AIMC Topic: Humans

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Functional outcomes after robot-assisted radical cystectomy: A review of literature.

International journal of urology : official journal of the Japanese Urological Association
Robot-assisted radical cystectomy has steadily gained wider acceptance among urologists compared with open and laparoscopic approaches. Robot-assisted radical cystectomy has shown comparable perioperative and oncologic outcomes compared with open rad...

Machine Learning Reduced Gene/Non-Coding RNA Features That Classify Schizophrenia Patients Accurately and Highlight Insightful Gene Clusters.

International journal of molecular sciences
RNA-seq has been a powerful method to detect the differentially expressed genes/long non-coding RNAs (lncRNAs) in schizophrenia (SCZ) patients; however, due to overfitting problems differentially expressed targets (DETs) cannot be used properly as bi...

[Artificial intelligence in psychiatry: predictive value of characteristics on MR imaging of the brain].

Nederlands tijdschrift voor geneeskunde
The clinical application of neuroimaging for psychological complaints has so far been limited to the exclusion of somatic pathology. Radiological assessment of brain scans usually does not explain the psychological symptoms. However, that does not me...

Time-frequency time-space LSTM for robust classification of physiological signals.

Scientific reports
Automated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here ...

Applying a bagging ensemble machine learning approach to predict functional outcome of schizophrenia with clinical symptoms and cognitive functions.

Scientific reports
It has been suggested that the relationship between cognitive function and functional outcome in schizophrenia is mediated by clinical symptoms, while functional outcome is assessed by the Quality of Life Scale (QLS) and the Global Assessment of Func...

Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD.

Scientific reports
To identify newborns at risk of developing ASD and to detect ASD biomarkers early after birth, we compared retrospectively ultrasound and biological measurements of babies diagnosed later with ASD or neurotypical (NT) that are collected routinely dur...

Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks.

Scientific reports
With the rapid growth and increasing use of brain MRI, there is an interest in automated image classification to aid human interpretation and improve workflow. We aimed to train a deep convolutional neural network and assess its performance in identi...

AI-assisted tracking of worldwide non-pharmaceutical interventions for COVID-19.

Scientific data
The Coronavirus disease 2019 (COVID-19) global pandemic has transformed almost every facet of human society throughout the world. Against an emerging, highly transmissible disease, governments worldwide have implemented non-pharmaceutical interventio...

Creation and validation of a chest X-ray dataset with eye-tracking and report dictation for AI development.

Scientific data
We developed a rich dataset of Chest X-Ray (CXR) images to assist investigators in artificial intelligence. The data were collected using an eye-tracking system while a radiologist reviewed and reported on 1,083 CXR images. The dataset contains the f...

Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs.

Nature communications
Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which use...