Cardiac Views Research Articles

Feasibility of fetal cardiac function and anatomy assessment by real-time spiral bSSFP MRI at 0.55T

BackgroundContemporary 0.55T MRI is promising for fetal MRI, due to the larger bore, reduced safety concerns, lower acoustic noise, and improved fast imaging capability. In this work, we explore improved fetal cardiac MRI (CMR) without relying on any synchronizing devices, prospective, or retrospective gating. PurposeTo determine the feasibility of real-time MRI evaluation of fetal cardiac function as well as cardiac and great vessel anatomies by using spiral balanced steady-state free precession (bSSFP) at 0.55T. MethodsA real-time spiral bSSFP pulse sequence for fetal CMR was implemented and optimized on a 0.55T whole-body MRI. Fetal CMR was prospectively performed between May 2022 and August 2023. The protocol included: 1) real-time images at standard cardiac views, for 10-20seconds/view and 40 to 43.6 ms/frame and 2) 4-9 stacks of slices at standard cardiac views that each cover the whole heart, with 15-30 slices/stack, and 2-5seconds/slice, at 320 to 349 ms/frame. Images were evaluated by a fetal cardiologist. Quantitative measurements of cardiothoracic area ratio and cardiac axis were compared with previous reports. Diagnostic accuracy was compared against postnatal echocardiographic findings. ResultsTwenty-nine participants were enrolled for 32 CMR exams, with mean maternal age 33.6±5.8 year (range 22-44 year) and mean gestational age 32.8±3.9 weeks (range 23-38 weeks). The proposed sequence enabled evaluation of the fetal heart in <30minutes in all cases (average 22minutes). Real-time MRI allowed easy adjustment of scan plan, automatic whole-heart volumetric sweeping, and flexible choice of reconstruction temporal resolution. For key cardiac anatomic features 60% were delineated well. Mean cardiothoracic area ratio and cardiac axis were 0.27±0.04 and 45.8±7.8. Diagnostic agreement with postnatal echocardiographic findings was 84%. ConclusionA spiral real-time bSSFP pulse sequence at 0.55T can provide both low framerate and high framerate fetal heart images without relying on maternal breath-hold, specialized gating devices, or cardiac gating. The low framerate images offer high diagnostic quality structural evaluations of the fetal heart, while the high framerate images capture fetal heart motion and may enable functional assessments.

Self-Directed Virtual Reality-Based Training versus Traditional Physician-Led Teaching for Point-of-Care Cardiac Ultrasound: A Randomized Controlled Study

To assess the learning efficacy of self-directed virtual reality ultrasound simulators as an alternative to traditional physician-led teaching for cardiac point-of-care ultrasound (POCUS) training. Single blinded (observer), noninferiority, parallel group, randomized controlled study. Tertiary university hospital in Singapore. Forty-three medical students with no prior formal ultrasound training. Participants first completed an e-learning module on basic ultrasonography. Participants' baseline knowledge was subsequently assessed using a multiple-choice question (MCQ) test. Participants were then randomized to either physician-led (PL) teaching or independent learning with a virtual-reality (VR) simulator to learn the 4 cardiac POCUS views. A post-training MCQ test and a practical skills test scored by 2 blinded assessors were conducted. Students repeated the MCQ test and skills test 1 month later. The VR group had higher baseline MCQ scores compared to the PL group (mean, 13.4 v 10.7). Immediately post-training, the PL group had a greater improvement in mean MCQ scores (from baseline) and higher total practical scores compared to the VR group (p = 0.03 and p < 0.01, respectively). At 1 month post-training, the PL group similarly had a greater mean change from baseline MCQ scores, but this difference was not statistically significant (p = 0.12). For practical scores, the VR group scored higher than the PL group, although this difference was not statistically significant (p = 0.06). Our study demonstrates that at 1 month post-training, self-directed VR training was noninferior to PL training. Although differences observed were not significant, there were trends to suggest enhanced retention of knowledge and skills with VR learning.

