Improvement of stereopsis following strabismus surgery in adults: a retrospective analysis.

Objective: Adults who undergo strabismus surgery, in addition to cosmesis, could benefit from improved stereopsis. This improvement is associated with the performance of motor skill tasks in young adults; they reduce the risk of tripping or falling during everyday locomotion and improve reading efficiency. This study aimed to assess stereopsis level after strabismus surgery in adults who underwent strabismus surgery for any reason.

Artificial intelligence improves risk prediction in cardiovascular disease.

Cardiovascular disease (CVD) represents a major public health issue, claiming numerous lives. This study aimed to demonstrate the advantages of employing artificial intelligence (AI) models to improve the prediction of CVD risk using a large cohort of relatively healthy adults aged 70 years or more. In this study, deep learning (DL) models provide enhanced predictions (DeepSurv: C-index = 0.

Navigating artificial intelligence in care homes: Competing stakeholder views of trust and logics of care.

The COVID-19 pandemic shed light on systemic issues plaguing care (nursing) homes, from staff shortages to substandard healthcare. Artificial Intelligence (AI) technologies, including robots and chatbots, have been proposed as solutions to such issues. Yet, socio-ethical concerns about the implications of AI for health and care practices have also been growing among researchers and practitioners.

A Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction.

Artificial intelligence (AI) based predictive models for early detection of cardiovascular disease (CVD) risk are increasingly being utilised. However, AI based risk prediction models that account for right-censored data have been overlooked. This systematic review (PROSPERO protocol CRD42023492655) includes 33 studies that utilised machine learning (ML) and deep learning (DL) models for survival outcome in CVD prediction.

Gender-specific aspects of socialisation and risk of cardiovascular disease among community-dwelling older adults: a prospective cohort study using machine learning algorithms and a conventional method.

Background: Gender influences cardiovascular disease (CVD) through norms, social relations, roles and behaviours. This study identified gender-specific aspects of socialisation associated with CVD.

Methods: A longitudinal study was conducted, involving 9936 (5,231 women and 4705 men) initially healthy, community-dwelling Australians aged 70 years or more from the ASPirin in Reducing Events in the Elderly (ASPREE) study and ASPREE Longitudinal Study of Older Persons, with a median follow-up time of 6.

Artificial Intelligence in Long-Term Care: Technological Promise, Aging Anxieties, and Sociotechnical Ageism.

This article explores views about older people and aging underpinning practices and perceptions of development and implementation of Artificial Intelligence (AI) in long-term care homes (LTC). Drawing on semi-structured interviews with seven AI developers, seven LTC staff, and four LTC advocates, we analyzed how AI technologies for later life are imagined, designed, deployed, and resisted. Using the concepts of "promissory discourse" and "aging anxieties", we investigated manifestations of ageism in accounts of AI applications in LTC.

A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences.

Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to conquer many technology products and their fields of application. Machine learning, as a major part of the current AI solutions, can learn from the data and through experience to reach high performance on various tasks. This growing success of AI algorithms has led to a need for interpretability to understand opaque models such as deep neural networks.

Behaviour Recognition with Kinodynamic Planning Over Continuous Domains.

We investigate the application of state-of-the-art goal recognition techniques for recognition over complex continuous domains using model predictive control (MPC) for trajectory generation. We formally define the problem of kinodynamic behaviour recognition and establish a set of baseline behaviours and performance measures in the complex domain of unmanned aerial maneuvers. We evaluate how well our approach performs over a range of standard aerial maneuvers and representative initial configurations of varying complexity.