Echocardiography, or cardiac ultrasound, is the most widely used and readily available imaging modality to assess cardiac function and structure. Combining portable instrumentation, rapid image acquisition, high temporal resolution, and without the risks of ionizing radiation, echocardiography is one of the most frequently utilized imaging studies in the United States and serves as the backbone of cardiovascular imaging. For diseases ranging from heart failure to valvular heart diseases, echocardiography is both necessary and sufficient to diagnose many cardiovascular diseases. In addition to our deep learning model, we introduce a new large video dataset of echocardiograms for computer vision research. The EchoNet-Dynamic database includes 10,030 labeled echocardiogram videos and human expert annotations (measurements, tracings, and calculations) to provide a baseline to study cardiac motion and chamber sizes.

The EchoNotes Structured Database derived from MIMIC-III (ECHO-NOTE2NUM) is a structured echocardiogram database derived from 43,472 observational notes obtained during echocardiogram studies conducted in the intensive care unit at the Beth Israel Deaconess Medical Center between 2001 and 2012. The database encompasses various aspects of cardiac structure and function, including cavity size, wall thickness, systolic and diastolic function, valve regurgitation and stenosis, as well as pulmonary pressures. To facilitate extensive data analysis, the clinical notes were transformed into a structured numerical format. Within each echocardiogram report sentence, specific words or phrases were identified to describe abnormal findings, and a severity staging system using numeric categories was established. This large publicly-accessible database of structured echocardiogram data holds significant potential as a tool to investigate cardiovascular disease in the intensive care unit.

Large volumes of data are continuously generated from clinical notes and diagnostic studies catalogued in electronic health records (EHRs). Echocardiography is one of the most commonly ordered diagnostic tests in cardiology. This study sought to explore the feasibility and reliability of using natural language processing (NLP) for large-scale and targeted extraction of multiple data elements from echocardiography reports. An NLP tool, EchoInfer, was developed to automatically extract data pertaining to cardiovascular structure and function from heterogeneously formatted echocardiographic data sources. EchoInfer was applied to echocardiography reports (2004 to 2013) available from 3 different on-going clinical research projects. EchoInfer analyzed 15,116 echocardiography reports from 1684 patients, and extracted 59 quantitative and 21 qualitative data elements per report. EchoInfer achieved a precision of 94.06%, a recall of 92.21%, and an F1-score of 93.12% across all 80 data elements in 50 reports. Physician review of 400 reports demonstrated that EchoInfer achieved a recall of 92–99.9% and a precision of >97% in four data elements, including three quantitative and one qualitative data element. Failure of EchoInfer to correctly identify or reject reported parameters was primarily related to non-standardized reporting of echocardiography data. EchoInfer provides a powerful and reliable NLP-based approach for the large-scale, targeted extraction of information from heterogeneous data sources. The use of EchoInfer may have implications for the clinical management and research analysis of patients undergoing echocardiographic evaluation.

This project aims to provide all the materials to the community to resolve the problem of echocardiographic image segmentation and volume estimation from 2D ultrasound sequences (both two and four-chamber views). To this aim, the following solutions were set up.

Introduction of the largest publicly-available and fully-annotated dataset for 2D echocardiographic assessment (to our knowledge). The CAMUS dataset, containing 2D apical four-chamber and two-chamber view sequences acquired from 500 patients, is made available for download.

Deployment of a dedicated Girder online platform. This platform aims to assess in a reproducible manner the performance of methods for segmenting cardiac structures (left ventricle endocardium and epicardium and left atrium borders) and extracting clinical indices (left ventricle volumes and ejection fraction).

The CAMUS online platform is now available and will be maintained and kept open as long as the data remains relevant for clinical research.