Automated selection of echocardiographic views using a novel artificial intelligence software

Abstract Background/Introduction Evaluation of a single echocardiographic study involves sorting, selecting, measuring and interpreting a large number of images. As these steps are currently performed manually, this can turn into a repetitive, time-consuming and tedious task. Although the use of artificial intelligence (AI) software could potentially improve this entire workflow, most of the efforts have been focused on measurement automatization. Incorporation of automated selection of echocardiographic views could further optimize this process. Purpose We investigated whether an AI-derived measurements software can be supplemented by automated view selection using an AI model. We hypothesized that automated selection of echocardiographic views yields similar results compared to manually selected views when using the same tool for automated measurements in both groups (Figure 1, top). Methods We trained an AI model (convolutional neural network) to recognize the main standard 2D echocardiographic views. This model was trained on individual frames from 20,000 DICOM clips to predict a specific cardiac view, based on the view annotation provided by an echocardiography expert (ground-truth). We then selected a test set of 5,000 DICOMs where we applied the AI model to all images in the study. We selected the highest-ranking apical 2 chamber (A2C) and apical 4 chamber (A4C) images, based on the model’s output probability (Figure 1, bottom). For the same 21 studies, an expert selected the best A2C and A4C image for left ventricular volumes and ejection fraction measurements. Then, we ran Philips AutoMeasure both on manually and automatically selected images. We then compared the measurement values for statistical significance using a pair-wise t-test, see Figure 2. Results The AI model achieved an accuracy of 96% and AUC of 0.99 on the test set. The results obtained when comparing the automatic and manually selected images in the 21 studies are shown in Figure 2. For all the measured parameters, there was no statistically significant difference in mean values between both selection methods. Conclusions Manual and AI-derived selection of standard echocardiographic images do not lead to a statistically significant difference in automated measurements, suggesting that incorporation of automated view selection may contribute to improve daily echocardiographic workflow. Future research could aim in extending these findings to more cardiac views, parameters, and populations.Comparison of the approachesResults

Artificial intelligence-assisted focused cardiac ultrasound training: A survey among undergraduate medical students.

Focused cardiac ultrasound (FoCUS) is increasingly applied in many specialities, and adequate education and training of physicians is therefore mandatory. This study aimed to assess the impact of artificial intelligence (AI)-assisted interactive focused cardiac ultrasound (FoCUS) teaching session on undergraduate medical students' confidence level and knowledge in cardiac ultrasound. The AI-assisted interactive FoCUS teaching session was held during the 9th National Undergraduate Cardiovascular Conference in London in March 2023 and all undergraduate medical students were invited to attend, and 79 students enrolled and attended the training. Two workshops were conducted each over 3-hour period. Each workshop consisted of a theoretical lecture followed by a supervised hands-on session by experts, first workshop trained 39 students and the second workshop trained 40 students. The students' pre- and post-session knowledge and confidence levels were assessed by Likert-type-scale questionnaires filled in by the students before and immediately after the workshop. A total of 61 pre-session and 52 post-session questionnaires were completed. Confidence level in ultrasound skills increased significantly for all six domains after the workshop, with the greatest improvement seen in obtaining basic cardiac views (p < 0.001 for all six domains). Students strongly agreed about the effectiveness of the teaching session and supported the integration of ultrasound training into their medical curriculum. AI-assisted interactive FoCUS training can be an effective and powerful tool to increase ultrasound skills and confidence levels of undergraduate medical students. Integration of such ultrasound courses into the medical curriculum should therefore be considered.

Comparison of 6 handheld ultrasound devices by point-of-care ultrasound experts: a cross-sectional study

