AIMC Topic: Humans

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COVID-19 Surveillance in a Primary Care Sentinel Network: In-Pandemic Development of an Application Ontology.

JMIR public health and surveillance
BACKGROUND: Creating an ontology for COVID-19 surveillance should help ensure transparency and consistency. Ontologies formalize conceptualizations at either the domain or application level. Application ontologies cross domains and are specified thro...

Using the National Trauma Data Bank (NTDB) and machine learning to predict trauma patient mortality at admission.

PloS one
A 400-estimator gradient boosting classifier was trained to predict survival probabilities of trauma patients. The National Trauma Data Bank (NTDB) provided 799233 complete patient records (778303 survivors and 20930 deaths) each containing 32 featur...

An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images.

PloS one
A newly emerged coronavirus (COVID-19) seriously threatens human life and health worldwide. In coping and fighting against COVID-19, the most critical step is to effectively screen and diagnose infected patients. Among them, chest X-ray imaging techn...

Artificial Intelligence for Surgical Safety: Automatic Assessment of the Critical View of Safety in Laparoscopic Cholecystectomy Using Deep Learning.

Annals of surgery
OBJECTIVE: To develop a deep learning model to automatically segment hepatocystic anatomy and assess the criteria defining the critical view of safety (CVS) in laparoscopic cholecystectomy (LC).

Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Hip.

The Journal of arthroplasty
BACKGROUND: The surgical management of complications surrounding patients who have undergone hip arthroplasty necessitates accurate identification of the femoral implant manufacturer and model. Failure to do so risks delays in care, increased morbidi...

Type IV Collagen 7S Is the Most Accurate Test For Identifying Advanced Fibrosis in NAFLD With Type 2 Diabetes.

Hepatology communications
This study aimed to examine whether the diagnostic accuracy of four noninvasive tests (NITs) for detecting advanced fibrosis in nonalcoholic fatty liver disease (NAFLD) is maintained or is inferior to with or without the presence of type 2 diabetes. ...

Robust facial landmark detection by cross-order cross-semantic deep network.

Neural networks : the official journal of the International Neural Network Society
Recently, convolutional neural networks (CNNs)-based facial landmark detection methods have achieved great success. However, most of existing CNN-based facial landmark detection methods have not attempted to activate multiple correlated facial parts ...

Visualizing "featureless" regions on mammograms classified as invasive ductal carcinomas by a deep learning algorithm: the promise of AI support in radiology.

Japanese journal of radiology
PURPOSE: To demonstrate how artificial intelligence (AI) can expand radiologists' capacity, we visualized the features of invasive ductal carcinomas (IDCs) that our algorithm, developed and validated for basic pathological classification on mammogram...

Methodological considerations in MVC epidemiological research.

Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention
BACKGROUND: The global burden of MVC injuries and deaths among vulnerable road users, has led to the implementation of prevention programmes and policies at the local and national level. MVC epidemiological research is key to quantifying MVC burden, ...