The papers came first.
InVision's products are not inspired by the literature — they are the literature, cleared. Every model in the platform was developed, peer-reviewed, and published before it became a product, and several were released as open datasets and open weights so other groups could test them without our permission.
Every paper, year by year
56 echocardiography publications in total. The 16 below are the ones the platform is built on — including three in Nature, and one independent evaluation run by a group outside InVision. The full list follows.
EchoNet-Dynamic · Nature
The first video-based deep learning model for beat-to-beat ejection fraction. The foundation the platform is built on.
Multi-task echo interpretation · npj Digital Medicine
Identifies cardiac structures, estimates volumetric measurements and function, and predicts systemic phenotypes — with the first interpretation framework applied to echo deep learning.
EchoNet-Labs · EBioMedicine
Laboratory values predicted directly from echo video, showing the study carries signal well beyond the measurements written into the report.
EchoNet-LVH · JAMA Cardiology
Development of the model now cleared as InVision Precision Cardiac Amyloid. 23,745 patients; amyloidosis AUC 0.83, HCM AUC 0.98.
EchoNet-RCT · Nature
The first blinded, randomized clinical trial of AI in cardiology. 16.8% vs 27.2% substantial change; NCT05140642.
EchoNet-MR · Circulation
Mitral regurgitation severity assessed directly from color Doppler video across 58,614 studies, with external validation at a geographically distinct health system.
Cohort construction · JACC: Advances
Model AUC moved from 0.660 to 0.898 on cohort construction alone. The reason cross-study AUC comparisons are not valid.
EchoCLIP · Nature Medicine
Vision-language pretraining on echo studies and their reports.
Occult atrial fibrillation · npj Digital Medicine
A two-stage video model that flags patients in sinus rhythm who had atrial fibrillation within 90 days — disease the study itself does not show.
Independent head-to-head · JACC: Advances
Northwestern-led head-to-head, run without InVision's involvement, including a fairness audit. We publish the whole table.
Precision Cardiac Amyloid · JACC: Advances
Five sites across the US and Japan. AUC 0.893; PPV 0.954. Site-level results published, including the lowest.
Tricuspid regurgitation · JAMA Cardiology
TR severity from color Doppler video. Moderate-or-severe AUC 0.928; severe AUC 0.956.
EchoNet-Measurements · Journal of the American College of Cardiology
877,983 annotations across 155,215 studies. 18 parameters at R² 0.967–0.987.
EchoNet-Liver · NEJM AI
Cirrhosis and steatotic liver disease from the subcostal view already captured in a routine echo. 1.5M+ videos.
Cardiac age · npj Digital Medicine
Predicted cardiac age from 2,610,266 videos across 166,508 studies, mean absolute error 6.76 years — a marker for cardiovascular disease independent of chronological age.
EchoPrime · Nature
12M+ video-report pairs, state of the art on 23 benchmarks, validated across five health systems.
Research threads
The work runs along several parallel lines. Each product traces back to one of them, and the methods work underneath applies to all of them.
Cardiac function
EchoNet-Dynamic · Multi-task echo interpretation
Cardiomyopathy & amyloidosis
EchoNet-LVH · Cohort construction · Independent head-to-head
Clinical trial
EchoNet-RCT
Foundation models
EchoCLIP · EchoPrime
Measurement & quality
EchoNet-Measurements
Opportunistic detection
EchoNet-Labs · Occult atrial fibrillation · EchoNet-Liver
Valvular & pericardial
EchoNet-MR · Tricuspid regurgitation
All publications
Every echocardiography publication, newest first — journal articles, conference papers, abstracts, datasets, and preprints. Entries with a resolvable link are linked; the rest are listed in full so the citation can be looked up.