BackgroundPoint-of-care ultrasound (POCUS) has emerged as an essential bedside tool for clinicians, but lack of access to ultrasound equipment has been a top barrier to POCUS use. Recently, several handheld ultrasound devices (“handhelds”) have become available, and clinicians are seeking data to guide purchasing decisions. Few comparative studies of different handhelds have been done. We conducted a cross-sectional study comparing 6 handhelds readily available in the United States (Butterfly iQ + ™ by Butterfly Network Inc.; Clarius™ by Clarius Mobile Health; Kosmos™ by EchoNous; TE Air™ by Mindray; Vscan Air™ SL and CL by General Electric; and Lumify™ by Philips Healthcare). A multi-specialty group of physician POCUS experts (n = 35) acquired three standard ultrasound views (abdominal right upper quadrant, cardiac apical 4-chamber, and superficial neck and lung views) in random order on the same standardized patients and rated the image quality. Afterward, a final survey of the overall ease of use, image quality, and satisfaction of each handheld was completed.ResultsThirty-five POCUS experts specializing in internal medicine/hospital medicine, critical care, emergency medicine, and nephrology acquired and rated right upper quadrant, apical 4-chamber, and superficial neck and lung views with 6 different handhelds. For image quality, the highest-rated handhelds were Vscan Air™ for the right upper quadrant view, Mindray TE Air™ for the cardiac apical 4-chamber view, and Lumify™ for superficial views of the neck and lung. Overall satisfaction with image quality was highest with Vscan Air™, Lumify™, and Mindray, while overall satisfaction with ease of use was highest with Vscan Air™. The 5 most desirable characteristics of handhelds were image quality, ease of use, portability, probe size, and battery life. Ultimately, all 6 handhelds had notable advantages and disadvantages, with no single device having all desired qualities or features.ConclusionsThe overall satisfaction with image quality was rated highest with Vscan Air™, Lumify™, and Mindray TE Air™when acquiring right upper quadrant, apical 4-chamber, and superficial neck and lung views. No single handheld was perceived to be superior in image quality for all views. Vscan Air™ was rated highest for overall ease of use and was the most preferred handheld for purchase by POCUS experts.

RUSH to the Diagnosis: Identifying Occult Pathology in Hypotensive Patients

Case Presentation: A 63-year-old female presented to our emergency department with altered mental status and hypotension. She was transferred from the outpatient interventional radiology suite after becoming unresponsive during the removal of an inferior vena cava filter. The patient arrived somnolent with no other history available. Her physical exam was unremarkable. We used point-of-care-ultrasound to perform a rapid ultrasound for shock and hypotension (RUSH) exam. A large pericardial effusion along with signs of cardiac tamponade were identified. The cardiothoracic surgery team was notified, and the patient was taken to the operating room where pericardial blood and a large hematoma were evacuated. She recovered uneventfully and was discharged one week later. Discussion: The above case describes a very unstable patient whose diagnosis was obtained using the RUSH exam. History and physical did not point to a clear etiology. Options were very limited. She was too unstable to go for computed tomography, and other tests such as electrocardiogram, chest radiograph, and lab work would have been non-diagnostic. It was only after the cardiac view of the RUSH exam was obtained that a pericardial effusion and developing tamponade were identified, facilitating timely management. The RUSH exam, like the extended focused assessment with sonography for trauma, is used to help determine pathologies that need immediate intervention. Incorporation in the evaluation of critically ill patients reduces the time to diagnosis. Our case is a unique example of how point-of-care ultrasound can be used to urgently identify a life-threatening pathology.

Impaired left ventricular function in lean women with PCOS: Insights from speckle tracking echocardiography

Aims: We aimed to conduct a study examining left ventricular function (LVEF) in lean women PCOS patients with speckle tracking echocardiography. Methods: The study included 60 patients diagnosed with PCOS and 30 healthy controls matched for age and body mass index. Morning fasting blood samples were collected to measure levels of glucose, insulin, high-sensitivity C-reactive protein (hs-CRP), and lipids. Left ventricular function (LVF) was evaluated using two-dimensional speckle tracking echocardiography (2D-STE) and real-time three-dimensional echocardiography (3D-Echo). Global strain was assessed from three standard apical views using 2D-STE. Results: The hs-CRP levels in lean women with PCOS were significantly higher compared to the control group (2.34±1.07 vs. 1.13±0.54; p=0.01). The peak longitudinal strain values in the 2-chamber, 4-chamber, and long-axis views were lower in lean women with PCOS compared to the control group (15.9±1.2 vs. 19.4±1.2; p=0.01, 17.0±1.1 vs. 19.2±1.4; p=0.01, 16.3±1.3 vs. 19.2±1.5; respectively, p=0.01). According to the multiple regression model, global strain was independently associated with hs-CRP (β=0.31, p=0.04), the ratio of early diastolic mitral inflow velocity (E) to early diastolic annular velocity (E/E’ ratio) (β=0.33, p=0.01), and ejection fraction (EF) (β=0.35, p=0.01). Conclusion: Our findings reveal that lean women with PCOS exhibit significantly higher levels of high-sensitivity C-reactive protein (hs-CRP) compared to healthy controls. Furthermore, the peak longitudinal strain values across multiple cardiac views were notably lower in the PCOS group, suggesting impaired left ventricular function. These results highlight the importance of monitoring cardiovascular health in lean women with PCOS, as they are at an increased risk of developing left ventricular dysfunction despite their lean body mass index.

