An electrocardiogram-based AI algorithm for early detection of pulmonary hypertension
Open accessAbstract
Background: Early diagnosis of pulmonary hypertension (PH) is critical for effective treatment and management. We aimed to develop and externally validate an artificial intelligence algorithm that could serve as a PH screening tool, based on analysis of a standard 12-lead ECG. Methods: The PH Early Detection Algorithm (PH-EDA) is a convolutional neural network developed using retrospective ECG voltage–time data, with patients classified as “PH-likely” or “PH-unlikely” (controls) based on right heart catheterisation or echocardiography. In total, 39 823 PH-likely patients and 219 404 control patients from Mayo Clinic were randomly split into training (48%), validation (12%) and test (40%) sets. ECGs taken within 1 month of PH diagnosis (diagnostic dataset) were used to train the PH-EDA at Mayo Clinic. Performance was tested on diagnostic ECGs within the test sets from Mayo Clinic (n=16 175/87 998 PH-likely/controls) and Vanderbilt University Medical Center (VUMC; n=6045/24 256 PH-likely/controls). In addition, performance was tested on ECGs taken 6–18 months (pre-emptive dataset), and up to 5 years prior to a PH diagnosis at both sites. Results: Performance testing yielded an area under the receiver operating characteristic curve (AUC) of 0.92 and 0.88 in the diagnostic test sets at Mayo Clinic and VUMC, respectively, and 0.86 and 0.81, respectively, in the pre-emptive test sets. The AUC remained a minimum of 0.79 at Mayo Clinic and 0.73 at VUMC up to 5 years before diagnosis. Conclusion: The PH-EDA can detect PH at diagnosis and 6–18 months prior, demonstrating the potential to accelerate diagnosis and management of this debilitating disease.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Hypertrophic cardiomyopathy, cardiac amyloidosis, and pulmonary hypertension AI-ECG applications remain earlier or pathway-incomplete.
“hypertrophic cardiomyopathy, cardiac amyloidosis, and pulmonary hypertension remain earlier or pathway-incomplete applications.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [32]
The Methods describe the pulmonary-hypertension AI-ECG algorithm as investigational, and the Discussion says future studies will assess outcomes for patients it identifies. That supports describing the pulmonary-hypertension application as early or pathway-incomplete. The paper does not establish the same status for hypertrophic cardiomyopathy or cardiac amyloidosis; its Introduction and References mention prior hypertrophic-cardiomyopathy work but do not make that assessment (no page numbers provided). (LLM, conf=0.86)
Experimental — accuracy not yet measuredEvidence for AI-ECG detection of pulmonary hypertension and right ventricular dysfunction remains comparatively early and predominantly retrospective, although reported discrimination has been encouraging with AUROCs of approximately 0.86-0.92 across internal and external validation cohorts.
“Evidence for AI-ECG detection of PH and right ventricular dysfunction remains comparatively early. Published studies are still predominantly retrospective, although reported discrimination has been encouraging, with AUROCs of approximately 0.86–0.92 across internal and external validation cohorts.31–33”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [32]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredEarlier identification of PH may allow more timely echocardiography, specialist referral, and confirmatory hemodynamic testing when appropriate.
“This finding is clinically important because delayed recognition of PH is common, and earlier identification may allow more timely echocardiography, specialist referral, and confirmatory hemodynamic testing when appropriate [37].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [37]
The Discussion (page unavailable) says earlier suspicion of PH could prompt echocardiography and further diagnostic evaluation, potentially accelerating diagnosis. The Introduction (page unavailable) identifies right-heart catheterisation as the definitive diagnostic test. The paper does not specifically discuss specialist referral, so that part of the attributed pathway is unsupported. (LLM, conf=0.84)
Experimental — accuracy not yet measuredPH-EDA achieved strong discrimination at the time of PH diagnosis.
“PH-EDA achieved strong discrimination at the time of PH diagnosis and also demonstrated predictive ability when applied to ECGs obtained months to years before clinical diagnosis.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [37]
PH-EDA demonstrated predictive ability when applied to ECGs obtained months to years before clinical diagnosis.
