AIMC Topic: Humans

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Deep learning for noninvasive liver fibrosis classification: A systematic review.

Liver international : official journal of the International Association for the Study of the Liver
BACKGROUND AND AIMS: While biopsy is the gold standard for liver fibrosis staging, it poses significant risks. Noninvasive assessment of liver fibrosis is a growing field. Recently, deep learning (DL) technology has revolutionized medical image analy...

The Role of Tele-Critical Care in Rescue and Resuscitation.

Critical care nursing clinics of North America
Tele-critical care (TCC) is a health care delivery model that connects medical information, interprofessional teams, patients, and families through advanced pathways, such as audio-video interfaces, machine learning, risk prediction algorithms, smart...

Effectiveness of resampling methods in coping with imbalanced crash data: Crash type analysis and predictive modeling.

Accident; analysis and prevention
Crash data analysis is commonly subjected to imbalanced data. Varied by facility and control types, some crash types are more frequent than others. However, uncommon crash types are routinely more severe and associated with higher economic and societ...

Investigation of clinical target volume segmentation for whole breast irradiation using three-dimensional convolutional neural networks with gradient-weighted class activation mapping.

Radiological physics and technology
This study aims to implement three-dimensional convolutional neural networks (3D-CNN) for clinical target volume (CTV) segmentation for whole breast irradiation and investigate the focus of 3D-CNNs during decision-making using gradient-weighted class...

Performance evaluation of a deep learning image reconstruction (DLIR) algorithm in "double low" chest CTA in children: a feasibility study.

La Radiologia medica
BACKGROUND: Chest CT angiography (CTA) is a convenient clinical examination for children with an increasing need to reduce both radiation and contrast medium doses. Iterative Reconstruction algorithms are often used to reduce image noise but encounte...

Application of machine learning to large in-vitro databases to identify cancer cell characteristics: telomerase reverse transcriptase (TERT) expression.

Oncogene
Advances in biotechnology and machine learning have created an enhanced environment for unearthing and exploiting previously unrecognized relationships between genomic and epigenetic data with potential therapeutic implications. We applied advanced a...

Objective pain stimulation intensity and pain sensation assessment using machine learning classification and regression based on electrodermal activity.

American journal of physiology. Regulatory, integrative and comparative physiology
An objective measure of pain remains an unmet need of people with chronic pain, estimated to be 1/3 of the adult population in the United States. The current gold standard to quantify pain is highly subjective, based upon self-reporting with numerica...

Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database.

Molecular diversity
Alzheimer's disease is the most common form of dementia, representing 60-70% of dementia cases. The enzyme acetylcholinesterase (AChE) cleaves the ester bonds in acetylcholine and plays an important role in the termination of acetylcholine activity a...

Application of deep learning in the detection of breast lesions with four different breast densities.

Cancer medicine
OBJECTIVE: This retrospective study evaluated the model from populations with different breast densities and showed the model's performance on malignancy prediction.