| Year | Journal | Paper | Type |
|---|---|---|---|
| 2026 | medRxiv | Artificial intelligence-enabled echocardiographic assessment of right ventricular function Right ventricular function · M Tokodi, B He, Á Szijártó, A Ferencz, K Shiida, M Tolvaj, A Fábián, ... | Preprint |
| 2026 | Pediatric Cardiology | Predicting cardiac magnetic resonance-derived ejection fraction from echocardiogram via deep learning approach in Tetralogy of Fallot Echo-to-CMR ejection fraction · A Adhikari, GV Wesley III, MB Nguyen, TT Doan, MY Rao, A Parthiban, ... | Journal |
| 2026 | Nature | Comprehensive echocardiogram evaluation with view primed vision language AI ↗ EchoPrime · M Vukadinovic, IM Chiu, X Tang, N Yuan, TY Chen, P Cheng, D Li, ... | Journal |
| 2026 | Journal of the American Society of Echocardiography | A Clinically Interpretable AI System for Real-Time Quality Control of Transthoracic Echocardiography: Development, Validation, and Deployment Real-time quality control · Z Shi, H Cheng, Z Qi, C Shan, CC Taub, D Ouyang, C Lee, R Chen, Y Du, ... | Journal |
| 2026 | JACC: Advances | Detection of left ventricular outflow obstruction from standard B-mode echocardiogram videos using deep learning LV outflow obstruction · V Yuan, H Ieki, C Binder, Y Sahashi, PC Cheng, D Ouyang | Journal |
| 2025 | npj Digital Medicine | Artificial intelligence prediction of age from echocardiography as a marker for cardiovascular disease ↗ Cardiac age · M Rawlani, H Ieki, C Binder, V Yuan, IM Chiu, A Bhatt, JE Ebinger, ... | Journal |
| 2025 | medRxiv | Automated deep learning pipeline for characterizing left ventricular diastolic function Diastolic function · V Yuan, Y Sahashi, H Ieki, M Vukadinovic, C Binder, K Pieszko, ... | Preprint |
| 2025 | medRxiv | Comprehensive aortic stenosis characterization using multi-view deep learning Aortic stenosis characterisation · H Ieki, Y Sahashi, M Vukadinovic, M Rawlani, C Binder, N Yuan, ... | Preprint |
| 2025 | medRxiv | Automated aortic regurgitation detection and quantification: A deep learning approach using multi-view echocardiography ↗ Aortic regurgitation · C Binder, Y Sahashi, H Ieki, M Vukadinovic, V Yuan, M Rawlani, P Cheng, ... | Preprint |
| 2025 | PhysioNet | Mimic-iv-echo-ext-mimicechoqa: A benchmark dataset for echocardiogram-based visual question answering ↗ Echo question answering · R Thapa, A Li, Q Wu, B He, Y Sahashi, C Binder-Rodriguez, A Zhang, ... | Dataset |
| 2025 | Open Heart | Ratio of interventricular septal thickness to global longitudinal strain accurately identifies cardiac amyloidosis Septal thickness to strain ratio · L Cao, GJ Hong, M Abiragi, J Le, PR Tacon, IM Chiu, J Patel, LK Stern, ... | Journal |
| 2025 | NEJM AI | Opportunistic screening of chronic liver disease with deep-learning–enhanced echocardiography ↗ EchoNet-Liver · Y Sahashi, M Vukadinovic, F Amrollahi, H Trivedi, J Rhee, J Chen, ... | Journal |
| 2025 | Journal of the American Society of Echocardiography | Using deep learning to predict cardiovascular magnetic resonance findings from echocardiographic videos Echo-to-CMR prediction · Y Sahashi, M Vukadinovic, G Duffy, D Li, S Cheng, DS Berman, D Ouyang, ... | Journal |
| 2025 | Journal of the American Heart Association | Understanding transient left ventricular ejection fraction reduction during atrial fibrillation with artificial intelligence ↗ EF during atrial fibrillation · N Yuan, GJ Hong, A Vrudhula, AC Kwan, G Duffy, P Botting, SS Dhruva, ... | Journal |
