AIMC Topic: Adult

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Prediction of cardiovascular risk factors from retinal fundus photographs: Validation of a deep learning algorithm in a prospective non-interventional study in Kenya.

Diabetes, obesity & metabolism
AIM: Hypertension and diabetes mellitus (DM) are major causes of morbidity and mortality, with growing burdens in low-income countries where they are underdiagnosed and undertreated. Advances in machine learning may provide opportunities to enhance d...

Comparing preferences for skin cancer screening: AI-enabled app vs dermatologist.

Social science & medicine (1982)
BACKGROUND AND AIM: Skin cancer is a major public health issue. While self-examinations and professional screenings are recommended, they are rarely performed. Mobile health (mHealth) apps utilising artificial intelligence (AI) for skin cancer screen...

Deep-learning-based real-time individualization for reduce-order haemodynamic model.

Computers in biology and medicine
The reduced-order lumped parameter model (LPM) has great computational efficiency in real-time numerical simulations of haemodynamics but is limited by the accuracy of patient-specific computation. This study proposed a method to achieve the individu...

The relationship between heavy metals and metabolic syndrome using machine learning.

Frontiers in public health
BACKGROUND: Exposure to high levels of heavy metals has been widely recognized as an important risk factor for metabolic syndrome (MetS). The main purpose of this study is to assess the associations between the level of heavy metal exposure and Mets ...

Geriatrics and artificial intelligence in Spain (Ger-IA project): talking to ChatGPT, a nationwide survey.

European geriatric medicine
PURPOSE: The purposes of the study was to describe the degree of agreement between geriatricians with the answers given by an AI tool (ChatGPT) in response to questions related to different areas in geriatrics, to study the differences between specia...

vEpiNet: A multimodal interictal epileptiform discharge detection method based on video and electroencephalogram data.

Neural networks : the official journal of the International Neural Network Society
To enhance deep learning-based automated interictal epileptiform discharge (IED) detection, this study proposes a multimodal method, vEpiNet, that leverages video and electroencephalogram (EEG) data. Datasets comprise 24 931 IED (from 484 patients) a...

A multimodal approach using fundus images and text meta-data in a machine learning classifier with embeddings to predict years with self-reported diabetes - An exploratory analysis.

Primary care diabetes
AIMS: Machine learning models can use image and text data to predict the number of years since diabetes diagnosis; such model can be applied to new patients to predict, approximately, how long the new patient may have lived with diabetes unknowingly....

Distributing Blame Among Multiple Entities When Autonomous Technologies Cause Harm.

Personality & social psychology bulletin
As autonomous technology emerges, new variations in old questions arise. When autonomous technologies cause harm, who is to blame? The current studies compare reactions toward harms caused by human-controlled vehicles (HCVs) or human soldiers (HSs) t...