“PH-EDA achieved strong discrimination at the time of PH diagnosis and also demonstrated predictive ability when applied to ECGs obtained months to years before clinical diagnosis.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [37]
The pulmonary hypertension literature shows consistent discrimination across multiple cohorts and suggests that AI-ECG may identify disease before formal diagnosis.
“Overall, the pulmonary hypertension literature shows consistent discrimination across multiple cohorts and suggests that AI-ECG may identify disease before formal diagnosis.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [37]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
2015 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension
Galiè, N., Humbert, M., Vachiery, J.-L., Gibbs, S., Lang, I., Torbicki, A., Simonneau, G., Peacock, A., Vonk Noordegraaf, A., Beghetti, M., et al. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehv317 · Cited by 6,931 · see citing works
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759 · see citing works
2022 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension
Humbert, M. J. C., Kovács, G., Hoeper, M. M., Badagliacca, R., Berger, R. M. F., Brida, M., Carlsen, J., Coats, A. J. S., Subías, P. E., Ferrari, P., et al. (2022) · European Heart Journal · doi:10.1093/eurheartj/ehac237 · Cited by 4,169 · see citing works
2015 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension
Galiè, N., Humbert, M. J. C., Vachiéry, J., Gibbs, S., Lang, I. M., Torbicki, A., Simonneau, G., Peacock, A. J., Noordegraaf, A. V., Beghetti, M., et al. (2015) · European Respiratory Journal · doi:10.1183/13993003.01032-2015 · Cited by 3,156 · see citing works
2022 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension
Humbert, M. J. C., Kovács, G., Hoeper, M. M., Badagliacca, R., Berger, R. M. F., Brida, M., Carlsen, J., Coats, A. J. S., Subías, P. E., Ferrari, P., et al. (2022) · European Respiratory Journal · doi:10.1183/13993003.00879-2022 · Cited by 2,331 · see citing works
Attia, Z. I., Noseworthy, P. A., Lopez-Jimenez, F., Asirvatham, S. J., Deshmukh, A. J., Gersh, B. J., Carter, R. E., Yao, X., Rabinstein, A. A., Erickson, B. J., et al. (2019) · The Lancet · doi:10.1016/s0140-6736(19)31721-0 · Cited by 1,613 · see citing works
Self-citationAttia, Z. I., Kapa, S., Lopez-Jimenez, F., McKie, P. M., Ladewig, D. J., Satam, G., Pellikka, P. A., Enriquez-Sarano, M., Noseworthy, P. A., Munger, T. M., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0240-2 · Cited by 1,389 · see citing works
Self-citationA global view of pulmonary hypertension
Hoeper, M. M., Humbert, M. J. C., Souza, R., Idrees, M. M., Kawut, S. M., Sliwa, K. S., Jing, Z., & Gibbs, J. S. R. (2016) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(15)00543-3 · Cited by 1,053 · see citing works
Noordegraaf, A. V., Chin, K., Haddad, F., Hassoun, P. M., Hemnes, A. R., Hopkins, S. R., Kawut, S. M., Langleben, D., Lumens, J., & Naeije, R. (2018) · European Respiratory Journal · doi:10.1183/13993003.01900-2018 · Cited by 577 · see citing works
Self-citationDiagnosis of pulmonary hypertension
Frost, A., Badesch, D. B., Gibbs, J. S. R., Gopalan, D., Khanna, D., Manes, A., Oudiz, R. J., Satoh, T., Torres, F., & Torbicki, A. (2018) · European Respiratory Journal · doi:10.1183/13993003.01904-2018 · Cited by 482 · see citing works
Ko, W.-Y., Siontis, K. C., Attia, Z. I., Carter, R. E., Kapa, S., Ommen, S. R., Demuth, S. J., Ackerman, M. J., Gersh, B. J., Arruda-Olson, A. M., et al. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2019.12.030 · Cited by 404 · see citing works
Self-citationDelay in Recognition of Pulmonary Arterial Hypertension
Brown, L. M., Chen, H., Halpern, S. D., Taichman, D. B., McGoon, M. D., Farber, H. W., Frost, A., Liou, T. G., Turner, M. P., Feldkircher, K., et al. (2011) · CHEST Journal · doi:10.1378/chest.10-1166 · Cited by 288 · see citing works
