Cardiomegaly: Navigating the uncharted territories of heart failure - A multimodal radiological journey through advanced imaging, pathophysiological landscapes, and innovative therapeutic frontiers.

Cardiomegaly is among the disorders categorized by a structural enlargement of the heart by any of the situations including pregnancy, resulting in damage to heart muscles and causing trouble in normal heart functioning. Cardiomegaly can be defined in terms of dilatation with an enlarged heart and decreased left or biventricular contraction. The genetic origin of cardiomegaly is becoming more evident due to extensive genomic research opening up new avenues to ensure the use of precision medicine. Cardiomegaly is usually assessed by using an array of radiological modalities, including computed tomography (CT) scans, chest X-rays, and MRIs. These imaging techniques have provided an important opportunity for the physiology and anatomy of the heart. This review aims to highlight the complexity of cardiomegaly, highlighting the contribution of both ecological and genetic variables to its progression. Moreover, we further highlight the worth of precise clinical diagnosis, which comprises blood biomarkers and electrocardiograms (EKG ECG), demonstrating the significance of distinguishing between numerous basic causes. Finally, the analysis highlights the extensive variation of treatment lines, such as lifestyle modifications, prescription drugs, surgery, and implantable devices, although highlighting the critical need for individualized and personalized care.

Segmentation-based cardiomegaly detection based on semi-supervised estimation of cardiothoracic ratio.

The successful integration of neural networks in a clinical setting is still uncommon despite major successes achieved by artificial intelligence in other domains. This is mainly due to the black box characteristic of most optimized models and the undetermined generalization ability of the trained architectures. The current work tackles both issues in the radiology domain by focusing on developing an effective and interpretable cardiomegaly detection architecture based on segmentation models. The architecture consists of two distinct neural networks performing the segmentation of both cardiac and thoracic areas of a radiograph. The respective segmentation outputs are subsequently used to estimate the cardiothoracic ratio, and the corresponding radiograph is classified as a case of cardiomegaly based on a given threshold. Due to the scarcity of pixel-level labeled chest radiographs, both segmentation models are optimized in a semi-supervised manner. This results in a significant reduction in the costs of manual annotation. The resulting segmentation outputs significantly improve the interpretability of the architecture's final classification results. The generalization ability of the architecture is assessed in a cross-domain setting. The assessment shows the effectiveness of the semi-supervised optimization of the segmentation models and the robustness of the ensuing classification architecture.

Stretch-induced compliance mechanism in pregnancy-induced cardiac hypertrophy and the impact of cardiovascular risk factors.

Pressure overload-induced hypertrophy compromises cardiac stretch-induced compliance (SIC) after acute volume overload (AVO). We hypothesized that SIC could be enhanced by physiological hypertrophy induced by pregnancy's chronic volume overload. This study evaluated SIC-cardiac adaptation in pregnant women with or without cardiovascular risk (CVR) factors. Thirty-seven women (1st trimester, 1stT) and a separate group of 31 (3rd trimester, 3rdT) women [healthy or with CVR factors (obesity and/or hypertension and/or with gestational diabetes)] underwent echocardiography determination of left ventricular end-diastolic volume (LVEDV) and E/e' before (T0), immediately after (T1), and 15 min after (T2; SIC) AVO induced by passive leg elevation. Blood samples for NT-proBNP quantification were collected before and after the AVO. Acute leg elevation significantly increased inferior vena cava diameter and stroke volume from T0 to T1 in both 1stT and 3rdT, confirming AVO. LVEDV and E/e' also increased immediately after AVO (T1) in both 1stT and 3rdT. SIC adaptation (T2, 15 min after AVO) significantly decreased E/e' in both trimesters, with additional expansion of LVEDV only in the 1stT. NT-pro-BNP increased slightly after AVO but only in the 1stT. CVR factors, but not parity or age, significantly impacted SIC cardiac adaptation. A distinct functional response to SIC was observed between 1stT and 3rdT, which was influenced by CVR factors. The LV of 3rdT pregnant women was hypertrophied, showing a structural limitation to dilate with AVO, whereas the lower LV filling pressure values suggest increased diastolic compliance.NEW & NOTEWORTHY The sudden increase of volume overload triggers an acute myocardial stretch characterized by an immediate rise in contractility by the Frank-Starling mechanism, followed by a progressive increase known as the slow force response. The present study is the first to characterize echocardiographically the stretch-induced compliance (SIC) mechanism in the context of physiological hypertrophy induced by pregnancy. A distinct functional adaptation to SIC was observed between first and third trimesters, which was influenced by cardiovascular risk factors.

