AIMC Topic: Machine Learning

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Learning dissection trajectories from expert surgical videos via imitation learning with equivariant diffusion.

Medical image analysis
Endoscopic Submucosal Dissection (ESD) constitutes a firmly well-established technique within endoscopic resection for the elimination of epithelial lesions. Dissection trajectory prediction in ESD videos has the potential to strengthen surgical skil...

Beyond total scores: Enhancing psychotherapy outcome prediction with item-level scores.

Journal of consulting and clinical psychology
OBJECTIVE: This study aims at improving dropout and treatment nonresponse prevention by optimizing the performance of models for their prediction through the integration of item-level data.

Exploring multi-instance learning in whole slide imaging: Current and future perspectives.

Pathology, research and practice
Whole slide images (WSI), due to their gigabyte-scale size and ultra-high resolution, play a significant role in diagnostic pathology. However, the enormous data size makes it difficult to directly input these images into image processing units (GPU)...

Vedolizumab in inflammatory bowel disease: Real-world outcomes and their prediction with machine learning-the IG-IBD LIVE study.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND AND AIMS: Real-world studies on vedolizumab in inflammatory bowel disease (IBD) are often limited by small sample size and short follow-up. In this study, we investigated the 2-year effectiveness and safety of vedolizumab in patients with ...

A lightweight graph neural network to predict long-term mortality in coronary artery disease patients: an interpretable causality-aware approach.

Journal of biomedical informatics
BACKGROUND: Coronary artery disease (CAD) causes substantial death toll in the United States and worldwide. While traditional methods for CAD mortality prediction are based on established risk factors, they have significant limitations in accuracy, a...

Screening of oxidative stress components of cigarette smoke based on machine learning model integration.

Toxicology and applied pharmacology
Cigarette smoke, a complex mixture of more than 7000 chemicals, poses a significant threat to human health, with oxidative stress being an important mechanism in its associated diseases. Traditional methods for assessing the toxicity of cigarette smo...

A systematic review and meta-analysis of the utility of quantitative, imaging-based approaches to predict radiation-induced toxicity in lung cancer patients.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: To conduct a systematic review and meta-analysis of the performance of radiomics, dosiomics and machine learning in generating toxicity prediction in thoracic radiotherapy.

Prediction of post stroke depression with machine learning: A national multicenter cohort study.

Journal of psychiatric research
OBJECTIVE: Post-stroke depression (PSD) is a common psychiatric complication following stroke, with low clinical detection rates and delayed diagnosis. Most existing PSD prediction models suffer from incomplete data inclusion, which limits their clin...

Machine learning approaches for classifying major depressive disorder using biological and neuropsychological markers: A meta-analysis.

Neuroscience and biobehavioral reviews
Traditional diagnostic methods for major depressive disorder (MDD), which rely on subjective assessments, may compromise diagnostic accuracy. In contrast, machine learning models have the potential to classify and diagnose MDD more effectively, reduc...

Radiomics-based machine learning in prediction of response to neoadjuvant chemotherapy in osteosarcoma: A systematic review and meta-analysis.

Clinical imaging
BACKGROUND AND AIMS: Osteosarcoma (OS) is the most common primary bone malignancy, and neoadjuvant chemotherapy (NAC) improves survival rates. However, OS heterogeneity results in variable treatment responses, highlighting the need for reliable, non-...