AIMC Topic: Adult

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Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models.

Scientific reports
In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large number of genetic variants with a high degree of r...

Preclinical Evaluation of a New ECCO2R Setup.

ASAIO journal (American Society for Artificial Internal Organs : 1992)
Low flow extracorporeal carbon dioxide removal (ECCO2R) is a promising approach to correct hypercapnic lung failure, facilitate lung protective ventilation in acute respiratory distress syndrome and to possibly prevent the application of invasive ven...

Artificial intelligence-based automatic assessment of lower limb torsion on MRI.

Scientific reports
Abnormal torsion of the lower limbs may adversely affect joint health. This study developed and validated a deep learning-based method for automatic measurement of femoral and tibial torsion on MRI. Axial T2-weighted sequences acquired of the hips, k...

Fully automatic segmentation of the mandible based on convolutional neural networks (CNNs).

Orthodontics & craniofacial research
OBJECTIVES: To evaluate the accuracy of automatic deep learning-based method for fully automatic segmentation of the mandible from CBCTs.

A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD.

NeuroImage
The pathological mechanism of attention deficit hyperactivity disorder (ADHD) is incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic resonance imaging (fMRI) has emerged as a common neuroimaging technique for s...

Deep learning-based quantification of temporalis muscle has prognostic value in patients with glioblastoma.

British journal of cancer
BACKGROUND: Glioblastoma is the commonest malignant brain tumour. Sarcopenia is associated with worse cancer survival, but manually quantifying muscle on imaging is time-consuming. We present a deep learning-based system for quantification of tempora...

Unsupervised Learning for Automated Detection of Coronary Artery Disease Subgroups.

Journal of the American Heart Association
Background The promise of precision population health includes the ability to use robust patient data to tailor prevention and care to specific groups. Advanced analytics may allow for automated detection of clinically informative subgroups that acco...

An interpretable machine learning model based on a quick pre-screening system enables accurate deterioration risk prediction for COVID-19.

Scientific reports
A high-performing interpretable model is proposed to predict the risk of deterioration in coronavirus disease 2019 (COVID-19) patients. The model was developed using a cohort of 3028 patients diagnosed with COVID-19 and exhibiting common clinical sym...

A fully automatic artificial intelligence-based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis.

European radiology
OBJECTIVES: An accurate and rapid diagnosis is crucial for the appropriate treatment of pulmonary tuberculosis (TB). This study aims to develop an artificial intelligence (AI)-based fully automated CT image analysis system for detection, diagnosis, a...

Deep neural network for video colonoscopy of ulcerative colitis: a cross-sectional study.

The lancet. Gastroenterology & hepatology
BACKGROUND: A combination of endoscopic and histological evaluation is important in the management of patients with ulcerative colitis. We aimed to adapt our previous deep neural network system (deep neural ulcerative colitis [DNUC]) to full video co...