REVEAL: a contemporary US pulmonary arterial hypertension registry
McGoon, M. D., & Miller, D. P. (2012) · European Respiratory Review · doi:10.1183/09059180.00008211 · Cited by 272 · see citing works
Noseworthy, P. A., Attia, Z. I., Brewer, L. C., Hayes, S. N., Yao, X., Kapa, S., Friedman, P. A., & Lopez-Jimenez, F. (2020) · Circulation: Arrhythmia and Electrophysiology · doi:10.1161/circep.119.007988 · Cited by 220 · see citing works
Self-citationArtificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology
Feeny, A. K., Chung, M. K., Madabhushi, A., Attia, Z. I., Čikeš, M., Firouznia, M., Friedman, P. A., Kalscheur, M. M., Kapa, S., Narayan, S. M., et al. (2020) · Circulation Arrhythmia and Electrophysiology · doi:10.1161/circep.119.007952 · Cited by 210 · see citing works
Self-citationStrange, G. A., Gabbay, E., Kermeen, F. D., Williams, T. J., Carrington, M. J., Stewart, S., & KEOGH, A. M. (2013) · Pulmonary Circulation · doi:10.4103/2045-8932.109919 · Cited by 144 · see citing works
Klok, F. A., Barco, S., Konstantinides, S., Dartevelle, P., Fadel, É., Jenkins, D. P., Kim, N. H., Madani, M. M., Matsubara, H., Mayer, E., et al. (2018) · European Respiratory Journal · doi:10.1183/13993003.01687-2018 · Cited by 112 · see citing works
Makimoto, H., Höckmann, M., Lin, T., Gloeckner, D., Gerguri, S., Clasen, L., Schmidt, J., Assadi-Schmidt, A., Bejinariu, A., Müller, P., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-65105-x · Cited by 108 · see citing works
Artificial intelligence for early prediction of pulmonary hypertension using electrocardiography
Kwon, J.-M., Kim, K.-H., Medina-Inojosa, J., Jeon, K.-H., Park, J., & Oh, B.-H. (2020) · The Journal of Heart and Lung Transplantation · doi:10.1016/j.healun.2020.04.009 · Cited by 108 · see citing works
A machine learning approach for the prediction of pulmonary hypertension
Leha, A., Hellenkamp, K., Unsöld, B. W., Mushemi-Blake, S., Shah, A. M., Hasenfuss, G. P., & Seidler, T. (2019) · PLoS ONE · doi:10.1371/journal.pone.0224453 · Cited by 95 · see citing works
Swift, A. J., Lu, H., Uthoff, J., Garg, P., Cogliano, M., Taylor, J. C., Metherall, P., Zhou, S., Johns, C., Alabed, S., et al. (2020) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jeaa001 · Cited by 89 · see citing works
Khou, V., Anderson, J., Strange, G. A., Corrigan, C., Collins, N. J., Celermajer, D. S., Dwyer, N., Feenstra, J., Horrigan, M., Keating, D., et al. (2020) · Respirology · doi:10.1111/resp.13768 · Cited by 89 · see citing works
The patient experience of pulmonary hypertension: a large cross-sectional study of UK patients
Armstrong, I. J., Billings, C., Kiely, D. G., Yorke, J., Harries, C., Clayton, S., & Gin‐Sing, W. (2019) · BMC Pulmonary Medicine · doi:10.1186/s12890-019-0827-5 · Cited by 80 · see citing works
Kusunose, K., Hirata, Y., Tsuji, T., Kotoku, J., & Sata, M. (2020) · Scientific Reports · doi:10.1038/s41598-020-76359-w · Cited by 67 · see citing works
Pulmonary Hypertension: Types and Treatments
Rose‐Jones, L. J., & McLaughlin, V. V. (2014) · Current Cardiology Reviews · doi:10.2174/1573403x09666131117164122 · Cited by 64 · see citing works
Kiely, D. G., Doyle, O., Drage, E., Jenner, H., Salvatelli, V., Daniels, F. A., Rigg, J., Schmitt, C. J., Samyshkin, Y., Lawrie, A., et al. (2019) · Pulmonary Circulation · doi:10.1177/2045894019890549 · Cited by 62 · see citing works
Reed, M. J., Grubb, N. R., Lang, C. C., O'Brien, R., Simpson, K., Padarenga, M., Grant, A., Tuck, S., Keating, L., Coffey, F., et al. (2019) · EClinicalMedicine · doi:10.1016/j.eclinm.2019.02.005 · Cited by 57 · see citing works
Braganza, M., Shaw, J., Solverson, K., Vis, D. C., Janovcik, J., Varughese, R. A., Thakrar, M. V., Hirani, N., Helmersen, D., & Weatherald, J. C. (2019) · CHEST Journal · doi:10.1016/j.chest.2019.01.035 · Cited by 54 · see citing works
Use of ECG and Other Simple Non-Invasive Tools to Assess Pulmonary Hypertension