Echocardiographic cardiac views classification using whale optimization and weighted support vector machine

Echocardiographic cardiac views classification using whale optimization and weighted support vector machine

Rapid Ultrasonography for Shock and Hypotension Protocol Performed using Handheld Ultrasound Devices by Paramedics in a Moving Ambulance: Evaluation of Image Accuracy and Time in Motion.

Handheld ultrasound (US) devices have become increasingly popular since the early 2000s due to their portability and affordability compared to conventional devices. The Rapid Ultrasonography for Shock and Hypotension (RUSH) protocol, introduced in 2009, has shown promising accuracy rates when performed with handheld devices. However, there are limited data on the accuracy of such examinations performed in a moving ambulance. This study aimed to assess the feasibility and accuracy of the RUSH protocol performed by paramedics using handheld US devices in a moving ambulance. The study aimed to examine the performability of the RUSH protocol with handheld US devices in a moving ambulance and to evaluate the accuracy of diagnostic views obtained within an appropriate time frame. A prospective study was conducted with paramedics who underwent theoretical and practical training in the RUSH protocol. The participants performed the protocol using a handheld US device in both stationary and moving ambulances. Various cardiac and abdominal views were obtained and evaluated for accuracy. The duration of the protocol performance was recorded for each participant. Nine paramedics completed the study, with 18 performances each in both stationary and moving ambulance groups. The accuracy of diagnostic views obtained during the RUSH protocol did not significantly differ between the stationary and moving groups. However, the duration of protocol performance was significantly shorter in the moving group compared to the stationary group. Paramedics demonstrated the ability to perform the RUSH protocol effectively using handheld US devices in both stationary and moving ambulances following standard theoretical and practical training. The findings suggest that ambulance movement does not significantly affect the accuracy of diagnostic views obtained during the protocol. Further studies with larger sample sizes are warranted to validate these findings and explore the potential benefits of prehospital US in dynamic environments.

Analysing and Evaluating the Performance of Deep-Learning-Based Arrhythmia Detection Using Electrocardiogram Signals

Cardiac arrhythmia is a cardiac irregularity that impacts a significant number of individuals globally. Certain arrhythmias may be benign or occur only once, while recurring arrhythmias have the potential to cause organ failure, increase the risk of stroke by a factor of five, and even lead to sudden cardiac death. Thus, in order to identify and treat arrhythmia and prevent potentially fatal cardiac problems, the rapid detection and categorization of Electrocardiogram (ECG) signals is of paramount importance. The non-invasive technique employs electrodes to examine the electrical potentials of the heart, facilitating the detection of structural and functional irregularities that contribute to the diagnosis of cardiovascular illnesses. Nevertheless, the high likelihood of manual interpretation, which is both time-consuming and susceptible to error caused by weariness, poses a significant challenge for cardiologists in identifying and diagnosing cardiac issues. In recent times, there has been a growing utilization of Deep Learning (DL) models in the field of arrhythmia prediction, with the aim of enhancing clinical decision-making and potentially mitigating the likelihood of valetudinarian fatality. These models enhance the diagnostic capabilities of electrocardiography by detecting pathological situations, extracting anatomically meaningful data, evaluating cardio-motion, and assessing the quality of echo images. As a result, they serve as an alternative tool for precise diagnosis and treatment of arrhythmias. In order to forecast and categorize arrhythmias based on ECG signal data, this study provides a comprehensive review of various deep learning approaches. The initial section of the study provides a concise overview of various deep learning-based prediction models that have been developed by multiple researchers for echocardiography systems. Subsequently, a comparative analysis is undertaken to comprehend the limitations of those algorithms and propose a novel approach to improve the accuracy of cardiac view classification in echocardiography systems.

