AIMC Topic: Middle Aged

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Deep Learning for Distinguishing Mucinous Breast Carcinoma From Fibroadenoma on Ultrasound.

Clinical breast cancer
PURPOSE: Mucinous breast carcinoma (MBC) tends to be misdiagnosed as fibroadenomas (FA) due to its benign imaging characteristics. We aimed to develop a deep learning (DL) model to differentiate MBC and FA based on ultrasound (US) images. The model c...

Enhancing Heart Failure Care: Deep Learning-Based Activity Classification in Left Ventricular Assist Device Patients.

ASAIO journal (American Society for Artificial Internal Organs : 1992)
Accurate activity classification is essential for the advancement of closed-loop control for left ventricular assist devices (LVADs), as it provides necessary feedback to adapt device operation to the patient's current state. Therefore, this study ai...

External Validation Demonstrates Machine Learning Models Outperform Human Experts in Prediction of Objective and Patient-reported Overactive Bladder Treatment Outcomes.

Urology
OBJECTIVE: To predict treatment response for overactive bladder (OAB) for a specific patient remains elusive. We sought to develop accurate models using machine learning for prediction of objective and patient-reported treatment response to intravesi...

The accuracy of deep learning models for diagnosing maxillary fungal ball rhinosinusitis.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
PURPOSE: To assess the accuracy of deep learning models for the diagnosis of maxillary fungal ball rhinosinusitis (MFB) and to compare the accuracy, sensitivity, specificity, precision, and F1-score with a rhinologist.

Prospective Evaluation of Artificial Intelligence Triage of Intracranial Hemorrhage on Noncontrast Head CT Examinations.

AJR. American journal of roentgenology
Retrospective studies evaluating artificial intelligence (AI) algorithms for intracranial hemorrhage (ICH) detection on noncontrast CT (NCCT) have shown promising results but lack prospective validation. The purpose of this article was to evaluate ...

Classifying High-Risk Patients for Persistent Opioid Use After Major Spine Surgery: A Machine-Learning Approach.

Anesthesia and analgesia
BACKGROUND: Persistent opioid use is a common occurrence after surgery and prolonged exposure to opioids may result in escalation and dependence. The objective of this study was to develop machine-learning-based predictive models for persistent opioi...

A new modular neuroprosthesis suitable for hybrid FES-robot applications and tailored assistance.

Journal of neuroengineering and rehabilitation
BACKGROUND: To overcome the application limitations of functional electrical stimulation (FES), such as fatigue or nonlinear muscle response, the combination of neuroprosthetic systems with robotic devices has been evaluated, resulting in hybrid syst...

Prediction of metabolic syndrome using machine learning approaches based on genetic and nutritional factors: a 14-year prospective-based cohort study.

BMC medical genomics
INTRODUCTION: Metabolic syndrome is a chronic disease associated with multiple comorbidities. Over the last few years, machine learning techniques have been used to predict metabolic syndrome. However, studies incorporating demographic, clinical, lab...

Automated Association for Osteosynthesis Foundation and Orthopedic Trauma Association classification of pelvic fractures on pelvic radiographs using deep learning.

Scientific reports
High-energy impacts, like vehicle crashes or falls, can lead to pelvic ring injuries. Rapid diagnosis and treatment are crucial due to the risks of severe bleeding and organ damage. Pelvic radiography promptly assesses fracture extent and location, b...