A deep-learning-based framework for identifying and localizing multiple abnormalities and assessing cardiomegaly in chest X-ray.

Accurate identification and localization of multiple abnormalities are crucial steps in the interpretation of chest X-rays (CXRs); however, the lack of a large CXR dataset with bounding boxes severely constrains accurate localization research based on deep learning. We created a large CXR dataset named CXR-AL14, containing 165,988 CXRs and 253,844 bounding boxes. On the basis of this dataset, a deep-learning-based framework was developed to identify and localize 14 common abnormalities and calculate the cardiothoracic ratio (CTR) simultaneously. The mean average precision values obtained by the model for 14 abnormalities reached 0.572-0.631 with an intersection-over-union threshold of 0.5, and the intraclass correlation coefficient of the CTR algorithm exceeded 0.95 on the held-out, multicentre and prospective test datasets. This framework shows an excellent performance, good generalization ability and strong clinical applicability, which is superior to senior radiologists and suitable for routine clinical settings.

Intra-amniotic infection and/or inflammation is associated with fetal cardiac concentric hypertrophy and diastolic dysfunction in preterm labor and preterm prelabor rupture of membranes.

BACKGROUND: Preterm delivery is associated with cardiovascular remodeling and dysfunction in children and adults. However, it is unknown whether these effects are caused by the neonatal consequences of preterm birth or if these are already present in utero. OBJECTIVE: We evaluated fetal cardiac morphology and function in fetuses of mothers admitted for preterm labor or preterm prelabor rupture of membranes and the association of these changes with the presence of intra-amniotic infection and/or inflammation. STUDY DESIGN: In this prospective cohort study, fetal echocardiography and amniocentesis were performed at admission in singleton pregnant women with preterm labor and/or preterm prelabor rupture of membranes between 24.0 and 34.0 weeks' gestation with (intra-amniotic infection and/or inflammation group, n=41) and without intra-amniotic infection and/or inflammation (non-intra-amniotic infection and/or inflammation, n=54). Controls (n=48) were outpatient pregnant women without preterm labor or preterm prelabor rupture of membranes. Intra-amniotic infection was defined by a positive amniotic fluid culture or positive 16S ribosomal RNA gene. Intra-amniotic inflammation was defined by using the amniotic fluid interleukin-6 cutoff levels previously reported by our group being >1.43 ng/mL in preterm prelabor rupture of membranes and >13.4 ng/mL in preterm labor. Fetal cardiac morphology and function was evaluated using echocardiography, and troponin-I and N-terminal pro-brain natriuretic peptide concentrations were measured in amniotic fluid from women with preterm labor or preterm prelabor rupture of membranes and compared with 20 amniotic fluid Biobank samples obtained for reasons other than preterm labor or preterm prelabor rupture of membranes or cardiac pathology. The data were adjusted for the estimated fetal weight below the 10th percentile and for preterm prelabor rupture of membranes at admission and also for gestational age at amniocentesis when amniotic fluid biomarkers were compared. RESULTS: From 2018 to 2021, 143 fetuses were included; 95 fetuses were from mothers admitted with a diagnosis of preterm labor or preterm prelabor rupture of membranes, and among those, 41 (28.7%) were in the intra-amniotic infection and/or inflammation group and 54 (37.8%) were in the non-intra-amniotic infection and/or inflammation group. A total of 48 (33.6%) fetuses were included in the control group. Fetuses with preterm labor and/or preterm prelabor rupture of membranes had signs of subclinical cardiac concentric hypertrophy (median left wall thickness of 0.93 [interquartile range, 0.72-1.16] in the intra-amniotic infection and/or inflammation group; 0.79 [0.66-0.92] in the non-intra-amniotic infection and/or inflammation group; and 0.69 [0.56-0.83] in controls; P<.001) and diastolic dysfunction (tricuspid A duration 0.23 seconds [0.21-0.25], 0.24 [0.22-0.25], and 0.21 [0.2-0.23]; P=.007). Systolic function was similar among groups. Higher values of amniotic fluid troponin I (1413 pg/mL [927-2334], 1190 [829-1636], and 841 [671-959]; P<.001) and N-terminal pro-brain natriuretic peptide were detected (35.0%, 17%, and 0%; P=.005) in fetuses with preterm labor or preterm prelabor rupture of membranes when compared with the control group. The highest N-terminal pro-brain natriuretic peptide concentrations were found in the intra-amniotic infection and/or inflammation group. CONCLUSION: Fetuses with preterm labor or preterm prelabor rupture of membranes showed signs of cardiac remodeling and subclinical dysfunction, which were more pronounced in those exposed to intra-amniotic infection and/or inflammation. These findings support that the cardiovascular effects observed in children and adults born preterm have, at least in part, a prenatal origin.

