AIMC Topic: Deep Learning

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Prediction of Parkinson's disease by transcranial sonography-based deep learning.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
OBJECTIVES: Transcranial sonography has been used as a valid neuroimaging tool to diagnose Parkinson's disease (PD). This study aimed to develop a modified transcranial sonography (TCS) technique based on a deep convolutional neural network (DCNN) mo...

From Proteins to Ligands: Decoding Deep Learning Methods for Binding Affinity Prediction.

Journal of chemical information and modeling
Accurate in silico prediction of protein-ligand binding affinity is important in the early stages of drug discovery. Deep learning-based methods exist but have yet to overtake more conventional methods such as giga-docking largely due to their lack o...

Deep learning-based NT-proBNP prediction from the ECG for risk assessment in the community.

Clinical chemistry and laboratory medicine
OBJECTIVES: The biomarker N-terminal pro B-type natriuretic peptide (NT-proBNP) has predictive value for identifying individuals at risk for cardiovascular disease (CVD). However, it is not widely used for screening in the general population, potenti...

Wavelet transform and deep learning-based obstructive sleep apnea detection from single-lead ECG signals.

Physical and engineering sciences in medicine
Sleep apnea is a common sleep disorder. Traditional testing and diagnosis heavily rely on the expertise of physicians, as well as analysis and statistical interpretation of extensive sleep testing data, resulting in time-consuming and labor-intensive...

AdaRes: A deep learning-based model for ultrasound image denoising: Results of image quality metrics, radiomics, artificial intelligence, and clinical studies.

Journal of clinical ultrasound : JCU
PURPOSE: The quality of ultrasound images is degraded by speckle and Gaussian noises. This study aims to develop a deep-learning (DL)-based filter for ultrasound image denoising.

A transformer-based deep learning approach for fairly predicting post-liver transplant risk factors.

Journal of biomedical informatics
Liver transplantation is a life-saving procedure for patients with end-stage liver disease. There are two main challenges in liver transplant: finding the best matching patient for a donor and ensuring transplant equity among different subpopulations...

Deep learning-based phenotype imputation on population-scale biobank data increases genetic discoveries.

Nature genetics
Biobanks that collect deep phenotypic and genomic data across many individuals have emerged as a key resource in human genetics. However, phenotypes in biobanks are often missing across many individuals, limiting their utility. We propose AutoComplet...

Large-scale pancreatic cancer detection via non-contrast CT and deep learning.

Nature medicine
Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC usi...

Self-supervised pre-training with contrastive and masked autoencoder methods for dealing with small datasets in deep learning for medical imaging.

Scientific reports
Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors, reduce radiologist workload, and accelerate diagnosis. Training such deep learning models requires large and accurate datasets, with annotations for all tra...

Optimization of the game improvement and data analysis model for the early childhood education major via deep learning.

Scientific reports
An ever-growing portion of the economy is dedicated to the field of education, intensifying the urgency of identifying strategies to secure the sector's enduring prosperity and elevate educational standards universally. This study introduces a model ...