AIMC Topic: Middle Aged

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Comparison of Machine Learning Models for Brain Age Prediction Using Six Imaging Modalities on Middle-Aged and Older Adults.

Sensors (Basel, Switzerland)
Machine learning (ML) has transformed neuroimaging research by enabling accurate predictions and feature extraction from large datasets. In this study, we investigate the application of six ML algorithms (Lasso, relevance vector regression, support v...

Identifying Reasons for Statin Nonuse in Patients With Diabetes Using Deep Learning of Electronic Health Records.

Journal of the American Heart Association
Background Statins are guideline-recommended medications that reduce cardiovascular events in patients with diabetes. Yet, statin use is concerningly low in this high-risk population. Identifying reasons for statin nonuse, which are typically describ...

Detecting individuals with severe mental illness using artificial intelligence applied to magnetic resonance imaging.

EBioMedicine
BACKGROUND: Identifying individuals at risk for severe mental illness (SMI) is crucial for prevention and early intervention strategies. While MRI shows potential for case identification even before illness onset, no practical model for mental health...

Single-incision robotic colorectal surgery with the da Vinci SP® surgical system: initial results of 50 cases.

Techniques in coloproctology
PURPOSE: The da Vinci SP® (dVSP) surgical system (Intuitive Surgical, Sunnyvale, CA, USA), a robotic platform designed for single-incision surgery, overcame the need for multiple ports in traditional robotic surgery and issues including triangulation...

Deep Learning With an Attention Mechanism for Differentiating the Origin of Brain Metastasis Using MR images.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Brain metastasis (BM) is a serious neurological complication of cancer of different origins. The value of deep learning (DL) to identify multiple types of primary origins remains unclear.

Fully Automated Artificial Intelligence Assessment of Aortic Stenosis by Echocardiography.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
BACKGROUND: Aortic stenosis (AS) is a common form of valvular heart disease, present in over 12% of the population age 75 years and above. Transthoracic echocardiography (TTE) is the first line of imaging in the adjudication of AS severity but is tim...

Robotic Right Posterior Sectionectomy by Intrafascial Approach for Pancreas Neuroendocrine Liver Metastasis.

Annals of surgical oncology
BACKGROUND: Liver resection is indicated for resectable liver metastases of neuroendocrine tumors. Minimally invasive liver resection offers decreased blood loss, reduces pain, reduces postoperative complications, and reduces time to functional recov...

Automatic deep learning-based assessment of spinopelvic coronal and sagittal alignment.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to evaluate an artificial intelligence (AI) solution for estimating coronal and sagittal spinopelvic alignment on conventional uniplanar two-dimensional whole-spine radiograph.

Deep Learning-Based Segmentation of Locally Advanced Breast Cancer on MRI in Relation to Residual Cancer Burden: A Multi-Institutional Cohort Study.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: While several methods have been proposed for automated assessment of breast-cancer response to neoadjuvant chemotherapy on breast MRI, limited information is available about their performance across multiple institutions.

Deep Learning Prediction for Distal Aortic Remodeling After Thoracic Endovascular Aortic Repair in Stanford Type B Aortic Dissection.

Journal of endovascular therapy : an official journal of the International Society of Endovascular Specialists
PURPOSE: This study aimed to develop a deep learning model for predicting distal aortic remodeling after proximal thoracic endovascular aortic repair (TEVAR) in patients with Stanford type B aortic dissection (TBAD) using computed tomography angiogra...