Regressive vision transformer for dog cardiomegaly assessment.

Cardiac disease is one of the leading causes of death in dogs. Automatic cardiomegaly detection has great significance in helping clinicians improve the accuracy of the diagnosis process. Deep learning methods show promising results in improving cardiomegaly classification accuracy, while they are still not widely applied in clinical trials due to the difficulty in mapping predicted results with input radiographs. To overcome these challenges, we first collect large-scale dog heart X-ray images. We then develop a dog heart labeling tool and apply a few-shot generalization strategy to accelerate the label speed. We also develop a regressive vision transformer model with an orthogonal layer to bridge traditional clinically used VHS metric with deep learning models. Extensive experimental results demonstrate that the proposed model achieves state-of-the-art performance.

Effect of Maternal Preeclampsia on Cardiac Structure and Function in Very Low Birth Weight Infants.

OBJECTIVE: We aimed to determine whether exposure to severe maternal preeclampsia (PE) in very low birth weight (VLBW) infants is associated with hypertrophic cardiac changes and altered hemodynamics. STUDY DESIGN: Case-control study of VLBW infants born at Los Angeles General Medical Center from May 2015 to August 2023, who had an echocardiogram within the first 7 days of life. Cases were infants exposed to maternal PE and controls were infants not exposed to maternal PE matched by birth weight (BW) 1:1. Laboratory, placental pathology results, hemodynamic data and clinical outcomes were collected and compared between cases and control infants. RESULTS: A total of 43 cases matched by BW with control infants were studied. There were no significant anatomical cardiac changes by echocardiography between cases and control infants. Cases had significantly higher blood pressure within the first 72 hours of life and lower ejection fraction (EF), fractional shortening, and peak systolic flow velocity through their patent ductus arteriosus (PDA) within the first week of life. Cases were more likely to be smaller despite being born at a later gestational age (GA), as well as small for GA with placental weight less than 10th percentile compared to control infants. CONCLUSION: Our findings indicate that infants born to mothers with PE have higher systemic vascular resistance as evidenced by elevated blood pressure, and lower EF and shortening fraction and higher pulmonary vascular resistance as evidenced by lower peak flow velocity through the PDA. We did not observe hypertrophic cardiac changes in exposed infants. These findings should be considered in clinical decision-making during management of these infants. KEY POINTS: · VLBW infants exposed to severe PE have higher rate of Small for gestational age and smaller placentas.. · VLBW infants exposed to severe PE have higher systemic vascular resistance during transitional period and lower EF and fractional shortening.. · VLBW infants exposed to severe PE have higher pulmonary vascular resistance..