Real-time localization for port-implanted catheter tip by echocardiographic guidance

The clinical data of 23 patients undergoing real-time echocardiography-guided infusion port implantation in the Breast Center of Tsinghua Changgung Hospital in Beijing from January to July 2021 were analyzed. The length of catheter insertion L1 was initially estimated using surface measurement method in all patients. Intraoperatively, transthoracic echocardiography was applied using the parasternal four-chamber view to visualize the catheter image within the right atrium, and the length of catheter insertion L2 was recorded under the guidance of echocardiography. Postoperatively, chest radiographs were taken in the upright position to observe the position of the catheter tip. According to chest CT scans, the ideal length (L) for catheter tip placement was calculated when it was located at the junction of superior vena cava and right atrium. Bland-Altman scatter plot analysis and linear regression fitting test were used on L1 and L2 respectively with L to evaluate the consistency. A total of 23 patients were included in this study, among which one case of left breast cancer patient undergoing breast-conserving surgery had difficulty in identifying the catheter tip position due to residual pleural effusion obscuring the imaging of the cardiac apex four-chamber view. In 22 patients, the results of intraoperative ultrasound imaging were good, including 1 case of catheter ectopic to azygos vein, and 21 cases of right atrial catheter could be detected by ultrasound. Statistical analysis showed that there was a good consistency between L1 and L, L2 and L, and the difference between them was d=0.28 cm (95%CI:-1.76-2.31 cm) and d=0.20 cm(95%CI:-0.84-1.23 cm), respectively, with no statistical significance (P>0.05). In the linear regression model, L2 and L had a higher fit than L1, and the difference was statistically significant (R²=0.954, P<0.001). This study found that real-time echocardiographic localization technique can be applied in adult port surgery to replace X-ray-guided real-time catheter tip detection and adjustment to the optimal position.

The effect of real-time EF automatic tool on cardiac ultrasound performance among medical students.

Point-of-care ultrasound (POCUS) is a sensitive, safe, and efficient tool used in many clinical settings and is an essential part of medical education in the United States. Numerous studies present improved diagnostic performances and positive clinical outcomes among POCUS users. However, others stress the degree to which the modality is user-dependent, rendering high-quality POCUS training necessary in medical education. In this study, the authors aimed to investigate the potential of an artificial intelligence (AI) based quality indicator tool as a teaching device for cardiac POCUS performance. The authors integrated the quality indicator tool into the pre-clinical cardiac ultrasound course for 4th-year medical students and analyzed their performances. The analysis included 60 students who were assigned to one of two groups as follows: the intervention group using the AI-based quality indicator tool and the control group. Quality indicator users utilized the tool during both the course and the final test. At the end of the course, the authors tested the standard echocardiographic views, and an experienced clinician blindly graded the recorded clips. Results were analyzed and compared between the groups. The results showed an advantage in quality indictor users' median overall scores (P = 0.002) with a relative risk of 2.3 (95% CI: 1.10, 4.93, P = 0.03) for obtaining correct cardiac views. In addition, quality indicator users also had a statistically significant advantage in the overall image quality in various cardiac views. The AI-based quality indicator improved cardiac ultrasound performances among medical students who were trained with it compared to the control group, even in cardiac views in which the indicator was inactive. Performance scores, as well as image quality, were better in the AI-based group. Such tools can potentially enhance ultrasound training, warranting the expansion of the application to more views and prompting further studies on long-term learning effects.

