Image-Based Cardiac Diagnosis With Machine Learning: A Review

Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD). Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. However, with the advent of big data and machine learning, new opportunities are emerging to build artificial intelligence tools that will directly assist the clinician in the diagnosis of CVDs. This paper presents a thorough review of recent works in this field and provide the reader with a detailed presentation of the machine learning methods that can be further exploited to enable more automated, precise and early diagnosis of most CVDs

Prediction of incident cardiovascular events using machine learning and CMR radiomics.

OBJECTIVES: Evaluation of the feasibility of using cardiovascular magnetic resonance (CMR) radiomics in the prediction of incident atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), and stroke using machine learning techniques. METHODS: We identified participants from the UK Biobank who experienced incident AF, HF, MI, or stroke during the continuous longitudinal follow-up. The CMR indices and the vascular risk factors (VRFs) as well as the CMR images were obtained for each participant. Three-segmented regions of interest (ROIs) were computed: right ventricle cavity, left ventricle (LV) cavity, and LV myocardium in end-systole and end-diastole phases. Radiomics features were extracted from the 3D volumes of the ROIs. Seven integrative models were built for each incident cardiovascular disease (CVD) as an outcome. Each model was built with VRF, CMR indices, and radiomics features and a combination of them. Support vector machine was used for classification. To assess the model performance, the accuracy, sensitivity, specificity, and AUC were reported. RESULTS: AF prediction model using the VRF+CMR+Rad model (accuracy: 0.71, AUC 0.76) obtained the best result. However, the AUC was similar to the VRF+Rad model. HF showed the most significant improvement with the inclusion of CMR metrics (VRF+CMR+Rad: 0.79, AUC 0.84). Moreover, adding only the radiomics features to the VRF reached an almost similarly good performance (VRF+Rad: accuracy 0.77, AUC 0.83). Prediction models looking into incident MI and stroke reached slightly smaller improvement. CONCLUSIONS: Radiomics features may provide incremental predictive value over VRF and CMR indices in the prediction of incident CVDs. KEY POINTS: • Prediction of incident atrial fibrillation, heart failure, stroke, and myocardial infarction using machine learning techniques. • CMR radiomics, vascular risk factors, and standard CMR indices will be considered in the machine learning models. • The experiments show that radiomics features can provide incremental predictive value over VRF and CMR indices in the prediction of incident cardiovascular diseases

Incidence and trends of low back pain hospitalisation during military service – An analysis of 387,070 Finnish young males

<p>Abstract</p> <p>Background</p> <p>There is evidence that low back pain (LBP) during young adulthood and military service predicts LBP later in life. The purpose of this study was to investigate the incidence and trends of LBP hospitalisation among Finnish military conscripts.</p> <p>Methods</p> <p>All male conscripts performing their compulsory military service during 1990–2002 were included in the study population. Altogether 387,070 military conscripts were followed throughout their six-to-twelve-month service period. Data on LBP hospitalisations were obtained from the National Hospital Discharge Register.</p> <p>Results</p> <p>Altogether 7,240 LBP hospitalisations were identified among 5,061 (1.3%) male conscripts during the study period. The event-based incidence of LBP hospitalisation was 27.0 (95% confidence interval (CI): 25.7–28.2). In most cases, the diagnosis was unspecified LBP (<it>n </it>= 5,141, 71%) followed by lumbar disc disorders (<it>n </it>= 2,069, 29%). Hospitalisation incidence due to unspecified LBP was 19.1 per 1,000 person-years (95% CI: 18.3 to 20.4), and 7.8 per 1,000 person-years (95% CI: 6.7 to 8.3) due to lumbar disc disorders. The incidence of unspecified LBP remained unaltered, while hospitalisation due to lumbar disc disorders declined from 1993 onwards.</p> <p>Conclusion</p> <p>Although conscripts accepted into military training pass physician-performed examinations as healthy, young adults, LBP hospitalisation causes significant morbidity during military service.</p

Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging

“The final authenticated version is available online at https://doi.org/10.1007/978-3-030-32245-8_83.”© 2019, Springer Nature Switzerland AG. Recent progress in fully-automated image segmentation has enabled efficient extraction of clinical parameters in large-scale clinical imaging studies, reducing laborious manual processing. However, the current state-of-the-art automatic image segmentation may still fail, especially when it comes to atypical cases. Visual inspection of segmentation quality is often required, thus diminishing the improvements in efficiency. This drives an increasing need to enhance the overall data processing pipeline with robust automatic quality scoring, especially for clinical applications. We present a novel quality control-driven (QCD) framework to provide reliable segmentation using a set of different neural networks. In contrast to the prior segmentation and quality scoring methods, the proposed framework automatically selects the optimal segmentation on-the-fly from the multiple candidate segmentations available, directly utilizing the inherent Dice similarity coefficient (DSC) predictions. We trained and evaluated the framework on a large-scale cardiovascular magnetic resonance aortic cine image sequences from the UK Biobank Study. The framework achieved segmentation accuracy of mean DSC at 0.966, mean prediction error of DSC within 0.015, and mean error in estimating lumen area ≤17.6 mm2 for both ascending aorta and proximal descending aorta. This novel QCD framework successfully integrates the automatic image segmentation along with detection of critical errors on a per-case basis, paving the way towards reliable fully-automatic extraction of clinical parameters for large-scale imaging studies

Quantitative effects of tobacco smoking exposure on the maternal-fetal circulation

<p>Abstract</p> <p>Background</p> <p>Despite the existence of various published studies regarding the effects of tobacco smoking on pregnancy, and especially in regards to placental blood flow and vascular resistance, some points still require clarification. In addition, the amount of damage due to tobacco smoking exposure that occurs has not been quantified by objective means. In this study, we looked for a possible association between flow resistance indices of several arteries and the levels of urinary cotinine and the concentration of carbon monoxide in the exhaled air (COex) of both smoking and non-smoking pregnant women. We also looked for a relationship between those findings and fetal growth and birth weight.</p> <p>Methods</p> <p>In a prospective design, thirty pregnant smokers and thirty-four pregnant non-smokers were studied. The volunteers signed consent forms, completed a self-applied questionnaire and were subjected to Doppler velocimetry. Tobacco smoking exposure was quantified by subject provided information and confirmed by the measurement of urinary cotinine levels and by the concentration of carbon monoxide in the exhaled air (COex). The weight of newborns was evaluated immediately after birth.</p> <p>Results</p> <p>Comparing smoking to non-smoking pregnant women, a significant increase in the resistance index was observed in the uterine arteries (P = 0.001) and umbilical artery (P = 0.001), and a decrease in the middle cerebral artery (P = 0.450). These findings were associated with progressively higher concentrations of COex and urinary cotinine. A decrease in the birth weight was also detected (P < 0.001) in association with a progressive increase in the tobacco exposure of the pregnant woman.</p> <p>Conclusions</p> <p>In pregnant women who smoke, higher arterial resistance indices and lower birth weights were observed, and these findings were associated with increasing levels of tobacco smoking exposure. The values were significantly different when compared to those found in non-smoking pregnant women. This study contributes to the findings that smoking damage during pregnancy is dose-dependent, as demonstrated by the objective methods for measuring tobacco smoking exposure.</p

Image-Based Biological Heart Age Estimation Reveals Differential Aging Patterns Across Cardiac Chambers.

