AIMC Topic: Female

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Fada: Fetal Accurate Detection AI for Automated Ultrasound Image Analysis and Reporting.

Studies in health technology and informatics
This study introduces Fetal Accurate Detection AI (FADA) an advanced AI-driven framework for generating clinically relevant descriptions from fetal ultrasound images, specifically focused on diverse anatomical structures and views, including trans-ab...

Constrained Tensor Factorization for Cancer Phenotyping and Mortality Prediction.

Studies in health technology and informatics
Electronic health records (EHR) enable machine learning methods like tensor factorization to extract computational phenotypes. Using Northwestern Medicine data (2000-2015), we analyzed breast, prostate, colorectal, and lung cancer cohorts to predict ...

Using Machine Learning Techniques for Lung Cancer Survival Prediction.

Studies in health technology and informatics
Lung cancer is one of the most common and lethal types of cancer. Early diagnosis and appropriate treatment play a crucial role in reducing mortality. Artificial intelligence techniques can be used to support clinical approaches to lung cancer, helpi...

Predicting Postpartum Depression Risk Using Social Determinants of Health.

Studies in health technology and informatics
Postpartum depression (PPD) affects approximately 20% of women after childbirth and has complex etiology. Existing predictive models of PPD lack training on large, national datasets and comprehensive integration of clinical and social determinants. T...

Predicting Nephrectomy Risk in Patients with Renal Cancer Using Real-World Electronic Health Records.

Studies in health technology and informatics
Nephrectomy, the surgical removal of a kidney, is a critical treatment for renal cancer, and predicting its likelihood can help guide clinical decision-making and optimize preoperative planning. This study utilized real-world electronic health record...

AI Bias and Confounding Risk in Health Feature Engineering for Machine Learning Classification Task.

Studies in health technology and informatics
Recent advancements in machine learning bring unique opportunities in health fields but also pose considerable challenges. Due to stringent ethical considerations and resource constraints, health data can vary in scope, population coverage, and colle...

Detection of Brain Cancer Using Genome-wide Cell-free DNA Fragmentomes.

Cancer discovery
UNLABELLED: Diagnostic delays in patients with brain cancer are common and can impact patient outcome. Development of a blood-based assay for detection of brain cancers could accelerate brain cancer diagnosis. In this study, we analyzed genome-wide c...

Machine learning diagnosis of cognitive impairment and dementia in harmonized older adult cohorts.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Clinical diagnosis (normal cognition, mild cognitive impairment [MCI], dementia) is critical for understanding cognitive impairment and dementia but can be resource intensive and subject to inconsistencies due to complex clinical judgme...

Predicting p53 Status in IDH-Mutant Gliomas Using MRI-Based Radiomic Model.

Cancer medicine
OBJECTIVES: Accurate and noninvasive detection of p53 status in isocitrate dehydrogenase mutant (IDH-mt) glioma is clinically meaningful for molecular stratification of glioma, yet it remains challenging. We aimed to investigate the diagnostic effica...

Machine Learning Model for Predicting Pathological Invasiveness of Pulmonary Ground-Glass Nodules Based on AI-Extracted Radiomic Features.

Thoracic cancer
BACKGROUND: With the widespread adoption of low-dose CT screening, the detection of pulmonary ground-glass nodules (GGNs) has risen markedly, presenting diagnostic challenges in distinguishing preinvasive lesions from invasive adenocarcinomas (IAC). ...