Kovács, G., Avian, A., Foris, V., Tscherner, M., Kqiku, X., Douschan, P., Bachmaier, G., Olschewski, A., Matucci‐Cerinic, M., & Olschewski, H. (2016) · PLoS ONE · doi:10.1371/journal.pone.0168706 · Cited by 53 · see citing works
Maron, B. A., & Abman, S. H. (2017) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201604-0882pp · Cited by 49 · see citing works
Electrocardiogram Detection of Pulmonary Hypertension Using Deep Learning
ARAS, M. A., ABREAU, S., MILLS, H., RADHAKRISHNAN, L., KLEIN, L., MANTRI, N., RUBIN, B., BARRIOS, J., CHEHOUD, C., KOGAN, E., et al. (2023) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2022.12.016 · Cited by 46 · see citing works
Self-citationDidden, E., Lee, E., Wyckmans, J., Quinn, D. A., & Perchenet, L. (2023) · Pulmonary Circulation · doi:10.1002/pul2.12188 · Cited by 37 · see citing works
Self-citationElectrocardiogram in patients with pulmonary hypertension
Ley, L., Höltgen, R., Bogossian, H., Ghofrani, H. A., & Bandorski, D. (2023) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2023.02.007 · Cited by 29 · see citing works
Finding Pulmonary Arterial Hypertension—Switching to Offense to Mitigate Disease Burden
Maron, B. A., & Humbert, M. J. C. (2022) · JAMA Cardiology · doi:10.1001/jamacardio.2022.0011 · Cited by 27 · see citing works
de Belen, E., McConnell, J. W., Elwing, J., Paculdo, D. R., Cabaluna, I. T. G., Linder, J., & Peabody, J. W. (2023) · Journal of the American Heart Association · doi:10.1161/jaha.122.026413 · Cited by 13 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Artificial intelligence and the electrocardiogram: A modern renaissance
Palermi, S., Vecchiato, M., Ng, F. S., Attia, Z., Cho, Y., Anselmino, M., De Ferrari, G. M., Saglietto, A., Sau, A., Chiu, I.-M., et al. (2025) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2025.04.036 · Cited by 42 · see citing works
Self-citationA Comprehensive Review of Artificial Intelligence (AI) Applications in Pulmonary Hypertension (PH)
Esfahani, S. A., Ali, N. B., Farina, J., Scalia, I. G., Pereyra, M., Abbas, M. T., Javadi, N., Bismee, N. N., Abdelfattah, F. E., Awad, K., et al. (2025) · Medicina · doi:10.3390/medicina61010085 · Cited by 20 · see citing works
Therapeutic targets for pulmonary arterial hypertension: insights into the emerging landscape
Flores, C. V., & Chan, S. Y. (2025) · Expert Opinion on Therapeutic Targets · doi:10.1080/14728222.2025.2507034 · Cited by 8 · see citing works
Huang, Z., Diao, X., Huo, Y., Zhao, Z., Geng, J., Zhao, Q., Liu, J., Xi, Q., Xia, Y. F., Xu, O., et al. (2025) · CHEST Journal · doi:10.1016/j.chest.2025.06.008 · Cited by 7 · see citing works
Artificial intelligence in pulmonary hypertension: a systematic review
Kramer, T., Krämer, M., Hagist, C., & Spinler, S. (2025) · European journal of medical research · doi:10.1186/s40001-025-03557-5 · Cited by 7 · see citing works
Transforming Pulmonary Arterial Hypertension: Key Milestones and Future Perspectives
Humbert, M. J. C., Zeder, K., Kovács, G., & Weatherald, J. C. (2026) · Circulation · doi:10.1161/circulationaha.126.079274 · Cited by 6 · see citing works
Jain, S., Kaur, A. P., Qadeer, A., Ghosh, V., Thota, S., Banala, M., Lee, J., Yerrapragada, G., Poonguzhali, E., Shariff, M. N., et al. (2025) · Advances in respiratory medicine · doi:10.3390/arm93050047 · Cited by 5 · see citing works
Clinical validation of a machine-learned, point-of-care system to IDENTIFY pulmonary hypertension
McLean, D. S., Rommel, J. J., Steuter, J., Carroll, W. S., Rabbat, M., Rajagopal, S., Srinivasan, V., Kereiakes, D. J., Roberts, M. C., Raval, A. A., et al. (2025) · ERJ Open Research · doi:10.1183/23120541.01287-2024 · Cited by 4 · see citing works
Ley, L., Wiedenroth, C. B., Guth, S., Gold, C., Yogeswaran, A., Ghofrani, H. A., & Bandorski, D. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13247613 · Cited by 3 · see citing works
Precision medicine in pulmonary hypertension: how close are we today?
