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Predicting postmortem interval based on microbial community sequences and machine learning algorithms.

Environmental microbiology
Microbes play an essential role in the decomposition process but were poorly understood in their succession and behaviour. Previous researches have shown that microbes show predictable behaviour that starts at death and changes during the decompositi...

Video-based AI for beat-to-beat assessment of cardiac function.

Nature
Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease, screening for cardiotoxicity and decisions regarding the clinical management of patients with a critical illness. However, human assessment of cardiac fun...

Interactive Echocardiography Translation Using Few-Shot GAN Transfer Learning.

Computational and mathematical methods in medicine
BACKGROUND: Interactive echocardiography translation is an efficient educational function to master cardiac anatomy. It strengthens the student's understanding by pixel-level translation between echocardiography and theoretically sketch images. Previ...

Evaluating the Portability of an NLP System for Processing Echocardiograms: A Retrospective, Multi-site Observational Study.

AMIA ... Annual Symposium proceedings. AMIA Symposium
While natural language processing (NLP) of unstructured clinical narratives holds the potential for patient care and clinical research, portability of NLP approaches across multiple sites remains a major challenge. This study investigated the portabi...

Accelerated coronary MRI with sRAKI: A database-free self-consistent neural network k-space reconstruction for arbitrary undersampling.

PloS one
PURPOSE: To accelerate coronary MRI acquisitions with arbitrary undersampling patterns by using a novel reconstruction algorithm that applies coil self-consistency using subject-specific neural networks.

Deep Learning for Automatic Calcium Scoring in CT: Validation Using Multiple Cardiac CT and Chest CT Protocols.

Radiology
Background Although several deep learning (DL) calcium scoring methods have achieved excellent performance for specific CT protocols, their performance in a range of CT examination types is unknown. Purpose To evaluate the performance of a DL method ...

Development and application of artificial intelligence in cardiac imaging.

The British journal of radiology
In this review, we describe the technical aspects of artificial intelligence (AI) in cardiac imaging, starting with radiomics, basic algorithms of deep learning and application tasks of algorithms, until recently the availability of the public databa...

Reconstruction of undersampled 3D non-Cartesian image-based navigators for coronary MRA using an unrolled deep learning model.

Magnetic resonance in medicine
PURPOSE: To rapidly reconstruct undersampled 3D non-Cartesian image-based navigators (iNAVs) using an unrolled deep learning (DL) model, enabling nonrigid motion correction in coronary magnetic resonance angiography (CMRA).

Robotic fluidic coupling and interrogation of multiple vascularized organ chips.

Nature biomedical engineering
Organ chips can recapitulate organ-level (patho)physiology, yet pharmacokinetic and pharmacodynamic analyses require multi-organ systems linked by vascular perfusion. Here, we describe an 'interrogator' that employs liquid-handling robotics, custom s...

Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG.

Scientific reports
Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patien...