AIMC Topic: Humans

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Fully Automated Wound Tissue Segmentation Using Deep Learning on Mobile Devices: Cohort Study.

JMIR mHealth and uHealth
BACKGROUND: Composition of tissue types within a wound is a useful indicator of its healing progression. Tissue composition is clinically used in wound healing tools (eg, Bates-Jensen Wound Assessment Tool) to assess risk and recommend treatment. How...

Understanding the Research Landscape of Deep Learning in Biomedical Science: Scientometric Analysis.

Journal of medical Internet research
BACKGROUND: Advances in biomedical research using deep learning techniques have generated a large volume of related literature. However, there is a lack of scientometric studies that provide a bird's-eye view of them. This absence has led to a partia...

Application Optimization of University Aesthetic Education Resources Based on Few-Shot Learning from the Perspective of Ecological Aesthetic Education.

Computational intelligence and neuroscience
The idea of EAE (Ecological Aesthetic Education) is put forward on the basis of the mature development of ecological aesthetics and AE theory. Starting with EAE, establishing people's aesthetic attitude and improving people's spiritual realm will hel...

Computed Tomography Images under Artificial Intelligence Algorithms on the Treatment Evaluation of Intracerebral Hemorrhage with Minimally Invasive Aspiration.

Computational and mathematical methods in medicine
The aim of this study was to investigate the therapeutic effect of minimally invasive aspiration on intracerebral hemorrhage (ICH) and the value of artificial intelligence algorithm combined with computed tomography (CT) image evaluation. Ninety-two ...

The clinical effects of brain-computer interface with robot on upper-limb function for post-stroke rehabilitation: a meta-analysis and systematic review.

Disability and rehabilitation. Assistive technology
PURPOSE: Many recent clinical studies have suggested that the combination of brain-computer interfaces (BCIs) can induce neurological recovery and improvement in motor function. In this review, we performed a systematic review and meta-analysis to ev...

Histopathology-Based Diagnosis of Oral Squamous Cell Carcinoma Using Deep Learning.

Journal of dental research
Oral squamous cell carcinoma (OSCC) is prevalent around the world and is associated with poor prognosis. OSCC is typically diagnosed from tissue biopsy sections by pathologists who rely on their empirical experience. Deep learning models may improve ...

Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncology.

Clinical and molecular hepatology
Molecular tests are necessary to stratify cancer patients for targeted therapy. However, high cost and technical barriers limit the application of these tests, hindering optimal treatment. Recently, deep learning (DL) has been applied to predict mole...

Pseudoprospective Paraclinical Interaction of Radiology Residents With a Deep Learning System for Prostate Cancer Detection: Experience, Performance, and Identification of the Need for Intermittent Recalibration.

Investigative radiology
OBJECTIVES: The aim of this study was to estimate the prospective utility of a previously retrospectively validated convolutional neural network (CNN) for prostate cancer (PC) detection on prostate magnetic resonance imaging (MRI).

Artificial Intelligence and Surgery: Ethical Dilemmas and Open Issues.

Journal of the American College of Surgeons
BACKGROUND: Artificial intelligence (AI) applications aiming to support surgical decision-making processes are generating novel threats to ethical surgical care. To understand and address these threats, we summarize the main ethical issues that may a...

Deep neural networks learn general and clinically relevant representations of the ageing brain.

NeuroImage
The discrepancy between chronological age and the apparent age of the brain based on neuroimaging data - the brain age delta - has emerged as a reliable marker of brain health. With an increasing wealth of data, approaches to tackle heterogeneity in ...