Self-supervised learning for medical image data with anatomy-oriented imaging planes

Self-supervised learning has emerged as a powerful tool for pretraining deep networks on unlabeled data, prior to transfer learning of target tasks with limited annotation. The relevance between the pretraining pretext and target tasks is crucial to the success of transfer learning. Various pretext tasks have been proposed to utilize properties of medical image data (e.g., three dimensionality), which are more relevant to medical image analysis than generic ones for natural images. However, previous work rarely paid attention to data with anatomy-oriented imaging planes, e.g., standard cardiac magnetic resonance imaging views. As these imaging planes are defined according to the anatomy of the imaged organ, pretext tasks effectively exploiting this information can pretrain the networks to gain knowledge on the organ of interest. In this work, we propose two complementary pretext tasks for this group of medical image data based on the spatial relationship of the imaging planes. The first is to learn the relative orientation between the imaging planes and implemented as regressing their intersecting lines. The second exploits parallel imaging planes to regress their relative slice locations within a stack. Both pretext tasks are conceptually straightforward and easy to implement, and can be combined in multitask learning for better representation learning. Thorough experiments on two anatomical structures (heart and knee) and representative target tasks (semantic segmentation and classification) demonstrate that the proposed pretext tasks are effective in pretraining deep networks for remarkably boosted performance on the target tasks, and superior to other recent approaches.

Artificial intelligence-based classification of echocardiographic views

Abstract Aims Augmenting echocardiography with artificial intelligence would allow for automated assessment of routine parameters and identification of disease patterns not easily recognized otherwise. View classification is an essential first step before deep learning can be applied to the echocardiogram. Methods and results We trained two- and three-dimensional convolutional neural networks (CNNs) using transthoracic echocardiographic (TTE) studies obtained from 909 patients to classify nine view categories (10 269 videos). Transthoracic echocardiographic studies from 229 patients were used in internal validation (2582 videos). Convolutional neural networks were tested on 100 patients with comprehensive TTE studies (where the two examples chosen by CNNs as most likely to represent a view were evaluated) and 408 patients with five view categories obtained via point-of-care ultrasound (POCUS). The overall accuracy of the two-dimensional CNN was 96.8%, and the averaged area under the curve (AUC) was 0.997 on the comprehensive TTE testing set; these numbers were 98.4% and 0.998, respectively, on the POCUS set. For the three-dimensional CNN, the accuracy and AUC were 96.3% and 0.998 for full TTE studies and 95.0% and 0.996 on POCUS videos, respectively. The positive predictive value, which defined correctly identified predicted views, was higher with two-dimensional rather than three-dimensional networks, exceeding 93% in apical, short-axis aortic valve, and parasternal long-axis left ventricle views. Conclusion An automated view classifier utilizing CNNs was able to classify cardiac views obtained using TTE and POCUS with high accuracy. The view classifier will facilitate the application of deep learning to echocardiography.

PREDICTING CARDIAC ISSUES FROM ECHOCARDIOGRAMS: A LITERATURE REVIEW USING DEEP LEARNING AND MACHINE LEARNING TECHNIQUES

Cardiovascular disease (CVD) has a substantial impact on overall health, well-being, and life-expectancy. Echocardiography is a widely used imaging technique in cardiovascular medicine, utilizing various medical imaging technology to visualize heart chambers and valve’s motion activity. In order to diagnose and treat complicated cardiovascular problems, it takes high-resolution images of the heart and its surroundings. However, it has limitations such as long procedure times, multiple measurement values, complex analyses, individualized assessments, operator subjectivity, and wide observation ranges. This makes it challenging for sonographers to accurately detect and diagnose heart diseases. In recent days, Deep Learning (DL) is increasingly used in clinical computer-assisted systems for disease detection, feature segmentation, functional evaluation, and diagnosis. It is an alternate technique for accurate detection and treatment of cardiovascular disorders; it improves the diagnostic capacities of echocardiography by identifying pathological conditions, extracting anatomically significant data, measuring cardio-motion, and calculating echo image quality. This paper presents a detailed review of various DL frameworks developed to analyse different cardiac views using echocardiography for improving the prediction and diagnosis of CVD. At the outset, a variety of echocardiography systems linked with DL-based segmentation and classification are reviewed briefly. Afterwards, a comparison research is carried out to gain insight into the shortcomings of those algorithms and provide a fresh approach to improve the accuracy of cardiac view categorization in echocardiography systems.

Cardiac point-of-care ultrasound: Practical integration in the pediatric and neonatal intensive care settings.