Aims: Echocardiographic assessment of left ventricular filling pressures is performed using a multi-parametric algorithm. Unselected sample of patients with heart failure with reduced ejection fraction (HFrEF) patients may demonstrate an indeterminate status of diastolic indices making interpretation challenging. We sought to test improvement in the diagnostic accuracy of standard and strain echocardiography of the left ventricle and left atrium (LA) to estimate a pulmonary capillary wedge pressure (PCWP) > 15 mmHg in patients with HFrEF.

Methods and results: Out of 82 consecutive patients, 78 patients were included in the final analysis and right heat catheterization, and echocardiogram was performed simultaneously. According to the univariable analysis, E wave velocity, the ratio between E-wave/A-wave (E/A, area under the curve [AUC] = 0.81, respectively), isovolumic relaxation time (AUC = 0.83), pulmonary vein D wave (AUC = 0.84), pulmonary vein S/D Ratio (AUC = 0.85), early pulmonary regurgitation velocity (AUC = 0.80), and accelerationa time at right ventricular out-flow tract (RVOT AT, AUC = 0.84) identified with the highest accuracy PCWP > 15 mmHg. They were all tested in multivariate analysis, and they were not independently correlated with PCWP. Tricuspid regurgitation (TR) velocity was measurement with the highest predictive value in identifying PCWP > 15 mmHg (AUC = 0.89), compared with other established parameters such as the ratio between e-wave velocity divided by mitral annular e' velocity (E/e'), deceleration time, or LA indexed volume (LAVi), which all reached a lower accuracy level (AUC = 0.75; 0.78; 0.76). Among strain measures, global longitudinal strain in four chamber view (GLS 4ch), the ratio between e-wave velocity divided by mitral annular e' strain rate (E/e'sr), and LA longitudinal strain at the reservoir phase were helpful in estimating elevated PCWP (AUC = 0.77; 0.76; 0.75). According to multivariable analysis, the following two models had the greatest accuracy in detecting PCWP > 15 mmHg: (i) TR velocity, LAVi, and E wave velocity (receiver operating characteristic [ROC]-AUC = 0.98), (ii) AT RVOT, LAVi and GLS 4ch (ROC-AUC = 0.96). Neither E/A (ROC-AUC = 0.81) nor E/e' (ROC-AUC = 0.75) was an independent predictor when included in the model. The two MODELS were applicable to the entire population and demonstrated better agreement with the invasive reference (91% and 88%) than the guidelines algorithm (77%) regardless of the type of rhythm.

Conclusions: Our suggested echocardiographic approach could be used to potentially reduce the frequency of "doubtful" classification and increase the accuracy in predicting elevated left ventricular filling pressure leading to a decrease in the number of invasive assessment made by right heart catheterization.

Abstract Background Coronavirus disease 2019 (Covid-19) can lead to severe respiratory distress and acute cardiac injury, but it is unclear how often it can cause cardiac dysfunction. Objective In this systematic review, we aimed to summarize the main echocardiographic findings in patients with Covid-19. Methods We systematically searched in PUBMED, EMBASE, LILACS and Cochrane databases, in addition MedRxiv and Scielo preprints from inception to July 21st, 2021. Studies reporting echocardiographic data in patients with Covid-19 were included. Demographic characteristics, previous cardiovascular disease (CVD), and echocardiographic findings were extracted. We performed a meta-analysis of proportions to estimate the main echocardiographic findings. The level of significance was p < 0.05. Results From 11,233 studies, 38 fulfilled inclusion criteria and were included in the meta-analysis. The estimated proportions of left ventricular (LV) systolic dysfunction were 25% (95%CI: 19, 31; I293%), abnormal global longitudinal strain 34% (95% CI 23, 45; I290%), righ ventricular (RV) systolic dysfunction 17% (95%CI 13, 21; I290%), pericardial effusion 17% (95%CI: 9, 26; I297%), and pulmonary hypertension 23% (95%CI: 15, 33, I2 96%). LV systolic dysfunction was directly associated with study-specific prevalence of previous abnormal echocardiogram (p

Table I. Calcium transient and contractility parameters of cardiomyocytes infected with Ad_pre-mir-146a or Ad_decoy-146a.

