AIMC Topic: Humans

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Deep Learning Reconstruction for Accelerated Spine MRI: Prospective Analysis of Interchangeability.

Radiology
Background Deep learning (DL)-based MRI reconstructions can reduce examination times for turbo spin-echo (TSE) acquisitions. Studies that prospectively employ DL-based reconstructions of rapidly acquired, undersampled spine MRI are needed. Purpose To...

Deep Learning Estimation of 10-2 Visual Field Map Based on Circumpapillary Retinal Nerve Fiber Layer Thickness Measurements.

American journal of ophthalmology
PURPOSE: To estimate central 10-degree visual field (VF) map from spectral-domain optical coherence tomography (SD-OCT) retinal nerve fiber layer thickness (RNFL) measurements in glaucoma with artificial intelligence.

Utopia versus dystopia: Professional perspectives on the impact of healthcare artificial intelligence on clinical roles and skills.

International journal of medical informatics
BACKGROUND: Alongside the promise of improving clinical work, advances in healthcare artificial intelligence (AI) raise concerns about the risk of deskilling clinicians. This purpose of this study is to examine the issue of deskilling from the perspe...

Strategies to Implement Pet Robots in Long-Term Care Facilities for Dementia Care: A Modified Delphi Study.

Journal of the American Medical Directors Association
OBJECTIVES: Pet robots are technology-based substitutes for live animals that have demonstrated psychosocial benefits for people living with dementia in long-term care. However, little research has been conducted to understand how pet robots should b...

Overcoming challenges of translating deep-learning models for glioblastoma: the ZGBM consortium.

The British journal of radiology
OBJECTIVE: To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep-learning models in glioblastoma care pathways. Additionally, to understand the most common i...

Comparison of atlas-based and deep learning methods for organs at risk delineation on head-and-neck CT images using an automated treatment planning system.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: To investigate the performance of head-and-neck (HN) organs-at-risk (OAR) automatic segmentation (AS) using four atlas-based (ABAS) and two deep learning (DL) solutions.

Explainable Deep-Learning-Assisted Sweat Assessment via a Programmable Colorimetric Chip.

Analytical chemistry
Multianalytes and individual differences of biofluids (such as blood, urine, or sweat) pose enormous complexity and challenges to rapid, facile, high-throughput, and accurate clinical analysis or health assessment. Deep-learning (DL)-assisted image a...