AIMC Topic: Deep Learning

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NPSV-deep: a deep learning method for genotyping structural variants in short read genome sequencing data.

Bioinformatics (Oxford, England)
MOTIVATION: Structural variants (SVs) play a causal role in numerous diseases but can be difficult to detect and accurately genotype (determine zygosity) with short-read genome sequencing data (SRS). Improving SV genotyping accuracy in SRS data, part...

EvoAug-TF: extending evolution-inspired data augmentations for genomic deep learning to TensorFlow.

Bioinformatics (Oxford, England)
SUMMARY: Deep neural networks (DNNs) have been widely applied to predict the molecular functions of the non-coding genome. DNNs are data hungry and thus require many training examples to fit data well. However, functional genomics experiments typical...

Development of deep-learning models for real-time anaerobic threshold and peak VO2 prediction during cardiopulmonary exercise testing.

European journal of preventive cardiology
AIMS: Exercise intolerance is a clinical feature of patients with heart failure (HF). Cardiopulmonary exercise testing (CPET) is the first-line examination for assessing exercise capacity in patients with HF. However, the need for extensive experienc...

Added prognostic value of 3D deep learning-derived features from preoperative MRI for adult-type diffuse gliomas.

Neuro-oncology
BACKGROUND: To investigate the prognostic value of spatial features from whole-brain MRI using a three-dimensional (3D) convolutional neural network for adult-type diffuse gliomas.

Protocol: revolutionizing central nervous system tumour diagnosis in low- and middle-income countries: an innovative observational study on intraoperative smear and deep learning.

JPMA. The Journal of the Pakistan Medical Association
OBJECTIVE: The aim of this study is to assess the feasibility and implementation of a novel approach for intraoperative brain smears within the operating room, which is augmented with deep learning technology.

Deep Learning to Differentiate Benign and Malignant Vertebral Fractures at Multidetector CT.

Radiology
Background Differentiating between benign and malignant vertebral fractures poses diagnostic challenges. Purpose To investigate the reliability of CT-based deep learning models to differentiate between benign and malignant vertebral fractures. Materi...

Predicting human chronological age via AI analysis of dorsal hand versus facial images: A study in a cohort of Indian females.

Experimental dermatology
Predicting a person's chronological age (CA) from visible skin features using artificial intelligence (AI) is now commonplace. Often, convolutional neural network (CNN) models are built using images of the face as biometric data. However, hands hold ...

A deep-learning workflow to predict upper tract urothelial carcinoma protein-based subtypes from H&E slides supporting the prioritization of patients for molecular testing.

The journal of pathology. Clinical research
Upper tract urothelial carcinoma (UTUC) is a rare and aggressive, yet understudied, urothelial carcinoma (UC). The more frequent UC of the bladder comprises several molecular subtypes, associated with different targeted therapies and overlapping with...