Top table, isolated mouse cardiomyocytes were infected with Ad_pre-mir-146a (MOI=50) for 24h. Data represent the mean ± s.e.m. in all panels (n=100-150 cells/4 hearts).

Bottom table, isolated mouse cardiomyocytes were infected with Ad_decoy-146a (MOI=50) for 24h. Data represent the mean ± s.e.m. in all panels (n=100-150 cells/4 hearts). Table II. Echocardiographic parameters of mice injected with rAAV9_pre-mir-146a.

Data represent the mean ± s.e.m. of cardiac functional parameters 4 weeks post-injection. Table III. Hemodynamic parameters of mice injected with rAAV9_pre-mir-146a.

Data represent the mean ± s.e.m. of cardiac functional parameters 4 weeks post-injection. Max dPdt, maximum dP/dt; Min dPdt, minimum dP/dt. Table IV. Echocardiographic parameters of mice injected with rAAV9_decoy-146a.

Data represent the mean ± s.e.m. of cardiac functional parameters 4 weeks post-injection. Table V. Hemodynamic parameters of mice injected with rAAV9_decoy-146a.

Data represent the mean ± s.e.m. of cardiac functional parameters 4 weeks post-injection. Table VI. Echocardiographic parameters of SUMO1 TG mice injected with rAAV9_pre-mir-146a.

Data represent the mean ± s.e.m. of cardiac functional parameters 8 weeks post-injection. NL, Negative littermate; SUMO1 TG, Cardiac-specific Cre/loxP-conditional Sumo1-transgenic miceTable VII. Cardiac cellular composition.

Isolated cardiac cells were sorted with FACS and analyzed. Data represent the mean ± s.e.m. (n=5).

Global structural heart imaging market worth at USD 9.58 Billion in 2024, is expected to surpass USD 17.33 Billion by 2034, with a CAGR of 6.11% from 2025 to 2034

AimsSignificant tricuspid regurgitation (TR) in atrial fibrillation (AF) patients is becoming a global issue, as it can lead to progressive right ventricular enlargement and heart failure, thereby increasing morbidity and mortality. This study aimed to evaluate potential predictors of significant TR in AF patients using open databases.MethodsPubMed, EMBASE, the Cochrane Library, and Web of Science were searched for relevant studies from inception to September 2023. Using STATA 14.0 statistical software, hazard ratios (HRs) were calculated for data synthesis. The potential predictors included clinical characteristics, echocardiography parameters, and prior comorbidities. Evidence certainty was evaluated based on the GRADE system.ResultsIn total, 12 studies involving almost 16,000 patients were included in this review. Female sex (HR = 2.14; 95% CI: 1.84–2.49; I2 = 0.0%; p = 0.430), persistent atrial fibrillation (HR = 2.99; 95% CI: 2.47–3.61; I2 = 0.0%; p = 0.896), left ventricular ejection fraction [standard mean difference (SMD) = −0.16; 95% CI:−0.30 to −0.03; I2 = 69.8%; p 

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Echocardiography Devices Market Size 2024-2028

The echocardiography (ECG) devices market size is forecast to increase by USD 5.27 billion at a CAGR of 6.78% between 2023 and 2028.

The ECG devices market is experiencing significant growth due to the increasing prevalence of cardiac disorders. The integration of advanced technologies, such as artificial intelligence and machine learning, is driving the development of more accurate and efficient ECG devices. However, the high cost of ECG products and procedures remains a challenge for both patients and healthcare providers. Despite this, the market is expected to continue growing due to the increasing demand for early and accurate diagnosis of cardiac conditions. Additionally, the integration of telemedicine and remote monitoring technologies is expanding access to ECG testing and improving patient outcomes.

What will be the Size of the Echocardiography (ECG) Devices Market During the Forecast Period?