Cardiac point-of-care ultrasound (POCUS) is a technology increasingly leveraged at the bedside by pediatric criticalcare and neonatology providers to identify real-time hemodynamic pathophysiology. We present a framework for (1)identifying the scope of cardiac POCUS within the clinical practice setting, (2) standardizing views for protocolizedhemodynamic assessment relevant to pediatric critical illness and (3) integrating POCUS findings for therapeuticguidance. Within the review, we also discuss practical strengths and limitations to image acquisition andinterpretation within the varied cardiac POCUS views. Finally, we explore unique considerations within the neonatalpopulation. Conclusion: Cardiac POCUS is a technology and tool that reveals important real-time information at the bedside of the criticallyill child and infant. Understanding strengths and limitations of cardiac POCUS views and protocolizing an approachto answer focused clinical questions provides a framework for training and translation to clinical care. What is Known: • Ultrasound technology is now ubiquitous among pediatric critical care and neonatology settings, and growing literature supports an expanded role in not only procedural but also diagnostic applications. • Cardiac POCUS influences provider perception of pathophysiology and changes clinical management. What is New: • Effective cardiac POCUS training and subsequent translation to clinical practice should improve when clinical questions and protocolized approaches to image acquisition are standardized within a specialty. • Cardiac POCUS views have strengths and limitations which must be recognized when assessing the hemodynamic profile of a child or neonate.

Myocardial strain assessment in the human fetus by cardiac MRI using Doppler ultrasound gating and feature tracking

ObjectivesAssessment of myocardial strain by feature tracking magnetic resonance imaging (FT-MRI) in human fetuses with and without congenital heart disease (CHD) using cardiac Doppler ultrasound (DUS) gating.MethodsA total of 43 human fetuses (gestational age 28–41 weeks) underwent dynamic cardiac MRI at 3 T. Cine balanced steady-state free-precession imaging was performed using fetal cardiac DUS gating. FT-MRI was analyzed using dedicated post-processing software. Endo- and epicardial contours were manually delineated from fetal cardiac 4-chamber views, followed by automated propagation to calculate global longitudinal strain (GLS) of the left (LV) and right ventricle (RV), LV radial strain, and LV strain rate.ResultsStrain assessment was successful in 38/43 fetuses (88%); 23 of them had postnatally confirmed diagnosis of CHD (e.g., coarctation, transposition of great arteries) and 15 were heart healthy. Five fetuses were excluded due to reduced image quality. In fetuses with CHD compared to healthy controls, median LV GLS (− 13.2% vs. − 18.9%; p < 0.007), RV GLS (− 7.9% vs. − 16.2%; p < 0.006), and LV strain rate (1.4 s−1 vs. 1.6 s−1; p < 0.003) were significantly higher (i.e., less negative). LV radial strain was without a statistically significant difference (20.7% vs. 22.6%; p = 0.1). Bivariate discriminant analysis for LV GLS and RV GLS revealed a sensitivity of 67% and specificity of 93% to differentiate between fetuses with CHD and healthy fetuses.ConclusionMyocardial strain was successfully assessed in the human fetus, performing dynamic fetal cardiac MRI with DUS gating. Our study indicates that strain parameters may allow for differentiation between fetuses with and without CHD.Clinical relevance statementMyocardial strain analysis by cardiac MRI with Doppler ultrasound gating and feature tracking may provide a new diagnostic approach for evaluation of fetal cardiac function in congenital heart disease.Key Points• MRI myocardial strain analysis has not been performed in human fetuses so far.• Myocardial strain was assessed in human fetuses using cardiac MRI with Doppler ultrasound gating.• MRI myocardial strain may provide a new diagnostic approach to evaluate fetal cardiac function.

Kiosk 5R-FA-02 - Ai-based Fully Automated Cardiac View Planning - Comparison to Manual Expert Planning

Kiosk 5R-FA-02 - Ai-based Fully Automated Cardiac View Planning - Comparison to Manual Expert Planning

Development and Validation of a Deep-Learning Network for Detecting Congenital Heart Disease from Multi-View Multi-Modal Transthoracic Echocardiograms.

