance imaging (BOLD-MRI) is a kind of non-invasive MRI technology which reflects the tissue blood oxyen levels. This stuy aims to explore the value of radiomics based on BOLD-MRI in differentiatin...

AI-Based Quantitative CT Analysis of Temporal Changes According to Disease Severity in COVID-19 Pneumonia.

Journal of computer assisted tomography
OBJECTIVE: To quantitatively evaluate computed tomography (CT) parameters of coronavirus disease 2019 (COVID-19) pneumonia an artificial intelligence (AI)-based software in different clinical severity groups during the disease course.

Radiomics-Based Machine Learning Classification for Glioma Grading Using Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging.

Journal of computer assisted tomography
OBJECTIVE: The aim of this study was to evaluate various radiomics-based machine learning classification models using the apparent diffusion coefficient (ADC) and cerebral blood flow (CBF) maps for differentiating between low-grade gliomas (LGGs) and...