Arrhythmic sudden death survival prediction using deep learning analysis of scarring in the heart.

Sudden cardiac death from arrhythmia is a major cause of mortality worldwide. Here, we develop a novel deep learning (DL) approach that blends neural networks and survival analysis to predict patient-specific survival curves from contrast-enhanced cardiac magnetic resonance images and clinical covariates for patients with ischemic heart disease. The DL-predicted survival curves offer accurate predictions at times up to 10 years and allow for estimation of uncertainty in predictions.

Anatomically informed deep learning on contrast-enhanced cardiac magnetic resonance imaging for scar segmentation and clinical feature extraction.

Background: Visualizing fibrosis on cardiac magnetic resonance (CMR) imaging with contrast enhancement (late gadolinium enhancement; LGE) is paramount in characterizing disease progression and identifying arrhythmia substrates. Segmentation and fibrosis quantification from LGE-CMR is intensive, manual, and prone to interobserver variability. There is an unmet need for automated LGE-CMR image segmentation that ensures anatomical accuracy and seamless extraction of clinical features.

Optimal ECG-lead selection increases generalizability of deep learning on ECG abnormality classification.

Deep learning (DL) has achieved promising performance in detecting common abnormalities from the 12-lead electrocardiogram (ECG). However, diagnostic redundancy exists in the 12-lead ECG, which could impose a systematic overfitting on DL, causing poor generalization. We, therefore, hypothesized that finding an optimal lead subset of the 12-lead ECG to eliminate the redundancy would help improve the generalizability of DL-based models.

A machine learning approach for magnetic resonance image-based mouse brain modeling and fast computation in controlled cortical impact.

Computational modeling of the brain is crucial for the study of traumatic brain injury. An anatomically accurate model with refined details could provide the most accurate computational results. However, computational models with fine mesh details could take prolonged computation time that impedes the clinical translation of the models.

Viscoelastic characterization of injured brain tissue after controlled cortical impact (CCI) using a mouse model.

Background: Mechanical properties of the brain tissue are crucial to understand the mechanisms of traumatic brain injury (TBI). Injured brain tissue could induce changes of mechanical properties and anatomical structures. However, limited data is available for the injured tissue.

Measurement of Intra-Orbital Structures in Normal Chinese Adults Based on a Three-Dimensional Coordinate System.

Purpose Of The Study: This study was to establish a three-dimensional (3D) coordinate system and to study the normal dimensions of intra-orbital structures in Chinese adults.

Materials And Methods: One hundred and forty-five adult Chinese were selected from patients who had undergone cranio-facial computed tomography scans with diagnosis other than orbital or ocular abnormality. An orbital 3D coordinate system was built on the basis of the scans.