AI Medical Compendium Topic

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Automation

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Automation of Quantifying Axonal Loss in Patients with Peripheral Neuropathies through Deep Learning Derived Muscle Fat Fraction.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Axonal loss denervates muscle, leading to an increase of fat accumulation in the muscle. Therefore, fat fraction (FF) in whole limb muscle using MRI has emerged as a monitoring biomarker for axonal loss in patients with peripheral neuropa...

Automated segmentation of endometrial cancer on MR images using deep learning.

Scientific reports
Preoperative MR imaging in endometrial cancer patients provides valuable information on local tumor extent, which routinely guides choice of surgical procedure and adjuvant therapy. Furthermore, whole-volume tumor analyses of MR images may provide ra...

Supervised machine learning for automated classification of human Wharton's Jelly cells and mechanosensory hair cells.

PloS one
Tissue engineering and gene therapy strategies offer new ways to repair permanent damage to mechanosensory hair cells (MHCs) by differentiating human Wharton's Jelly cells (HWJCs). Conventionally, these strategies require the classification of each c...

A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers.

Tuberculosis (Edinburgh, Scotland)
Recently, the number of artificial intelligence powered computer-aided detection (CAD) products that detect tuberculosis (TB)-related abnormalities from chest X-rays (CXR) available on the market has increased. Although CXR is a relatively effective ...

Automated Counting Grains on the Rice Panicle Based on Deep Learning Method.

Sensors (Basel, Switzerland)
Grain number per rice panicle, which directly determines grain yield, is an important agronomic trait for rice breeding and yield-related research. However, manually counting grains of rice per panicle is time-consuming, laborious, and error-prone. I...

Machine learning enables design automation of microfluidic flow-focusing droplet generation.

Nature communications
Droplet-based microfluidic devices hold immense potential in becoming inexpensive alternatives to existing screening platforms across life science applications, such as enzyme discovery and early cancer detection. However, the lack of a predictive un...

A physics-guided modular deep-learning based automated framework for tumor segmentation in PET.

Physics in medicine and biology
An important need exists for reliable positron emission tomography (PET) tumor-segmentation methods for tasks such as PET-based radiation-therapy planning and reliable quantification of volumetric and radiomic features. To address this need, we propo...

Automated age estimation of young individuals based on 3D knee MRI using deep learning.

International journal of legal medicine
Age estimation is a crucial element of forensic medicine to assess the chronological age of living individuals without or lacking valid legal documentation. Methods used in practice are labor-intensive, subjective, and frequently comprise radiation e...

Fully Automatic Atrial Fibrosis Assessment Using a Multilabel Convolutional Neural Network.

Circulation. Cardiovascular imaging
BACKGROUND: Pathological atrial fibrosis is a major contributor to sustained atrial fibrillation. Currently, late gadolinium enhancement (LGE) scans provide the only noninvasive estimate of atrial fibrosis. However, widespread adoption of atrial LGE ...