AI Medical Compendium Topic:
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Identifying schizophrenia subgroups using clustering and supervised learning.

Schizophrenia research
Schizophrenia has a 1% incidence rate world-wide and those diagnosed present with positive (e.g. hallucinations, delusions), negative (e.g. apathy, asociality), and cognitive symptoms. However, both symptom burden and associated brain alterations are...

Deep Learning Classifiers for Automated Detection of Gonioscopic Angle Closure Based on Anterior Segment OCT Images.

American journal of ophthalmology
PURPOSE: To develop and test deep learning classifiers that detect gonioscopic angle closure and primary angle closure disease (PACD) based on fully automated analysis of anterior segment OCT (AS-OCT) images.

Peak alignment of gas chromatography-mass spectrometry data with deep learning.

Journal of chromatography. A
We present ChromAlignNet, a deep learning model for alignment of peaks in Gas Chromatography-Mass Spectrometry (GC-MS) data. In GC-MS data, a compound's retention time (RT) may not stay fixed across multiple chromatograms. To use GC-MS data for bioma...

Convolution kernel and iterative reconstruction affect the diagnostic performance of radiomics and deep learning in lung adenocarcinoma pathological subtypes.

Thoracic cancer
BACKGROUND: The aim of this study was to investigate the influence of convolution kernel and iterative reconstruction on the diagnostic performance of radiomics and deep learning (DL) in lung adenocarcinomas.

Increase Trichomonas vaginalis detection based on urine routine analysis through a machine learning approach.

Scientific reports
Trichomonas vaginalis (T. vaginalis) detection remains an unsolved problem in using of automated instruments for urinalysis. The study proposes a machine learning (ML)-based strategy to increase the detection rate of T. vaginalis in urine. On the bas...

ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU.

Journal of biomedical informatics
To improve the performance of Intensive Care Units (ICUs), the field of bio-statistics has developed scores which try to predict the likelihood of negative outcomes. These help evaluate the effectiveness of treatments and clinical practice, and also ...

Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: A multicenter radiomics study.

Magnetic resonance imaging
BACKGROUND AND PURPOSE: Advanced imaging analysis for the prediction of tumor biology and modelling of clinically relevant parameters using computed imaging features is part of the emerging field of radiomics research. Here we test the hypothesis tha...

Machine learning versus physicians' prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor.

Critical care (London, England)
BACKGROUND: Early diagnosis of acute kidney injury (AKI) is a major challenge in the intensive care unit (ICU). The AKIpredictor is a set of machine-learning-based prediction models for AKI using routinely collected patient information, and accessibl...

An improved fuzzy-differential evolution approach applied to classification of tumors in liver CT scan images.

Medical & biological engineering & computing
Fuzzy inference systems have been frequently used in medical diagnosis for managing uncertainty sources in the medical images. In addition, fuzzy systems have high level of interpretability because of using linguistic terms for knowledge representati...

A deep learning model for real-time mortality prediction in critically ill children.

Critical care (London, England)
BACKGROUND: The rapid development in big data analytics and the data-rich environment of intensive care units together provide unprecedented opportunities for medical breakthroughs in the field of critical care. We developed and validated a machine l...