AIMC Topic: Aged

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Prediction of postoperative visual cognitive impairment using graph theory and machine learning based on resting-state brain networks.

BMC medical imaging
BACKGROUND: Visual cognitive impairment is among the most common postoperative cognitive dysfunctions, significantly impacting recovery and quality of life in elderly patients. However, effective preoperative prediction methods remain lacking. We dev...

A deep learning model for predicting radiation-induced xerostomia in patients with head and neck cancer based on multi-channel fusion.

BMC medical imaging
OBJECTIVES: Radiation-induced xerostomia is a common sequela in patients who undergo head and neck radiation therapy. This study aims to develop a three-dimensional deep learning model to predict xerostomia by fusing data from the gross tumor volume ...

Systematic data management for effective AI-driven decision support systems in robotic rehabilitation.

Scientific reports
Robotic rehabilitation is becoming a standard in post-stroke physical rehabilitation, and these setups, often coupled with virtual exercises, collect a large and finely grained amount of data about patients' motor performance, in terms of kinematics ...

Association between atherogenic index of plasma and hypertension combined with diabetes mellitus in United States adults: an analysis of the NHANES surveys from 2011 to 2016.

Journal of health, population, and nutrition
INTRODUCTION: Observational studies have indicated that individuals with hypertension (HTN) and diabetes mellitus (DM) tend to exhibit elevated plasma atherogenic index of plasma (AIP), defined as log (triglyceride [TG]/high-density lipoprotein chole...

Ultrasound derived deep learning features for predicting axillary lymph node metastasis in breast cancer using graph convolutional networks in a multicenter study.

Scientific reports
The purpose of this study was to create and validate an ultrasound-based graph convolutional network (US-based GCN) model for the prediction of axillary lymph node metastasis (ALNM) in patients with breast cancer. A total of 820 eligible patients wit...

Machine Learning-Based Cognitive Assessment With The Autonomous Cognitive Examination: Randomized Controlled Trial.

Journal of medical Internet research
BACKGROUND: The rising prevalence of dementia necessitates a scalable solution to cognitive assessments. The Autonomous Cognitive Examination (ACoE) is a foundational cognitive test for the phenotyping of cognitive symptoms across the primary cogniti...

Optimizing Thyroid Nodule Management With Artificial Intelligence: Multicenter Retrospective Study on Reducing Unnecessary Fine Needle Aspirations.

JMIR medical informatics
BACKGROUND: Most artificial intelligence (AI) models for thyroid nodules are designed to screen for malignancy to guide further interventions; however, these models have not yet been fully implemented in clinical practice.

Predicting the clinical evolution of septic patients from routinely collected data and vital signs variability using machine learning.

Physiological measurement
The existing literature lacks a comprehensive analysis of the clinical evolution of septic patients, which is highly heterogeneous and patient-dependent. The aim of this study is to develop machine learning models capable of predicting the clinical e...

Analysis of liquid biopsy by Raman spectroscopy to facilitate prediction of response to immunotherapy in non-small-cell lung cancer (NSCLC) patients.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Immunotherapy has revolutionized lung cancer treatment, yet predicting patient response remains a challenge. This study used Raman spectroscopy to differentiate between non-small-cell lung cancer patients with short-lasting and long-lasting responses...

Knee osteoarthritis prediction from gait kinematics: Exploring the potential of deep neural networks and transfer learning methods for time series classification.

Journal of biomechanics
Recent advances in artificial intelligence methods have allowed improved disease diagnosis using fast and low-cost protocols. The present study explored the potential of different deep neural networks (DNNs) and transfer learning methods to detect kn...