Right Atrial Enlargement on Electrocardiogram in Previously Healthy Young Patients.

OBJECTIVES: To assess whether right atrial enlargement (RAE) on electrocardiogram (ECG) correlates with true RAE on echocardiogram in previously healthy young patients and to understand which patients with RAE on ECG may warrant additional testing. STUDY DESIGN: A single-center, retrospective review of previously healthy young patients with (1) ECGs that were read as RAE by a pediatric cardiologist and (2) echocardiograms obtained within 90 days of the ECG. ECGs were reviewed to confirm RAE and determine which leads met criteria. The echocardiograms were then reviewed and RA measurements with z scores obtained. A z score >2 was considered positive for RAE on echocardiogram. RESULTS: In total, 162 patients with median age 10.8 years were included in the study. A total of 23 patients had true RAE on echocardiogram, giving a positive predictive value (PPV) of 14%. In patients <1 year of age, the PPV increased to 35%. In patients older than 1 year, the PPV was low at 7%. Patients with true RAE were more likely to meet criteria for RAE in the anterior precordial leads (V1-V3) (48% vs 5%, P < .001) and meet criteria for right ventricular hypertrophy (22% vs 6%, P = .023). CONCLUSION: Our findings show that RAE on ECG has a low PPV for RAE on echocardiogram in previously healthy young patients. The highest yield for RAE on echocardiogram was observed in patients who were <1 year of age, had RAE in the anterior precordial leads, or displayed right ventricular hypertrophy on ECG.

Prognostic dilemmas for SIDS in idiopathic fetal right atrium dilatation: Case report and review literature.

The authors describe a case of fetal isolated right atrial enlargement or IDRA (idiopathic dilatations of the right atrium) evident in third trimester, complicated by arrhythmia in the female infant during the 1° month of life with ECG diagnosis of Wolf-Parkinson-White syndrome (WPW). The eldest sister died at 6 years because of an arrhythmia with the same diagnosis of WPW. The review of the literature on IDRA frequently shows a familial genetic aggregation. The pathogenetic mechanism underlying the dilation of the right atrium could consist of a myopathy or electrical conduction disorder. The exclusive involvement of the right atrium may be due to the increased pressure in the fetal right atrium. On the basis of our case and after review of the literature, we must be careful in defining as physiological the enlargement of the right fetal atrium in the third trimester of pregnancy. The ultrasound sign of IDRA may be a fetal prodrome of SIDS (sudden infant death syndrome).

Utility of vertebral left atrial size and vertebral heart size to aid detection of congestive heart failure in dogs with respiratory signs.

BACKGROUND: Differentiating cardiogenic vs noncardiogenic causes of respiratory signs can be challenging when echocardiography is unavailable. Radiographic vertebral left atrial size (VLAS) and vertebral heart size (VHS) have been shown to predict echocardiographic left heart size, with VLAS specifically estimating left atrial size. HYPOTHESIS/OBJECTIVES: Compare the diagnostic accuracy of VLAS and VHS to predict left-sided congestive heart failure (CHF) in dogs presenting with respiratory signs. ANIMALS: One-hundred fourteen dogs with respiratory signs and radiographic pulmonary abnormalities. METHODS: Retrospective cross-sectional study. Dogs had to have an echocardiogram and thoracic radiographs obtained within 24 hours. Diagnosis of CHF was confirmed based on the presence of respiratory signs, cardiac disease, LA enlargement, and cardiogenic pulmonary edema. RESULTS: Fifty-seven dogs had CHF and 57 did not have CHF. Compared to VHS (area under the curve [AUC] 0.85; 95% confidence interval [CI], 0.77-0.91), VLAS was a significantly (P = .03) more accurate predictor of CHF (AUC, 0.92; 95% CI, 0.85-0.96). Optimal cutoff for VLAS was >2.3 vertebrae (sensitivity, 93.0%; specificity, 82.5%). Murmur grade (P = .02) and VLAS (P < .0001) were independently associated with CHF and VHS was not. Increased VHS (54%) was significantly (P = .01) more common than increased VLAS (24%) in dogs without CHF. Results were similar in a subsample of older and smaller dogs. CONCLUSIONS AND CLINICAL IMPORTANCE: When echocardiography is unavailable, VLAS and murmur grade have clinically utility to aid in differentiating cardiogenic from noncardiogenic respiratory signs. These findings might be especially useful to help rule out CHF in dogs with increased VHS that present with respiratory signs.

