AIMC Topic: Humans

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A multi-granularity convolutional neural network model with temporal information and attention mechanism for efficient diabetes medical cost prediction.

Computers in biology and medicine
As the cost of diabetes treatment continues to grow, it is critical to accurately predict the medical costs of diabetes. Most medical cost studies based on convolutional neural networks (CNNs) ignore the importance of multi-granularity information of...

Progress in Control-Actuation Robotic System for Gastrointestinal NOTES Development.

BioMed research international
PURPOSE: Natural orifice transluminal endoscopic surgery (NOTES) is a minimally invasive surgical procedure that reduces patient trauma, infection probability, and rehabilitation time. This paper reviews the progress made in the control-actuation rob...

Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer.

Biological psychiatry
Predictive models in neuroimaging are increasingly designed with the intent to improve risk stratification and support interventional efforts in psychiatry. Many of these models have been developed in samples of children school-aged or older. Neverth...

Exactech Equinoxe anatomic versus reverse total shoulder arthroplasty for primary osteoarthritis: case controlled comparisons using the machine learning-derived Shoulder Arthroplasty Smart score.

Journal of shoulder and elbow surgery
BACKGROUND: The role of reverse total shoulder arthroplasty (rTSA) for glenohumeral osteoarthritis (GHOA) with an intact rotator cuff remains unclear with prior investigations demonstrating similar patient-reported outcome measures (PROMs) to anatomi...

Intraoperative angiography of the neurovascular bundle using indocyanine green and near-infrared fluorescence improves anatomical dissection during robot-assisted radical prostatectomy: initial clinical experience.

Journal of robotic surgery
Landmark artery identification in the neurovascular bundle (NVB) is important for nerve-sparing in radical prostatectomy. We aimed to investigate intraoperative angiography using indocyanine green and near-infrared fluorescence (ICG-NIRF) during robo...

Single-Port Transaxillary Robotic Modified Radical Neck Dissection (STAR-RND): Initial Experiences.

The Laryngoscope
OBJECTIVES: This study aimed to demonstrate the usefulness of single-port transaxillary robotic modified radical neck dissection (STAR-RND) for metastatic thyroid cancer, and its potential to make small and invisible surgical wounds possible compared...

SAM-X: sorting algorithm for musculoskeletal x-ray radiography.

European radiology
OBJECTIVE: To develop a two-phased deep learning sorting algorithm for post-X-ray image acquisition in order to facilitate large musculoskeletal image datasets according to their anatomical entity.

Fully-automated deep learning-based flow quantification of 2D CINE phase contrast MRI.

European radiology
OBJECTIVES: Time-resolved, 2D-phase-contrast MRI (2D-CINE-PC-MRI) enables in vivo blood flow analysis. However, accurate vessel contour delineation (VCD) is required to achieve reliable results. We sought to evaluate manual analysis (MA) compared to ...

Heterogeneous treatment effects analysis for social scientists: A review.

Social science research
Social scientists have long been interested in the varying responses to a specific intervention, motivating the enterprise of heterogeneous treatment effects (HTE) analysis. Over the past five decades, the rapid development of HTE methods, from conve...

A review of critical challenges in MI-BCI: From conventional to deep learning methods.

Journal of neuroscience methods
Brain-computer interfaces (BCIs) have achieved significant success in controlling external devices through the Electroencephalogram (EEG) signal processing. BCI-based Motor Imagery (MI) system bridges brain and external devices as communication tools...