Incremental Value of Radiomics Features of Epicardial Adipose Tissue for Detecting the Severity of COVID-19 Infection.

Introduction: Epicardial adipose tissue (EAT) is known for its pro-inflammatory properties and association with Coronavirus Disease 2019 (COVID-19) severity. However, existing detection methods for COVID-19 severity assessment often lack consideration of organs and tissues other than the lungs, which limits the accuracy and reliability of these predictive models.

Material And Methods: The retrospective study included data from 515 COVID-19 patients (Cohort 1, n=415; Cohort 2, n=100) from two centers (Shanghai Public Health Center and Brazil Niteroi Hospital) between January 2020 and July 2020.

An automated two-stage approach to kidney and tumor segmentation in CT imaging.

Background: The incidence of kidney tumors is progressively increasing each year. The precision of segmentation for kidney tumors is crucial for diagnosis and treatment.

Objective: To enhance accuracy and reduce manual involvement, propose a deep learning-based method for the automatic segmentation of kidneys and kidney tumors in CT images.

Radiomics Nomogram Derived from Gated Myocardial Perfusion SPECT for Identifying Ischemic Cardiomyopathy.

Personalized management involving heart failure (HF) etiology is crucial for better prognoses. We aim to evaluate the utility of a radiomics nomogram based on gated myocardial perfusion imaging (GMPI) in distinguishing ischemic from non-ischemic origins of HF. A total of 172 heart failure patients with reduced left ventricular ejection fraction (HFrEF) who underwent GMPI scan were divided into training (n = 122) and validation sets (n = 50) based on chronological order of scans.

Classification guided thick fog removal network for drone imaging: ClassifyCycle.

The foggy images captured by drones are nonuniform due to inhomogeneous distribution of fog in higher altitude, leading to the obvious fog thickness differences in the images. This paper proposes a classification guided thick fog removal network for drone imaging, termed ClassifyCycle. The drone images are input into the proposed classification module (ICLFn) to enhance the reliability of follow-up learning network.

Deep learning-based diagnosis of disease activity in patients with Graves' orbitopathy using orbital SPECT/CT.

Purpose: Orbital [Tc]TcDTPA orbital single-photon emission computed tomography (SPECT)/CT is an important method for assessing inflammatory activity in patients with Graves' orbitopathy (GO). However, interpreting the results requires substantial physician workload. We aim to propose an automated method called GO-Net to detect inflammatory activity in patients with GO.

A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images.

Accurate segmentation of the left ventricle (LV) is crucial for evaluating myocardial perfusion SPECT (MPS) and assessing LV functions. In this study, a novel method combining deep learning with shape priors was developed and validated to extract the LV myocardium and automatically measure LV functional parameters. The method integrates a three-dimensional (3D) V-Net with a shape deformation module that incorporates shape priors generated by a dynamic programming (DP) algorithm to guide its output during training.

Automatic reorientation by deep learning to generate short-axis SPECT myocardial perfusion images.

Background: Single photon emission computed tomography (SPECT) myocardial perfusion images (MPI) can be displayed both in traditional short-axis (SA) cardiac planes and polar maps for interpretation and quantification. It is essential to reorient the reconstructed transaxial SPECT MPI into standard SA slices. This study is aimed to develop a deep-learning-based approach for automatic reorientation of MPI.

Severity detection of COVID-19 infection with machine learning of clinical records and CT images.

Background: Coronavirus disease 2019 (COVID-19) is a deadly viral infection spreading rapidly around the world since its outbreak in 2019. In the worst case a patient's organ may fail leading to death. Therefore, early diagnosis is crucial to provide patients with adequate and effective treatment.

A Deep Learning-based Method to Extract Lumen and Media-Adventitia in Intravascular Ultrasound Images.

Intravascular ultrasound (IVUS) imaging allows direct visualization of the coronary vessel wall and is suitable for assessing atherosclerosis and the degree of stenosis. Accurate segmentation and lumen and median-adventitia (MA) measurements from IVUS are essential for such a successful clinical evaluation. However, current automated segmentation by commercial software relies on manual corrections, which is time-consuming and user-dependent.