Measuring cardiomyocyte cellular characteristics in cardiac hypertrophy using diffusion-weighted MRI.

PURPOSE: This paper presents a hierarchical modeling approach for estimating cardiomyocyte major and minor diameters and intracellular volume fraction (ICV) using diffusion-weighted MRI (DWI) data in ex vivo mouse hearts. METHODS: DWI data were acquired on two healthy controls and two hearts 3 weeks post transverse aortic constriction (TAC) using a bespoke diffusion scheme with multiple diffusion times ( Δ $$ \Delta $$ ), q-shells and diffusion encoding directions. Firstly, a bi-exponential tensor model was fitted separately at each diffusion time to disentangle the dependence on diffusion times from diffusion weightings, that is, b-values. The slow-diffusing component was attributed to the restricted diffusion inside cardiomyocytes. ICV was then extrapolated at Δ = 0 $$ \Delta =0 $$ using linear regression. Secondly, given the secondary and the tertiary diffusion eigenvalue measurements for the slow-diffusing component obtained at different diffusion times, major and minor diameters were estimated assuming a cylinder model with an elliptical cross-section (ECS). High-resolution three-dimensional synchrotron X-ray imaging (SRI) data from the same specimen was utilized to evaluate the biophysical parameters. RESULTS: Estimated parameters using DWI data were (control 1/control 2 vs. TAC 1/TAC 2): major diameter-17.4 µ $$ \mu $$ m/18.0 µ $$ \mu $$ m versus 19.2 µ $$ \mu $$ m/19.0 µ $$ \mu $$ m; minor diameter-10.2 µ $$ \mu $$ m/9.4 µ $$ \mu $$ m versus 12.8 µ $$ \mu $$ m/13.4 µ $$ \mu $$ m; and ICV-62%/62% versus 68%/47%. These findings were consistent with SRI measurements. CONCLUSION: The proposed method allowed for accurate estimation of biophysical parameters suggesting cardiomyocyte diameters as sensitive biomarkers of hypertrophy in the heart.

A convolutional attention mapping deep neural network for classification and localization of cardiomegaly on chest X-rays.

Building a reliable and precise model for disease classification and identifying abnormal sites can provide physicians assistance in their decision-making process. Deep learning based image analysis is a promising technique for enriching the decision making process, and accordingly strengthening patient care. This work presents a convolutional attention mapping deep learning model, Cardio-XAttentionNet, to classify and localize cardiomegaly effectively. We revisit the global average pooling (GAP) system and add a weighting term to develop a light and effective Attention Mapping Mechanism (AMM). The model enables the classification of cardiomegaly from chest X-rays through image-level classification and pixel-level localization only from image-level labels. We leverage some of the advanced ConvNet architectures as a backbone-model of the proposed attention mapping network to build Cardio-XAttentionNet. The proposed model is trained on ChestX-Ray14, which is a publicly accessible chest X-ray dataset. The best single model achieves an overall precision, recall, F-1 measure and area under curve (AUC) scores of 0.87, 0.85, 0.86 and 0.89, respectively, for the classification of the cardiomegaly. The results also demonstrate that the Cardio-XAttentionNet model well captures the cardiomegaly class information at image-level as well as localization at pixel-level on chest x-rays. A comparative analysis between the proposed AMM and existing GAP based models shows that the proposed model achieves a state-of-the-art performance on this dataset for cardiomegaly detection using a single model.