| 2025 | Journal of the American College of Cardiology | Artificial intelligence automation of echocardiographic measurements ↗ EchoNet-Measurements · Y Sahashi, H Ieki, V Yuan, M Christensen, M Vukadinovic, ... | Journal |
| 2025 | Journal of Cardiology | AI-echocardiography: Current status and future direction Field review · Y Sahashi, D Ouyang, H Okura, N Kagiyama | Journal |
| 2025 | JAMA Cardiology | Automated deep learning phenotyping of tricuspid regurgitation in echocardiography ↗ Tricuspid regurgitation · A Vrudhula, M Vukadinovic, C Haeffele, AC Kwan, D Berman, D Liang, ... | Journal |
| 2025 | JACC: Advances | International validation of echocardiographic artificial intelligence amyloid detection algorithm ↗ Precision Cardiac Amyloid · G Duffy, EK Oikonomou, N Easton, H Usuku, J Patel, Y Katsumata, ... | Journal |
| 2025 | JACC: Advances | Evaluating the performance and potential bias of predictive models for detection of transthyretin cardiac amyloidosis ↗ Independent head-to-head · J Hourmozdi, N Easton, S Benigeri, JD Thomas, A Narang, D Ouyang, ... | Journal |
| 2025 | IEEE/CVF ICCV Workshops | EchoNet-Quality: Denoising Echocardiograms via Deep Generative Modeling of Ultrasound Noise ↗ EchoNet-Quality · D Choi, M Vukadinovic, B He, C Binder, Y Sahashi, D Ouyang | Conference |
| 2025 | European Heart Journal — Digital Health | Automated evaluation for pericardial effusion and cardiac tamponade with echocardiographic artificial intelligence ↗ EchoNet-Pericardium · IM Chiu, Y Sahashi, M Vukadinovic, PP Cheng, S Cheng, D Ouyang | Journal |
| 2025 | Current Cardiology Reports | Current State of Artificial Intelligence in Assessing Cardiac Function Field review · V Yuan, K Lee, AP Ambrosy, D Ouyang, H Ieki | Journal |
| 2024 | npj Digital Medicine | Deep learning evaluation of echocardiograms to identify occult atrial fibrillation ↗ Occult atrial fibrillation · N Yuan, NR Stein, G Duffy, RK Sandhu, SS Chugh, PS Chen, ... | Journal |
| 2024 | The Journal of Emergency Medicine | AI-enabled assessment of cardiac function and video quality in emergency department point- of-care echocardiograms Point-of-care ultrasound · B He, D Dash, Y Duanmu, TX Tan, D Ouyang, J Zou | Journal |
| 2024 | Scientific Reports | Deep learning for transesophageal echocardiography view classification ↗ TEE view classification · KR Steffner, M Christensen, G Gill, M Bowdish, J Rhee, A Kumaresan, ... | Journal |
| 2024 | Pacific Symposium on Biocomputing | Leveraging 3D echocardiograms to evaluate AI model performance in predicting cardiac function on Out-of-Distribution data 3D model evaluation · G Duffy, K Christensen, D Ouyang | Conference |
| 2024 | Open Heart | Detection of cardiac amyloidosis using machine learning on routine echocardiographic measurements Amyloidosis from measurements · RSW Chang, I Chiu, P Tacon, M Abiragi, L Cao, G Hong, J Le, J Zou, ... | Journal |
| 2024 | Nature Medicine | Vision–language foundation model for echocardiogram interpretation ↗ EchoCLIP · M Christensen, M Vukadinovic, N Yuan, D Ouyang | Journal |
| 2024 | Journal of the American College of Cardiology | Random Forest Machine Learning to Detect Cardiac Amyloidosis Amyloidosis random forest · RSW Chang, PR Tacon, M Abiragi, L Cao, G Hong, J Le, P Ricchiuto, ... | Abstract |
| 2024 | Journal of the American College of Cardiology | DEEP LEARNING FOR HIGH THROUGHPUT AUTOMATED CALCULATION OF LVEF AND GLS Automated LVEF and GLS · G Hong, PR Tacon, J Le, L Cao, M Abiragi, RSW Chang, IM Chiu, ... | Abstract |
| 2024 | JAMA Cardiology | A multimodal video-based AI biomarker for aortic stenosis development and progression ↗ Aortic stenosis progression · EK Oikonomou, G Holste, N Yuan, A Coppi, RL McNamara, NA Haynes, ... | Journal |