Early detection and treatment of congenital heart disease (CHD) can significantly improve the prognosis of children. However, inexperienced sonographers often face difficulties in recognizing CHD through transthoracic echocardiogram (TTE) images. In this study, 2-dimensional (2D) and Doppler TTEs of children collected from 2 clinical groups from Beijing Children's Hospital between 2018 and 2022 were analyzed, including views of apical 4 chamber, subxiphoid long-axis view of 2 atria, parasternal long-axis view of the left ventricle, parasternal short-axis view of aorta, and suprasternal long-axis view. A deep learning (DL) framework was developed to identify cardiac views, integrate information from various views and modalities, visualize the high-risk region, and predict the probability of the subject being normal or having an atrial septal defect (ASD) or a ventricular septaldefect (VSD). A total of 1,932 children (1,255 healthy controls, 292 ASDs, and 385 VSDs) were collected from 2 clinical groups. For view classification, the DL model reached a mean [SD] accuracy of 0.989 [0.001]. For CHD screening, the model using both 2D and Doppler TTEs with 5 views achieved a mean [SD] area under the receiver operating characteristic curve (AUC) of 0.996 [0.000] and an accuracy of 0.994 [0.002] for within-center evaluation while reaching a mean [SD] AUC of 0.990 [0.003] and an accuracy of 0.993 [0.001] for cross-center test set. For the classification of healthy, ASD, and VSD, the model reached the mean [SD] accuracy of 0.991 [0.002] and 0.986 [0.001] for within- and cross-center evaluation, respectively. The DL models aggregating TTEs with more modalities and scanning views attained superior performance to approximate that of experienced sonographers. The incorporation of multiple views and modalities of TTEs in the model enables accurate identification of children with CHD in a noninvasive manner, suggesting the potential to enhance CHD detection performance and simplify the screening process.

Use of POCUS for the assessment of dehydration in pediatric patients-a narrative review.

In pediatric practice, POCUS (point-of-care ultrasound) has been mostly implemented to recognize lung conditions and pleural and pericardial effusions, but less to evaluate fluid depletion. The main aim of this review is to analyze the current literature on the assessment of dehydration in pediatric patients by using POCUS. The size of the inferior vena cava (IVC) and its change in diameter in response to respiration have been investigated as a tool to screen for hypovolemia. A dilated IVC with decreased collapsibility (< 50%) is a sign of increased right atrial pressure. On the contrary, a collapsed IVC may be indicative of hypovolemia. The IVC collapsibility index (cIVC) reflects the decrease in the diameter upon inspiration. Altogether the IVC diameter and collapsibility index can be easily determined, but their role in children has not been fully demonstrated, and an estimation of volume status solely by assessing the IVC should thus be interpreted with caution. The inferior vena cava/abdominal aorta (IVC/AO) ratio may be a suitable parameter to assess the volume status in pediatric patients even though there is a need to define age-based thresholds. A combination of vascular, lung, and cardiac POCUS could be a valuable supplementary tool in the assessment of dehydration in several clinical scenarios, enabling rapid identification of life-threatening primary etiologies and helping physicians avoid inappropriate therapeutic interventions. Conclusion: POCUS can provide important information in the assessment of intravascular fluid status in emergency scenarios, but measurements may be confounded by a number of other clinical variables. The inclusion of lung and cardiac views may assist in better understanding the patient's physiology and etiology regarding volume status. What is Known: • In pediatric practice, POCUS (point-of-care ultrasound) has been mostly implemented to recognize lung conditions (like pneumonia and bronchiolitis) and pleural and pericardial effusions, but less to evaluate fluid depletion. • The size of the IVC (inferior vena cava) and its change in diameter in response to respiration have been studied as a possible screening tool to assess the volume status, predict fluid responsiveness, and assess potential intolerance to fluid loading. What is New: • The IVC diameter and collapsibility index can be easily assessed, but their role in predicting dehydration in pediatric age has not been fully demonstrated, and an estimation of volume status only by assessing the IVC should be interpreted carefully. • The IVC /AO(inferior vena cava/abdominal aorta) ratio may be a suitable parameter to assess the volume status in pediatric patients even though there is a need to define age-based thresholds.A combination of vascular, lung, and cardiac POCUS can be a valuable supplementary tool in the assessment of intravascular volume in several clinical scenarios.