AIMC Topic: Deep Learning

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Accelerated CEST imaging through deep learning quantification from reduced frequency offsets.

Magnetic resonance in medicine
PURPOSE: To shorten CEST acquisition time by leveraging Z-spectrum undersampling combined with deep learning for CEST map construction from undersampled Z-spectra.

Clinical impact of deep learning-derived intravascular ultrasound characteristics in patients with deferred coronary artery.

International journal of cardiology
Prognostic markers for long-term outcomes are lacking in patients with deferred (nonculprit) coronary artery lesions. This study aimed to identify the morphological criteria for predicting adverse outcomes and validate their clinical impact. Using de...

Immunohistochemistry annotations enhance AI identification of lymphocytes and neutrophils in digitized H&E slides from inflammatory bowel disease.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Histologic assessment of the immune infiltrate in H&E slides is vital in diagnosing and managing inflammatory bowel diseases, but these assessments are subjective and time-consuming even for those with expertise. The develop...

Automatic generation of diffusion tensor imaging for the lumbar nerve using convolutional neural networks.

Magnetic resonance imaging
【PURPOSE】: Diffusion Tensor Imaging (DTI) with tractography is useful for the functional diagnosis of degenerative lumbar disorders. However, it is not widely used in clinical settings due to time and health care provider costs, as it is performed ma...

An adaptive testing item selection strategy via a deep reinforcement learning approach.

Behavior research methods
Computerized adaptive testing (CAT) aims to present items that statistically optimize the assessment process by considering the examinee's responses and estimated trait levels. Recent developments in reinforcement learning and deep neural networks pr...

A deep learning approach to prediction of blood group antigens from genomic data.

Transfusion
BACKGROUND: Deep learning methods are revolutionizing natural science. In this study, we aim to apply such techniques to develop blood type prediction models based on cheap to analyze and easily scalable screening array genotyping platforms.

Main challenges on the curation of large scale datasets for pancreas segmentation using deep learning in multi-phase CT scans: Focus on cardinality, manual refinement, and annotation quality.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate segmentation of the pancreas in computed tomography (CT) holds paramount importance in diagnostics, surgical planning, and interventions. Recent studies have proposed supervised deep-learning models for segmentation, but their efficacy relie...

The Role of Deep Learning and Gait Analysis in Parkinson's Disease: A Systematic Review.

Sensors (Basel, Switzerland)
Parkinson's disease (PD) is the second most common movement disorder in the world. It is characterized by motor and non-motor symptoms that have a profound impact on the independence and quality of life of people affected by the disease, which increa...

Gemini-Assisted Deep Learning Classification Model for Automated Diagnosis of High-Resolution Esophageal Manometry Images.

Medicina (Kaunas, Lithuania)
To develop a deep learning model for esophageal motility disorder diagnosis using high-resolution manometry images with the aid of Gemini. Gemini assisted in developing this model by aiding in code writing, preprocessing, model optimization, and tr...

Detection of Alcoholic EEG signal using LASSO regression with metaheuristics algorithms based LSTM and enhanced artificial neural network classification algorithms.

Scientific reports
The world has a higher count of death rates as a result of Alcohol consumption. Identification is possible because Alcoholic EEG waves have a certain behavior that is totally different compared to the non-alcoholic individual. The available approache...