AIMC Topic: Risk Assessment

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Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides.

Nature communications
Accurate risk stratification is critical for guiding treatment decisions in early breast cancer. We present an artificial intelligence (AI)-based tool that analyzes digitized tumor slides to predict 5-year metastasis-free survival (MFS) in patients w...

Automated ejection fraction and risk stratification in cardiomyopathy patients with diverse LV geometry using 2D echocardiography.

Scientific reports
Cardiomyopathy often alters left ventricular geometry (LVG), impairing cardiac function. We developed a deep learning (DL) model to estimate left ventricular ejection fraction (LVEF) from echocardiographic images while accounting for LVG variability ...

Development and validation of a modified SOFA score for mortality prediction in candidemia patients.

Scientific reports
Candidemia is a life-threatening bloodstream infection associated with high mortality rates, particularly in critically ill patients. Accurate risk stratification is crucial for timely intervention and could improve patient outcomes. This study aimed...

Advances in risk prediction models for Glaucoma: An updated narrative review.

Experimental eye research
Glaucoma is a leading cause of irreversible blindness and is characterized by optic nerve atrophy and progressive visual field loss. Risk prediction models are crucial for early screening and personalized treatment by identifying high-risk individual...

Radiomic 'Stress Test': exploration of a deep learning radiomic model in a high-risk prospective lung nodule cohort.

BMJ open respiratory research
BACKGROUND: Indeterminate pulmonary nodules (IPNs) are commonly biopsied to ascertain a diagnosis of lung cancer, but many are ultimately benign. The Lung Cancer Prediction (LCP) score is a commercially available deep learning radiomic model with str...

A Responsible Framework for Assessing, Selecting, and Explaining Machine Learning Models in Cardiovascular Disease Outcomes Among People With Type 2 Diabetes: Methodology and Validation Study.

JMIR medical informatics
BACKGROUND: Building machine learning models that are interpretable, explainable, and fair is critical for their trustworthiness in clinical practice. Interpretability, which refers to how easily a human can comprehend the mechanism by which a model ...

Deep transfer learning radiomics combined with explainable machine learning for preoperative thymoma risk prediction based on CT.

European journal of radiology
OBJECTIVE: To develop and validate a computerized tomography (CT)‑based deep transfer learning radiomics model combined with explainable machine learning for preoperative risk prediction of thymoma.

Artificial Intelligence for Identification of Patients with Increased Risk of Severe Cancer Therapy-Related Cardiac Dysfunction Following Anthracycline Therapy.

The American journal of medicine
BACKGROUND: Early detection of cancer therapy-related cardiac dysfunction (CTRCD) after anthracycline exposure is critically important in minimizing morbidity and mortality. Artificial intelligence models applied to electrocardiograms (ECG-AI) may al...

Predicting complications in breast reconstruction: External validation of a machine learning model.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS
BACKGROUND: Nipple-sparing mastectomy (NSM) with immediate implant-based breast reconstruction provides aesthetic and psychosocial benefits, but nipple-areolar complex (NAC) necrosis remains a significant risk. This study externally validated a previ...

Machine learning methods, applications and economic analysis to predict heart failure hospitalisation risk: a scoping review.

BMJ open
BACKGROUND:  Machine Learning (ML) has been transformative in healthcare, enabling more precise diagnostics, personalised treatment regimens and enhanced patient care. In cardiology, ML plays a crucial role in risk prediction and patient stratificati...