AIMC Topic: Humans

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Improved diagnosis of arrhythmogenic right ventricular cardiomyopathy using electrocardiographic deep learning.

Heart rhythm
BACKGROUND: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a rare genetic heart disease associated with life-threatening ventricular arrhythmias. Diagnosis of ARVC is based on the 2010 Task Force Criteria (TFC), application of which often ...

DECNet: Left Atrial Pulmonary Vein Class Imbalance Classification Network.

Journal of imaging informatics in medicine
In clinical practice, the anatomical classification of pulmonary veins plays a crucial role in the preoperative assessment of atrial fibrillation radiofrequency ablation surgery. Accurate classification of pulmonary vein anatomy assists physicians in...

2023 Industry Perceptions Survey on AI Adoption and Return on Investment.

Journal of imaging informatics in medicine
This SIIM-sponsored 2023 report highlights an industry view on artificial intelligence adoption barriers and success related to diagnostic imaging, life sciences, and contrasts. In general, our 2023 survey indicates that there has been progress in ad...

Confidence-Aware Severity Assessment of Lung Disease from Chest X-Rays Using Deep Neural Network on a Multi-Reader Dataset.

Journal of imaging informatics in medicine
In this study, we present a method based on Monte Carlo Dropout (MCD) as Bayesian neural network (BNN) approximation for confidence-aware severity classification of lung diseases in COVID-19 patients using chest X-rays (CXRs). Trained and tested on 1...

Effects of track-based stair climbing robot on muscle activity, usability, and psychological anxiety: a preliminary study.

Disability and rehabilitation. Assistive technology
This study investigated the effects of using the LiftCar-150 track-based stair-climbing robot on muscle activity, usability, and psychological anxiety. While stair-climbing robots enhance mobility for individuals with physical disabilities, existing ...

Deep learning and optimization enabled multi-objective for task scheduling in cloud computing.

Network (Bristol, England)
In cloud computing (CC), task scheduling allocates the task to best suitable resource for execution. This article proposes a model for task scheduling utilizing the multi-objective optimization and deep learning (DL) model. Initially, the multi-objec...

Exploring the role of artificial intelligence, large language models: Comparing patient-focused information and clinical decision support capabilities to the gynecologic oncology guidelines.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
Gynecologic cancer requires personalized care to improve outcomes. Large language models (LLMs) hold the potential to provide intelligent question-answering with reliable information about medical queries in clear and plain English, which can be unde...

Beyond the Conventional Structural MRI: Clinical Application of Deep Learning Image Reconstruction and Synthetic MRI of the Brain.

Investigative radiology
Recent technological advancements have revolutionized routine brain magnetic resonance imaging (MRI) sequences, offering enhanced diagnostic capabilities in intracranial disease evaluation. This review explores 2 pivotal breakthrough areas: deep lear...

Life and self-organization on the way to artificial intelligence for collective dynamics.

Physics of life reviews
This work is dedicated to the study, modeling, and simulation, of the collective dynamics of interacting living entities. The first perspective is to develop a mathematical theory of swarm intelligence for the above mentioned systems. The second pers...

SNN-BERT: Training-efficient Spiking Neural Networks for energy-efficient BERT.

Neural networks : the official journal of the International Neural Network Society
Spiking Neural Networks (SNNs) are naturally suited to process sequence tasks such as NLP with low power, due to its brain-inspired spatio-temporal dynamics and spike-driven nature. Current SNNs employ "repeat coding" that re-enter all input tokens a...