AIMC Topic: Male

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Clinical Implications of The Cancer Genome Atlas Molecular Classification System in Esophagogastric Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: The Cancer Genome Atlas (TCGA) project defined four distinct molecular subtypes of esophagogastric adenocarcinoma: microsatellite instable (MSI), Epstein-Barr virus (EBV)-associated, genomically stable (GS), and chromosomally instable (CIN)....

Electroencephalography estimates brain age in infants with high precision: Leveraging advanced machine learning in healthcare.

NeuroImage
Changes in the pace of neurodevelopment are key indicators of atypical maturation during early life. Unfortunately, reliable prognostic tools rely on assessments of cognitive and behavioral skills that develop towards the second year of life and afte...

Protein Profiles Predict Treatment Responses to the PI3K Inhibitor Umbralisib in Patients with Chronic Lymphocytic Leukemia.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: The management of chronic lymphocytic leukemia (CLL) has significantly improved with targeted therapies. However, many patients experience a suboptimal response. To optimally select the best therapy, predictive biomarkers are necessary. In t...

MIMI-ONET: Multi-Modal image augmentation via Butterfly Optimized neural network for Huntington DiseaseDetection.

Brain research
Huntington's disease (HD) is a chronic neurodegenerative ailment that affects cognitive decline, motor impairment, and psychiatric symptoms. However, the existing HD detection methods are struggle with limited annotated datasets that restricts their ...

A machine-learning-derived online prediction model for depression risk in COPD patients: A retrospective cohort study from CHARLS.

Journal of affective disorders
BACKGROUND: Depression associated with Chronic Obstructive Pulmonary Disease (COPD) is a detrimental complication that significantly impairs patients' quality of life. This study aims to develop an online predictive model to estimate the risk of depr...

Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study.

Neuro-oncology
BACKGROUND: Glioblastoma (GBM) is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification.

Deep Learning Models for Multi-Part Morphological Segmentation and Evaluation of Live Unstained Human Sperm.

Sensors (Basel, Switzerland)
To perform accurate computer vision quality assessments of sperm used within reproductive medicine, a clear separation of each sperm component from the background is critical. This study systematically evaluates and compares the performance of Mask R...

Predicting sepsis treatment decisions in the paediatric emergency department using machine learning: the AiSEPTRON study.

BMJ paediatrics open
BACKGROUND: Early identification of children at risk of sepsis in emergency departments (EDs) is crucial for timely treatment and improved outcomes. Existing risk scores and criteria for paediatric sepsis are not well-suited for early diagnosis in ED...

Machine learning-based diagnostic model for neonatal intestinal diseases in multiple centres: a cross-sectional study protocol.

BMJ open
BACKGROUND: Neonatal intestinal diseases often have an insidious onset and can lead to poor outcomes if not identified early. Early assessment of abnormal bowel function is critical for timely intervention and improving prognosis, underscoring the cl...

Machine Learning for Predicting Postoperative Functional Disability and Mortality Among Older Patients With Cancer: Retrospective Cohort Study.

JMIR aging
BACKGROUND: The global cancer burden is rapidly increasing, with 20 million new cases estimated in 2022. The world population aged ≥65 years is also increasing, projected to reach 15.9% by 2050, making cancer control for older patients urgent. Surgic...