Expectations and Requirements of Surgical Staff for an AI-Supported Clinical Decision Support System for Older Patients: Qualitative Study.

Background: Geriatric comanagement has been shown to improve outcomes of older surgical inpatients. Furthermore, the choice of discharge location, that is, continuity of care, can have a fundamental impact on convalescence. These challenges and demands have led to the SURGE-Ahead project that aims to develop a clinical decision support system (CDSS) for geriatric comanagement in surgical clinics including a decision support for the best continuity of care option, supported by artificial intelligence (AI) algorithms.

Use and benefit of information, communication, and assistive technology among community-dwelling older adults - a cross-sectional study.

Background: Technology can support healthy aging and empower older adults to live independently. However, technology adoption by older adults, particularly assistive technology (AT), is limited and little is known about the types of AT used among older adults. This study explored the use of key information and communication technologies (ICT) and AT among community-dwelling adults aged ≥ 65.

Artificial Intelligence-Based Clinical Decision Support Systems in Geriatrics: An Ethical Analysis.

Objectives: To provide an ethical analysis of the implications of the usage of artificial intelligence-supported clinical decision support systems (AI-CDSS) in geriatrics.

Design: Ethical analysis based on the normative arguments regarding the use of AI-CDSS in geriatrics using a principle-based ethical framework.

Setting And Participants: Normative arguments identified in 29 articles on AI-CDSS in geriatrics.

Artificial neural networks for non-linear age correction of diffusion metrics in the brain.

Human aging is characterized by progressive loss of physiological functions. To assess changes in the brain that occur with increasing age, the concept of brain aging has gained momentum in neuroimaging with recent advancements in statistical regression and machine learning (ML). A common technique to assess the brain age of a person is, first, fitting a regression model to neuroimaging data from a group of healthy subjects, and then, using the resulting model for age prediction.

Multiparametric Microstructural MRI and Machine Learning Classification Yields High Diagnostic Accuracy in Amyotrophic Lateral Sclerosis: Proof of Concept.

The potential of multiparametric quantitative neuroimaging has been extensively discussed as a diagnostic tool in amyotrophic lateral sclerosis (ALS). In the past, the integration of multimodal, quantitative data into a useful diagnostic classifier was a major challenge. With recent advances in the field, machine learning in a data driven approach is a potential solution: neuroimaging biomarkers in ALS are mainly observed in the cerebral microstructure, with diffusion tensor imaging (DTI) and texture analysis as promising approaches.

Feature selection from magnetic resonance imaging data in ALS: a systematic review.

Background: With the advances in neuroimaging in amyotrophic lateral sclerosis (ALS), it has been speculated that multiparametric magnetic resonance imaging (MRI) is capable to contribute to early diagnosis. Machine learning (ML) can be regarded as the missing piece that allows for the useful integration of multiparametric MRI data into a diagnostic classifier. The major challenges in developing ML classifiers for ALS are limited data quantity and a suboptimal sample to feature ratio which can be addressed by sound feature selection.

Differential functional connectivity in thalamic and dopaminergic pathways in restless legs syndrome: a meta-analysis.

Objective: Restless legs syndrome (RLS) is a sensorimotor disorder with alterations in somatosensory processing in association with a dysfunctional cerebral network, involving the basal ganglia, limbic network, and sensorimotor pathways. Resting state functional magnetic resonance imaging (MRI) is a powerful tool to provide insight into functional processing and as such is of special interest in RLS considering the widespread pattern of networks involved in this disorder. In this meta-analysis of resting state functional MRI studies, we analyzed the preponderance of functional connectivity changes associated with RLS and discussed possible links to sensorimotor dysfunction and somatosensory processing.