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The global echocardiography market is experiencing significant growth due to the increasing prevalence of cardiovascular diseases, such as blood clots, heart valve disorders, and atrial fibrillation. These noninvasive diagnostic procedures play a crucial role in assessing heart function, including the heart's chambers and valves. Both transthoracic and transesophageal methods are commonly used for echocardiography examinations. Stress cardiac ultrasound is another variant that provides valuable information on heart function under various conditions. Moreover, advancements in Heart Attack Diagnostics are complementing the growth of echocardiography, enabling quicker detection and more accurate assessment of heart-related issues.



Technological developments, including ultrasound systems with improved efficiency and workflow optimization, are driving market growth. Notable institutions, such as the Heart Center Leipzig and Saarland University Hospital, are at the forefront of advancing cardiovascular imaging through innovative echocardiography devices like the Ultrasound 3300. The British Heart Foundation estimates that over 92 million adults globally live with cardiovascular diseases, further emphasizing the importance of this market in addressing uncontrolled hypertension and other related conditions.

How is this Echocardiography (ECG) Devices Industry segmented and which is the largest segment?

The echocardiography (ECG) devices industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Hospitals
  Diagnostic centers


Product

  Resting
  Ambulatory
  Stress


Geography

  North America

    US


  Europe

    Germany


  Asia

    China
    Japan


  Rest of World (ROW)

By End-user Insights

The hospitals segment is estimated to witness significant growth during the forecast period.

Echocardiography, a non-invasive diagnostic procedure utilizing transducer technology to emit and receive ultrasonic sound waves, is experiencing increased demand due to its ability to identify cardiomyopathies, such as dilated and hypertrophic forms, with high accuracy. This technique is particularly valuable in diagnosing chest pain or related symptoms, which may indicate heart disease. The benefits of echocardiography include its non-invasive nature and lack of known risks or adverse effects. As not all hospitals offer this service, diagnostic centers are seizing the opportunity to acquire echocardiography devices and provide specialized services to patients. Technological advancements continue to drive product innovation in this field, ensuring measurement uniformity and improving diagnostic accuracy.

Get a glance at the Echocardiography (ECG) Devices Industry report of share of various segments Request Free Sample

The Hospitals segment was valued at USD 7.34 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 45% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The ECG devices market in North America is driven by the high prevalence of cardiovascular diseases (CVD), increasing geriatric population, and advanced technology adoption. The US and Canada are significant contributors to the market's revenue due to substantial healthcare expenditure on CVD treatment. According to the US Census Bureau, the population aged 65 and above was 17.3% in 2022, with this demographic more susceptible to CVD and undergoing frequent ECG procedures. Government and non-pro

Echocardiographic parameters of the study population.

This SR-TEE dataset is for our accepted paper at MICCAI2023 titled 'Regressing Simulation to Real: Unsupervised Domain Adaptation for Automated Quality Assessment in Transoesophageal Echocardiography'. Official code can be found at https://github.com/wzjialang/SR-AQA.

It includes 16,192 simulated and 4,427 real transoesophageal echocardiography (TEE) images from 9 standard views (i.e., Mid-Esophageal 4-Chamber, Mid-Esophageal 2-Chamber, Mid-Esophageal Aortic Valve Short-Axis, Transgastric Mid-Short-Axis, Mid-Esophageal Right Ventricle inflow-outflow, Mid-Esophageal Aortic Valve Long-Axis, Transgastric 2-Chamber, Deep Transgastric Long-Axis, Mid-Esophageal Mitral Commissural).

Simulated images were collected with the HeartWorks TEE simulation platform from 38 participants of varied experience asked to image the 9 views. Fully anonymized real TEE data were collected from 10 cardiovascular procedures in 2 hospitals, with ethics for research use and collection approved by the respective Research Ethics Committees.

