AIMC Topic: Deep Learning

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Predicting Kováts Retention Indices Using Graph Neural Networks.

Journal of chromatography. A
The Kováts retention index is a dimensionless quantity that characterizes the rate at which a compound is processed through a gas chromatography column. This quantity is independent of many experimental variables and, as such, is considered a near-un...

Tissue at Risk and Ischemic Core Estimation Using Deep Learning in Acute Stroke.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: In acute stroke patients with large vessel occlusions, it would be helpful to be able to predict the difference in the size and location of the final infarct based on the outcome of reperfusion therapy. Our aim was to demonstr...

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...

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...

Predicting treatment response from longitudinal images using multi-task deep learning.

Nature communications
Radiographic imaging is routinely used to evaluate treatment response in solid tumors. Current imaging response metrics do not reliably predict the underlying biological response. Here, we present a multi-task deep learning approach that allows simul...

Imaging sebaceous gland using optical coherence tomography with deep learning assisted automatic identification.

Journal of biophotonics
Imaging sebaceous glands and evaluating morphometric parameters are important for diagnosis and treatment of serum problems. In this article, we investigate the feasibility of high-resolution optical coherence tomography (OCT) in combination with dee...

Deep learning diagnostic and risk-stratification pattern detection for COVID-19 in digital lung auscultations: clinical protocol for a case-control and prospective cohort study.

BMC pulmonary medicine
BACKGROUND: Lung auscultation is fundamental to the clinical diagnosis of respiratory disease. However, auscultation is a subjective practice and interpretations vary widely between users. The digitization of auscultation acquisition and interpretati...

Deep learning assisted multi-omics integration for survival and drug-response prediction in breast cancer.

BMC genomics
BACKGROUND: Survival and drug response are two highly emphasized clinical outcomes in cancer research that directs the prognosis of a cancer patient. Here, we have proposed a late multi omics integrative framework that robustly quantifies survival an...

Proteome analysis using machine learning approaches and its applications to diseases.

Advances in protein chemistry and structural biology
With the tremendous developments in the fields of biological and medical technologies, huge amounts of data are generated in the form of genomic data, images in medical databases or as data on protein sequences, and so on. Analyzing this data through...

Small Bowel Capsule Endoscopy and artificial intelligence: First or second reader?

Best practice & research. Clinical gastroenterology
Several machine learning algorithms have been developed in the past years with the aim to improve SBCE (Small Bowel Capsule Endoscopy) feasibility ensuring at the same time a high diagnostic accuracy. If past algorithms were affected by low performan...