A survey on deep learning in medical image analysis

ACCF
: American College of Cardiology Foundation
ACS
: acute coronary syndrome
AHA
: American Heart Association
CAD
: coronary artery disease
CABG
: coronary artery bypass grafting
CKMB
: creatine kinase MB isoform
cTn
: cardiac troponin
CT
: computed tomography
CV
: coefficient of variation
ECG
: electrocardiogram
ESC
: European Society of Cardiology
FDG
: fluorodeoxyglucose
h
: hour(s)
HF
: heart failure
LBBB
: left bundle branch block
LV
: left ventricle
LVH
: left ventricular hypertrophy
MI
: myocardial infarction
mIBG
: meta-iodo-benzylguanidine
min
: minute(s)
MONICA
: Multinational MONItoring of trends and determinants in CArdiovascular disease)
MPS
: myocardial perfusion scintigraphy
MRI
: magnetic resonance imaging
mV
: millivolt(s)
ng/L
: nanogram(s) per litre
Non-Q MI
: non-Q wave myocardial infarction
NSTEMI
: non-ST-elevation myocardial infarction
PCI
: percutaneous coronary intervention
PET
: positron emission tomography
pg/mL
: pictogram(s) per millilitre
Q wave MI
: Q wave myocardial infarction
RBBB
: right bundle branch block
sec
: second(s)
SPECT
: single photon emission computed tomography
STEMI
: ST elevation myocardial infarction
ST–T
: ST-segment –T wave
URL
: upper reference limit
WHF
: World Heart Federation
WHO
: World Health Organization

Myocardial infarction (MI) can be recognised by clinical features, including electrocardiographic (ECG) findings, elevated values of biochemical markers (biomarkers) of myocardial necrosis, and by imaging, or may be defined by pathology. It is a major cause of death and disability worldwide. MI may be the first manifestation of coronary artery disease (CAD) or it may occur, repeatedly, in patients with established disease. Information on MI rates can provide useful information regarding the burden of CAD within and across populations, especially if standardized data are collected in a manner that …

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Third universal definition of myocardial infarction

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Modern Applied Statistics With S

The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.

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The UK Biobank resource with deep phenotyping and genomic data

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

competitive athletes function: a prospective and longitudinal assessment of Training-specific changes in cardiac structure and

Myocardial perfusion in excessively trabeculated hearts: Insights from imaging and histological studies.

Microvascular Obstruction: How to Find the Traces of a Transient Pathology After Myocardial Infarction.

Authors’ Reply to Kindermann et al.’s Comment on: “Athlete’s Heart: Diagnostic Challenges and Future Perspectives”

COPYRIGHT © 2023 Leiner, Lekadir, Petersen and Young. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Steffen E. Petersen

Papers

competitive athletes function: a prospective and longitudinal assessment of Training-specific changes in cardiac structure and

Myocardial perfusion in excessively trabeculated hearts: Insights from imaging and histological studies.

Microvascular Obstruction: How to Find the Traces of a Transient Pathology After Myocardial Infarction.

Authors’ Reply to Kindermann et al.’s Comment on: “Athlete’s Heart: Diagnostic Challenges and Future Perspectives”

Editorial: Current and future role of artificial intelligence in cardiac imaging, volume II