Each image is annotated by 3 expert anaesthetists with two independent scores w.r.t. two automated quality assessment tasks for TEE. The criteria percentage (CP) score ranging from ‘0-100’, measuring the number of essential criteria, from the checklists of the ASE/SCA/BSE imaging guidelines, met during image acquisition and a general impression (GI) score ranging from ‘0-4‘, representing overall ultrasound image quality.

There are significant style differences (e.g. resolution, brightness, contrast, acoustic shadowing, and refraction artifact) between simulated and real data, posing a considerable challenge to unsupervised domain adaptation.

The structure of the dataset is as follows:

'real_cases_data_frames' folder: contains real TEE images. 'simulated_data_frames' folder: contains simulated TEE images. real_cases_data_frames.csv: ground truth of real TEE images, four columns represent image name, view class, CP value, and GI value, respectively. simulated_data_frames.csv: ground truth of simulated TEE images, four columns represent image name, view class, CP value, and GI value, respectively.

The AI Medical Imaging Software market for cardiovascular disease is experiencing robust growth, driven by the increasing prevalence of heart conditions globally and the need for faster, more accurate diagnoses. The market, estimated at $2 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $7 billion by 2033. This growth is fueled by several key factors: advancements in AI algorithms capable of detecting subtle anomalies in medical images (ECG, echocardiograms, CT scans, etc.), the increasing adoption of cloud-based solutions for image analysis and data sharing, and the rising demand for improved patient outcomes through early and precise diagnosis. Major players like Siemens, United-Imaging, and Lepu Medical are leading the innovation, constantly improving the accuracy and efficiency of AI-powered cardiovascular diagnostics. However, challenges remain, including regulatory hurdles in securing approvals for AI-driven medical devices, concerns about data privacy and security, and the need for robust clinical validation to ensure reliable performance and widespread adoption. The segmentation of the market reflects the diverse applications of AI in cardiovascular care. This includes solutions focused on specific imaging modalities (e.g., AI for echocardiogram analysis, AI for coronary CT angiography analysis), different disease areas (e.g., AI for arrhythmia detection, AI for heart failure risk stratification), and various end-users (hospitals, diagnostic centers, cardiology clinics). The North American and European markets currently hold significant shares, but rapid growth is anticipated in Asia-Pacific regions driven by increasing healthcare investment and technological advancements. The competitive landscape is dynamic, with established medical device companies and innovative startups vying for market share, fostering innovation and driving down costs, ultimately benefiting patients and healthcare providers alike. Future growth hinges on overcoming the aforementioned challenges and continuing to demonstrate the clinical utility and cost-effectiveness of AI-driven cardiovascular imaging solutions.

Volume overload and fluid congestion are a fundamental issue in the assessment and management of patients with heart failure (HF). Recent studies have found that in acute decompensated heart failure (ADHF), right and left-sided pressures generally start to increase before any notable weight changes take place preceding an admission. ADHF may be a problem of volume redistribution among different vascular compartments instead of, or in addition to, fluid shift from the interstitial compartment. Thus, identifying heterogeneity of volume overload would allow guidance of tailored therapy. A comprehensive evaluation of congestive HF needs to take into account myriad parameters, including physical examination, echocardiographic values, and biomarker serum changes. Furthermore, potentially useful diagnostic tools include bioimpedance to measure intercompartmental fluid shifts, and evaluation of ultrasound lung comets to detect extravascular lung water.

A beginner-friendly version of the MIT-BIH Arrhythmia Database, which contains 48 electrocardiograms (EKGs) from 47 patients that were at Beth Israel Deaconess Medical Center in Boston, MA in 1975-1979.

There are 48 CSVs, each of which is a 30-minute echocardiogram (EKG) from a single patient (record 201 and 202 are from the same patient). Data was collected at 360 Hz, meaning that 360 data points is equal to 1 second of time.

Banner photo by Joshua Chehov on Unsplash.