Automatic identification of end-diastolic and end-systolic cardiac frames from invasive coronary angiography videos.

Background: Automatic identification of proper image frames at the end-diastolic (ED) and end-systolic (ES) frames during the review of invasive coronary angiograms (ICA) is important to assess blood flow during a cardiac cycle, reconstruct the 3D arterial anatomy from bi-planar views, and generate the complementary fusion map with myocardial images. The current identification method primarily relies on visual interpretation, making it not only time-consuming but also less reproducible.

Objecitve: In this paper, we propose a new method to automatically identify angiographic image frames associated with the ED and ES cardiac phases.

Machine learning for the diagnosis of pulmonary hypertension.

Objective This paper aims to investigate whether machine learning (ML) can be used to predict the state of pulmonary hypertension (PH), including pre-capillary and post-capillary, from echocardiographic data.Methods Two hundred and seventy-five patients with PH who underwent both echocardiography and right heart catheterization were included in the study. Mean pulmonary artery pressure, pulmonary artery wedge pressure measured by right heart catheterization were used as criteria for judging pre-capillary PH and post-capillary PH.

Analyze Informant-Based Questionnaire for The Early Diagnosis of Senile Dementia Using Deep Learning.

Objective: This paper proposes a multiclass deep learning method for the classification of dementia using an informant-based questionnaire.

Methods: A deep neural network classification model based on Keras framework is proposed in this paper. To evaluate the advantages of our proposed method, we compared the performance of our model with industry-standard machine learning approaches.

Machine Learning for the Preliminary Diagnosis of Dementia.

Objective: The reliable diagnosis remains a challenging issue in the early stages of dementia. We aimed to develop and validate a new method based on machine learning to help the preliminary diagnosis of normal, mild cognitive impairment (MCI), very mild dementia (VMD), and dementia using an informant-based questionnaire.

Methods: We enrolled 5,272 individuals who filled out a 37-item questionnaire.

Prognostic value of left-ventricular systolic and diastolic dyssynchrony measured from gated SPECT MPI in patients with dilated cardiomyopathy.

Background: Left-ventricular systolic dyssynchrony (LVSD) has been an important prognostic factor in the patients with dilated cardiomyopathy (DCM). However, the association between the LV diastolic dyssynchrony (LVDD) and clinical outcome is not well established. This study aims to evaluate the prognostic values of both systolic and diastolic dyssynchrony in patients with DCM.

Hypomethylation of the Toll-like receptor-2 gene increases the risk of essential hypertension.

Studies on the etiology of essential hypertension (EH) have demonstrated that chronic inflammation contributes to the onset and development of elevated blood pressure. Toll‑like receptors (TLRs), important immune receptors, serve a role in chronic inflammation and are associated with EH. In the present study, 96 patients with EH, and 96 age‑ and sex‑matched healthy controls were recruited, and eight cytosine‑phosphate‑guanine (CpG) dinucleotides (CpG1‑8) were analyzed using bisulfite pyrosequencing technology.

Interactions between Promoter Methylation and Smoking Increase Risk of Essential Hypertension.

Aldosterone synthase (CYP11B2) is closely linked to essential hypertension (EH). However, it remains unclear whether the methylation of the promoter is involved in the development of EH in humans. Our study is aimed at evaluating the contribution of promoter methylation to the risk of EH.

Topological Alterations of the Intrinsic Brain Network in Patients with Functional Dyspepsia.

Background/aims: Previous studies reported that integrated information in the brain ultimately determines the subjective experience of patients with chronic pain, but how the information is integrated in the brain connectome of functional dyspepsia (FD) patients remains largely unclear. The study aimed to quantify the topological changes of the brain network in FD patients.

Methods: Small-world properties, network efficiency and nodal centrality were utilized to measure the changes in topological architecture in 25 FD patients and 25 healthy controls based on functional magnetic resonance imaging.