| 2024 | JACC: Cardiovascular Imaging | Deep learning-derived myocardial strain ↗ Myocardial strain · AC Kwan, EW Chang, I Jain, J Theurer, X Tang, N Francisco, F Haddad, ... | Journal |
| 2024 | JACC: Advances | Navigating the gray zone: AI decision support to identify aortic stenosis severity ↗ Aortic stenosis severity · A Sarraju, D Ouyang | Journal |
| 2024 | JACC: Advances | Impact of case and control selection on training artificial intelligence screening of cardiac amyloidosis ↗ Cohort construction · A Vrudhula, L Stern, PC Cheng, P Ricchiuto, C Daluwatte, R Witteles, ... | Journal |
| 2024 | European Heart Journal — Digital Health | Clinical and genetic associations of asymmetric apical and septal left ventricular hypertrophy LVH genetic associations · V Yuan, M Vukadinovic, AC Kwan, F Rader, D Li, D Ouyang | Journal |
| 2024 | Circulation | External Validation of EchoNet-LVH, a Deep Learning Model for Cardiac Amyloidosis, for Association with Cardiomyopathy EchoNet-LVH external validation · PMM Castellote, G Duffy, W Zhou, S Cheng, J Chen, N Viney, S Tsimikas, ... | Abstract |
| 2024 | Circulation | High-throughput deep learning detection of mitral regurgitation ↗ EchoNet-MR · A Vrudhula, G Duffy, M Vukadinovic, D Liang, S Cheng, D Ouyang | Journal |
| 2024 | Blood | Evaluation of Functional Cardiac Measures and Response to Treatment Initiation in Patients with Systemic Light-Chain (AL) Amyloidosis: Results from a Single Site Retrospective … AL amyloidosis treatment response · J Thompson, J Catini, D Ouyang, IM Chiu, CC Quarta | Abstract |
| 2023 | Pacific Symposium on Biocomputing | Impact of Measurement Noise on Genetic Association Studies of Cardiac Function Measurement noise · M Vukadinovic, G Renjith, V Yuan, A Kwan, SC Cheng, D Li, SL Clarke, ... | Conference |
| 2023 | Nature | Blinded, randomized trial of sonographer versus AI cardiac function assessment ↗ EchoNet-RCT · B He, AC Kwan, JH Cho, N Yuan, C Pollick, T Shiota, J Ebinger, NA Bello, ... | Journal |
| 2023 | Journal of the American Society of Echocardiography | Prediction of coronary artery calcium using deep learning of echocardiograms ↗ Coronary artery calcium · N Yuan, AC Kwan, G Duffy, J Theurer, JH Chen, K Nieman, P Botting, ... | Journal |
| 2023 | Journal of the American Society of Echocardiography | Video-Based Deep Learning for Automated Assessment of Left Ventricular Ejection Fraction in Pediatric Patients ↗ EchoNet-Peds · CD Reddy, L Lopez, D Ouyang, JY Zou, B He | Journal |
| 2023 | European Heart Journal | Severe aortic stenosis detection by deep learning applied to echocardiography ↗ Severe aortic stenosis · G Holste, EK Oikonomou, BJ Mortazavi, A Coppi, KF Faridi, EJ Miller, ... | Journal |
| 2022 | arXiv | Deep learning discovery of demographic biomarkers in echocardiography ↗ Demographic biomarkers · G Duffy, SL Clarke, M Christensen, B He, N Yuan, S Cheng, D Ouyang | Preprint |
| 2022 | JAMA Cardiology | High-throughput precision phenotyping of left ventricular hypertrophy with cardiovascular deep learning ↗ EchoNet-LVH · G Duffy, PP Cheng, N Yuan, B He, AC Kwan, MJ Shun-Shin, ... | Journal |
| 2022 | JACC: Cardiovascular Imaging | Revival and revision of right ventricular assessment by machine learning Right ventricular assessment · D Ouyang, S Cheng | Journal |
| 2022 | European Heart Journal — Digital Health | Multimodal deep learning enhances diagnostic precision in left ventricular hypertrophy ↗ LVH-fusion · JT Soto, J Weston Hughes, PA Sanchez, M Perez, D Ouyang, EA Ashley | Journal |
