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Parsing Immune Correlates of Protection Against SARS-CoV-2 from Biomedical Literature.

AMIA ... Annual Symposium proceedings. AMIA Symposium
After the emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in 2019, identification of immune correlates of protection (CoPs) have become increasingly important to understand the immune response to SARS-CoV-2. The vast amount ...

A pilot study for the prediction of liver function related scores using breath biomarkers and machine learning.

Scientific reports
Volatile organic compounds (VOCs) present in exhaled breath can help in analysing biochemical processes in the human body. Liver diseases can be traced using VOCs as biomarkers for physiological and pathophysiological conditions. In this work, we pro...

Exploring the use of machine learning for the assessment of skeletal fracture morphology and differentiation between impact mechanisms: A pilot study.

Journal of forensic sciences
Analyzing and interpreting traumatic injuries is a fundamental aspect of routine forensic case work. As the human skeleton can be impacted through a combination of loading mechanisms and varying impact energies, the analysis and interpretation of ske...

Indocyanine Green Drives Computer Vision Based 3D Augmented Reality Robot Assisted Partial Nephrectomy: The Beginning of "Automatic" Overlapping Era.

Urology
Augmented reality robot-assisted partial nephrectomy (AR-RAPN) is limited by the need of a constant manual overlapping of the hyper-accuracy 3D (HA3D) virtual models to the real anatomy. To present our preliminary experience with automatic 3D virtual...

A pilot study of a deep learning approach to detect marginal bone loss around implants.

BMC oral health
BACKGROUND: Recently, there has been considerable innovation in artificial intelligence (AI) for healthcare. Convolutional neural networks (CNNs) show excellent object detection and classification performance. This study assessed the accuracy of an a...

Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients.

Rheumatology international
The study proposes a novel machine learning (ML) paradigm for cardiovascular disease (CVD) detection in individuals at medium to high cardiovascular risk using data from a Greek cohort of 542 individuals with rheumatoid arthritis, or diabetes mellitu...

A pilot study of machine-learning based automated planning for primary brain tumours.

Radiation oncology (London, England)
PURPOSE: High-quality radiotherapy (RT) planning for children and young adults with primary brain tumours is essential to minimize the risk of late treatment effects. The feasibility of using automated machine-learning (ML) to aid RT planning in this...

Image quality and radiologists' subjective acceptance using model-based iterative and deep learning reconstructions as adjuncts to ultrahigh-resolution CT in low-dose contrast-enhanced abdominopelvic CT: phantom and clinical pilot studies.

Abdominal radiology (New York)
PURPOSE: In contrast-enhanced abdominopelvic CT (CE-APCT) for oncologic follow-up, ultrahigh-resolution CT (UHRCT) may improve depiction of fine lesions and low-dose scans are desirable for minimizing the potential adverse effects by ionizing radiati...

A New Socially Assistive Robot with Integrated Serious Games for Therapies with Children with Autism Spectrum Disorder and Down Syndrome: A Pilot Study.

Sensors (Basel, Switzerland)
This work introduces a new socially assistive robot termed MARIA T21 (meaning "Mobile Autonomous Robot for Interaction with Autistics", with the addition of the acronym T21, meaning "Trisomy 21", which is used to designate individuals with Down syndr...