How to Analyze the Heart with Python

1. How to Analyze Heartbeats in 15 Minutes with Python
2. How the Heart Works (and What is a "QRS" Complex?)
3. How to Identify and Label the Waves of an EKG
4. How to Flatten a Wandering EKG
5. How to Calculate the Heart Rate

What is a 12-lead EKG?

EKGs, or electrocardiograms, measure the heart's function by looking at its electrical activity. The electrical activity in each part of the heart is supposed to happen in a particular order and intensity, creating that classic "heartbeat" line (or "QRS complex") you see on monitors in medical TV shows.

There are a few types of EKGs (4-lead, 5-lead, 12-lead, etc.), which give us varying detail about the heart. A 12-lead is one of the most detailed types of EKGs, as it allows us to get 12 different outputs or graphs, all looking at different, specific parts of the heart muscles.

This dataset only publishes two leads from each patient's 12-lead EKG, since that is all that the original MIT-BIH database provided.

What does each part of the QRS complex mean?

Check out Ninja Nerd's EKG Basics tutorial on YouTube to understand what each part of the QRS complex (or heartbeat) means from an electrical standpoint.

Filenames

Each file's name is the ID of the patient (except for 201 and 202, which are the same person).

Columns

• index
• calculated elapsed milliseconds (index / 360 * 1000)
• the first lead
• the second lead

The two leads are often lead MLII and another lead such as V1, V2, or V5, though some datasets do not use MLII at all. MLII is the lead most often associated with the classic QRS Complex (the medical name for a single heartbeat).

Milliseconds were calculated and added as a secondary index to each dataset. Calculations were made by dividing the index by 360 Hz then multiplying by 1000. The original index was preserved, since the calculation of milliseconds as digital signals processing (e.g. filtering) occurs may cause issues with the correlation and merging of data. You are encouraged to try whichever index is most suitable for your analysis and/or recalculate a time index with Pandas' to_timedelta().

Patient information

Info about each of the 47 patients is available here, including age, gender, medications, diagnoses, etc.

Getting Started

Physionet has some online tutorials and tips for analyzing EKGs and other time series / digital signals.

Check out our notebook for opening and visualizing the data.

How the CSVs were obtained

A write-up on how the data was converted from .dat to .csv files is available on Medium.com. Data was downloaded from the MIT-BIH Arrhythmia Database then converted to CSV.

Citations

Moody GB, Mark RG. The impact of the MIT-BIH Arrhythmia Database. IEEE Eng in Med and Biol 20(3):45-50 (May-June 2001). (PMID: 11446209)

Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.

Electrocardiography Electrocardiography data obtained in diabetic and age-matched control rats using Bioamp (ADInstruments, Dunedin, New Zealand), 3 weeks after induction of diabetes.-------------Echocardiography Transthoracic echocardiography was performed in diabetic and age-matched control rats using VEVO 2100 (Visual Sonics, Toronto, ON, Canada) equipped with a 40-MHz transducer, 2 weeks after diabetes induction.-------------Myocyte shorteningCardiac myocytes were placed in a perfusion chamber mounted on the stage of an inverted microscope (Eclipse TS100; Nikon, Tokyo, Japan) equipped with an analog camera (Myocam; IonOptix, Milton, MA, USA). Only rod-shaped myocytes with clear edges and without spontaneous contractions were selected for analysis. Cells were maintained at 37°C and field stimulated (MyoPacer; IonOptix) with 5 ms bipolar pulses at 1 Hz frequency using platinum electrodes placed on the opposite sides of the chamber. Contraction signals of load-free myocytes were recorded with the help of a commercial software (IonWizard; IonOptix) and the sarcomere striation pattern used to calculate changes in sarcomere spacing using a fast Fourier transform algorithm (Sarclen Algorithm; IonOptix). The following parameters were obtained: resting sarcomere length; amplitude of shortening (expressed as % of resting sarcomere length); shortening and relaxation velocities and time intervals to reach the peak of contraction (TPC); maximal shortening velocity (TMS); maximal relaxation velocity (TMR) and 50% resting sarcomere length (THALF). The parameters were calculated averaging at least 5 consecutive contractions.-------------HistologyFor histological data, prepared sections were observed under microscopy (Leica Application suite). Magnification was set up at ×400. Micrographs were used to calculate the collagen content in the myocardial interstitium using the Image J software as the percentage of red-stained area in the section. An average of 15-20 images from each animal from each group was analyzed. To evaluate the perivascular fibrosis of intramyocardial arterioles of the ventricles, arterioles with a diameter between 100-200 μm were chosen. For each image of the arteriole, a region of interest was delimited around it to exclude areas of interstitial collagen which were not related to the arteriole. Perivascular fibrosis was determined by the ratio between the area occupied by collagen stained with picro-sirius within the region of interest and the area of ​​the lumen. The software Image J was used to calculate the area occupied by the collagen and to mark the regions of interest, and the software Zen (Zeiss, Germany) to calculate the area of ​​the lumen. The images were obtained with a 20x magnifying objective (Eclipse TE 300, Nikon, USA) with a digital camera (AxioCam, Zeiss, Germany).