| 2021 | Pacific Symposium on Biocomputing | Interpretable deep learning prediction of 3d assessment of cardiac function 3D cardiac function · G Duffy, I Jain, B He, D Ouyang | Conference |
| 2021 | JACC: Cardiovascular Imaging | Characterizing mitral regurgitation with precision phenotyping and unsupervised learning Mitral regurgitation phenotyping · D Ouyang, JD Thomas | Journal |
| 2021 | JACC: Cardiovascular Imaging | Systematic quantification of sources of variation in ejection fraction calculation using deep learning EF measurement variability · N Yuan, I Jain, N Rattehalli, B He, C Pollick, D Liang, P Heidenreich, ... | Journal |
| 2021 | EBioMedicine | Deep learning evaluation of biomarkers from echocardiogram videos ↗ EchoNet-Labs · JW Hughes, N Yuan, B He, J Ouyang, J Ebinger, P Botting, J Lee, ... | Journal |
| 2021 | Circulation | Video-based deep learning model for automated assessment of ejection fraction in pediatric patients EchoNet-Peds · B He, D Ouyang, L Lopez, J Zou, CD Reddy | Abstract |
| 2020 | npj Digital Medicine | Deep learning interpretation of echocardiograms ↗ Multi-task echo interpretation · A Ghorbani, D Ouyang, A Abid, B He, JH Chen, RA Harrington, DH Liang, ... | Journal |
| 2020 | Nature | Video-based AI for beat-to-beat assessment of cardiac function ↗ EchoNet-Dynamic · D Ouyang, B He, A Ghorbani, N Yuan, J Ebinger, CP Langlotz, ... | Journal |
| 2020 | Journal of the American College of Cardiology | A deep learning algorithm accurately detects pericardial effusion on echocardiography Pericardial effusion · A Nayak, D Ouyang, EA Ashley | Abstract |
| 2019 | NeurIPS ML4H Workshop | Echonet-dynamic: a large new cardiac motion video data resource for medical machine learning EchoNet-Dynamic (dataset) · D Ouyang, B He, A Ghorbani, MP Lungren, EA Ashley, DH Liang, JY Zou | Conference |
Open science
The model family behind these products is published and reproducible, and the underlying annotated datasets were released publicly — including 23,212 annotated echocardiogram videos from the JAMA Cardiology 2022 study.
That has a consequence most vendors avoid: independent groups can evaluate these models without asking us, and they have. A Northwestern-led team ran a head-to-head against another FDA-cleared device and published a fairness audit alongside it. We host that study, in full, including the metrics where we score lower — see the amyloid evidence page.
A note on names. EchoNet is the research lab, not a single model. Each paper carries its own name — EchoNet-Dynamic for ejection fraction, EchoNet-LVH for hypertrophy and amyloidosis, EchoNet-Labs for laboratory values, EchoNet-MR for mitral regurgitation, EchoNet-Measurements, EchoNet-Liver, EchoNet-Quality, alongside EchoCLIP and EchoPrime. The FDA clearances use product names for the same models: EchoNet-Dynamic is InVision Precision LVEF, and EchoNet-LVH is InVision Precision Cardiac Amyloid.
How to read an AUC
The same model reports different AUCs across these papers. That is not instability — it is cohort construction, and we published the paper that demonstrates it.
In JACC: Advances (2024), model AUCs ranged from 0.660 to 0.898 on matched held-out test sets and 0.467 to 0.898 in a general patient population — varying by nothing except how cases and controls were selected. An AUC is a property of a model and a cohort. Comparing one study's AUC against another's is not a valid comparison.
Questions about the research?
Our clinical team includes authors on most of these papers. They will take any of it as deep as you want to go.