AIMC Topic: Humans

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Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements.

Scientific reports
Chronic kidney disease (CKD) is a global health concern with early detection playing a pivotal role in effective management. Machine learning models demonstrate promise in CKD detection, yet the impact on detection and classification using different ...

Constructing a machine learning model for systemic infection after kidney stone surgery based on CT values.

Scientific reports
This study aims to develop a machine learning model utilizing Computed Tomography (CT) values to predict systemic inflammatory response syndrome (SIRS) after endoscopic surgery for kidney stones. The goal is to identify high-risk patients early and p...

Breast cancer classification based on hybrid CNN with LSTM model.

Scientific reports
Breast cancer (BC) is a global problem, largely due to a shortage of knowledge and early detection. The speed-up process of detection and classification is crucial for effective cancer treatment. Medical image analysis methods and computer-aided diag...

Eye-tracking dataset of endoscopist-AI teaming during colonoscopy: Retrospective and real-time acquisition.

Scientific data
Recent studies have demonstrated that integrating AI into colonoscopy procedures significantly improves the adenoma detection rate (ADR) and reduces the adenoma miss rate (AMR). However, few studies address the critical issue of endoscopist-AI collab...

Machine learning research methods to predict postoperative pain and opioid use: a narrative review.

Regional anesthesia and pain medicine
The use of machine learning to predict postoperative pain and opioid use has likely been catalyzed by the availability of complex patient-level data, computational and statistical advancements, the prevalence and impact of chronic postsurgical pain, ...

AI for IMPACTS Framework for Evaluating the Long-Term Real-World Impacts of AI-Powered Clinician Tools: Systematic Review and Narrative Synthesis.

Journal of medical Internet research
BACKGROUND: Artificial intelligence (AI) has the potential to revolutionize health care by enhancing both clinical outcomes and operational efficiency. However, its clinical adoption has been slower than anticipated, largely due to the absence of com...

Capturing Requirements for a Data Annotation Tool for Intensive Care: Experimental User-Centered Design Study.

JMIR human factors
BACKGROUND: Increasing use of computational methods in health care provides opportunities to address previously unsolvable problems. Machine learning techniques applied to routinely collected data can enhance clinical tools and improve patient outcom...

Detection of Alzheimer Disease in Neuroimages Using Vision Transformers: Systematic Review and Meta-Analysis.

Journal of medical Internet research
BACKGROUND: Alzheimer disease (AD) is a progressive condition characterized by cognitive decline and memory loss. Vision transformers (ViTs) are emerging as promising deep learning models in medical imaging, with potential applications in the detecti...

Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study.

JMIR medical informatics
BACKGROUND: Modern lifestyle risk factors, like physical inactivity and poor nutrition, contribute to rising rates of obesity and chronic diseases like type 2 diabetes and heart disease. Particularly personalized interventions have been shown to be e...

A multi-domain feature fusion epilepsy seizure detection method based on spike matching and PLV functional networks.

Journal of neural engineering
The identification of spikes, as a typical characteristic wave of epilepsy, is crucial for diagnosing and locating the epileptogenic region. The traditional seizure detection methods lack spike features and have low sample richness. This paper propos...