About this dataset

Context

The dataset tabulates the median household income in Echo. It can be utilized to understand the trend in median household income and to analyze the income distribution in Echo by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Echo, OR Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Echo, OR: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Echo, OR
• Echo, OR households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Echo median household income. You can refer the same here

Echo’s Mobility Data package includes attributes that allow it to map the activity around more than 58M+ Points-of-Interest. Visits & visitors are matched to physical locations, enabling companies to gain an in-depth understanding of: - New movement trends - Popular locations - The customers’ journey - Frequency of visits & repeat visitors - And more…

Thanks to these insights, it is possible to: - Assess an area’s growth potential by evaluating its’ Foot Traffic - Identify cross-visitation trends - Evaluate customer loyalty to a specific brand - The length of the buying journey

We run monthly or quarterly maintenance and updates on our existing database to ensure ongoing data accuracy and precision. This data is Non-PII and GDPR- compliant.

It is possible to request Activity Analyses to get further contextualisation of the mobility around a POI. Ask one of our data experts for our: - Cross Visitation Analysis - Customer Journey Analysis

This record contains raw data related to the article “Comparison between Automatic and Semiautomatic System for the 3D Echocardiographic Multiparametric Evaluation of RV Function and Dimension”

Background: The right ventricle (RV) plays a pivotal role in cardiovascular diseases and 3-dimensional echocardiography (3DE) has gained acceptance for the evaluation of RV volumes and function. Recently, a new artificial intelligence (AI)–based automated 3DE software for RV evaluation has been proposed and validated against cardiac magnetic resonance. The aims of this study were three-fold: (i) feasibility of the AI-based 3DE RV quantification, (ii) comparison with the semi-automatic 3DE method and (iii) assessment of 2-dimensional echocardiography (2DE) and strain measurements obtained automatically. Methods: A total of 203 subject (122 normal and 81 patients) underwent a 2DE and both the semi-automatic and automatic 3DE methods for Doppler standard, RV volumes and ejection fraction (RVEF) measurements. Results: The automatic 3DE method was highly feasible, faster than 2DE and semi-automatic 3DE and data obtained were comparable with traditional measurements. Both in normal subjects and patients, the RVEF was similar to the two 3DE methods and 2DE and strain measurements obtained by the automated system correlated very well with the standard 2DE and strain ones. Conclusions: results showed that rapid analysis and excellent reproducibility of AI-based 3DE RV analysis supported the routine adoption of this automated method in the daily clinical workflow.

About this dataset

Context

The dataset tabulates the Echo household income by gender. The dataset can be utilized to understand the gender-based income distribution of Echo income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Echo, OR annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Echo, OR annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Echo income distribution by gender. You can refer the same here