BACKGROUND: Biological heart age estimation can provide insights into cardiac aging. However, existing studies do not consider differential aging across cardiac regions. PURPOSE: To estimate biological age of the left ventricle (LV), right ventricle (RV), myocardium, left atrium, and right atrium using magnetic resonance imaging radiomics phenotypes and to investigate determinants of aging by cardiac region. STUDY TYPE: Cross-sectional. POPULATION: A total of 18,117 healthy UK Biobank participants including 8338 men (mean age = 64.2 ± 7.5) and 9779 women (mean age = 63.0 ± 7.4). FIELD STRENGTH/SEQUENCE: A 1.5 T/balanced steady-state free precession. ASSESSMENT: An automated algorithm was used to segment the five cardiac regions, from which radiomic features were extracted. Bayesian ridge regression was used to estimate biological age of each cardiac region with radiomics features as predictors and chronological age as the output. The "age gap" was the difference between biological and chronological age. Linear regression was used to calculate associations of age gap from each cardiac region with socioeconomic, lifestyle, body composition, blood pressure and arterial stiffness, blood biomarkers, mental well-being, multiorgan health, and sex hormone exposures (n = 49). STATISTICAL TEST: Multiple testing correction with false discovery method (threshold = 5%). RESULTS: The largest model error was with RV and the smallest with LV age (mean absolute error in men: 5.26 vs. 4.96 years). There were 172 statistically significant age gap associations. Greater visceral adiposity was the strongest correlate of larger age gaps, for example, myocardial age gap in women (Beta = 0.85, P = 1.69 × 10-26 ). Poor mental health associated with large age gaps, for example, "disinterested" episodes and myocardial age gap in men (Beta = 0.25, P = 0.001), as did a history of dental problems (eg LV in men Beta = 0.19, P = 0.02). Higher bone mineral density was the strongest associate of smaller age gaps, for example, myocardial age gap in men (Beta = -1.52, P = 7.44 × 10-6 ). DATA CONCLUSION: This work demonstrates image-based heart age estimation as a novel method for understanding cardiac aging. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 1

Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of UK Biobank - rationale, challenges and approaches

PMCID: PMC3668194SEP was directly funded by the National Institute for Health Research Cardiovascular Biomedical Research Unit at Barts. SN acknowledges support from the Oxford NIHR Biomedical Research Centre and from the Oxford British Heart Foundation Centre of Research Excellence. SP and PL are funded by a BHF Senior Clinical Research fellowship. RC is supported by a BHF Research Chair and acknowledges the support of the Oxford BHF Centre for Research Excellence and the MRC and Wellcome Trust. PMM gratefully acknowledges training fellowships supporting his laboratory from the Wellcome Trust, GlaxoSmithKline and the Medical Research Council

Optimizing Functional Network Representation of Multivariate Time Series

By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks

Higher spatial resolution improves the interpretation of the extent of ventricular trabeculation.

The ventricular walls of the human heart comprise an outer compact layer and an inner trabecular layer. In the context of an increased pre-test probability, diagnosis left ventricular noncompaction cardiomyopathy is given when the left ventricle is excessively trabeculated in volume (trabecular vol >25% of total LV wall volume) or thickness (trabecular/compact (T/C) >2.3). Here, we investigated whether higher spatial resolution affects the detection of trabeculation and thus the assessment of normal and excessively trabeculated wall morphology. First, we screened left ventricles in 1112 post-natal autopsy hearts. We identified five excessively trabeculated hearts and this low prevalence of excessive trabeculation is in agreement with pathology reports but contrasts the prevalence of approximately 10% of the population found by in vivo non-invasive imaging. Using macroscopy, histology and low- and high-resolution MRI, the five excessively trabeculated hearts were compared with six normal hearts and seven abnormally trabeculated and excessive trabeculation-negative hearts. Some abnormally trabeculated hearts could be considered excessively trabeculated macroscopically because of a trabecular outflow or an excessive number of trabeculations, but they were excessive trabeculation-negative when assessed with MRI-based measurements (T/C <2.3 and vol <25%). The number of detected trabeculations and T/C ratio were positively correlated with higher spatial resolution. Using measurements on high resolution MRI and with histological validation, we could not replicate the correlation between trabeculations of the left and right ventricle that has been previously reported. In conclusion, higher spatial resolution may affect the sensitivity of diagnostic measurements and in addition could allow for novel measurements such as counting of trabeculations

The global cardiovascular magnetic resonance registry (GCMR) of the society for cardiovascular magnetic resonance (SCMR): its goals, rationale, data infrastructure, and current developments

GCMR received seed funding from SCMR (SCMR_GRANT_001) for the development and maintenance of GCMR websites and database infrastructure