Maron, B. A. (2025) · Current Opinion in Pulmonary Medicine · doi:10.1097/mcp.0000000000001195 · Cited by 3 · see citing works
Artificial intelligence meets pulmonary hypertension: early detection for a late-presenting disease
Cassady, S. J., & Maron, B. A. (2024) · European Respiratory Journal · doi:10.1183/13993003.01138-2024 · Cited by 3 · see citing works
Zhu, F., Zhang, Y., Liang, G., Nan, J., Li, Y., Han, C., Sun, D., Wang, Z., Zhao, C., Zhou, W., et al. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01705-1 · Cited by 2 · see citing works
Wang, M., Lu, J., Li, X., Xie, H., Liang, J., Luo, J., Deng, X., Pan, G., Ji, B., & Ding, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-28921-7 · Cited by 2 · see citing works
Vidal, M. L. B., Arvanitaki, A., & Diller, G. (2025) · International Journal of Cardiology Congenital Heart Disease · doi:10.1016/j.ijcchd.2025.100622 · Cited by 1 · see citing works
Eshraghi, R., Yazdani, M. S., & Bahrami, A. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1011128 · Cited by 1 · see citing works
Improving trust in AI diagnosis of pulmonary hypertension with patient-specific insight
Fathieh, F., Nemati, N., Burton, T., Gillins, H. R., Shadforth, I., Bridges, C. R., & Ramchandani, S. (2026) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2026.100390 · Cited by 1 · see citing works
Tang, Q., Deng, K., Cui, Y., Liu, H., & Qian, B. (2026) · International Journal of General Medicine · doi:10.2147/ijgm.s610298
Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease
Shaulian, S., Zeltser, R., & Makaryus, A. N. (2026) · Diagnostics · doi:10.3390/diagnostics16172834
Sobolev, A. V., Blinova, E. V., Анатольевна, С. Т., & Drozdov, D. V. (2024) · Medical alphabet · doi:10.33667/2078-5631-2024-30-28-35
Pulmonary hypertension: diagnostic aspects-what is the role of imaging?
Ali, H., & Guha, A. (2025) · Current Opinion in Cardiology · doi:10.1097/hco.0000000000001241
Das, S., & Shabnam, S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-95017-9_34
Electrocardiography: How Artificial Intelligence Can Make a Diagnosis
Saglietto, A., Nissardi, L., & Ferrari, G. M. D. (2025) · Springer eBooks · doi:10.1007/978-3-031-95256-2_5
Artificial intelligence-driven ECG biomarkers for screening of large pericardial effusionPreprint
Soh, M.-S., Lee, Y., Kim, G., Park, M., Lim, H., Shin, J.-H., Cho, Y., & Kim, J. (2025) · medRxiv · doi:10.64898/2025.12.22.25342872
Old criteria, new intelligence: The evolution of ECG in pulmonary hypertension diagnosis
Herrera-Leaño, N., Sahay, S., Benza, R. L., Correa, J. E. B., & Haddad, F. (2026) · Respiratory Medicine · doi:10.1016/j.rmed.2026.108646
Artificial Intelligence-Driven Integration of ECG and Molecular Biomarkers in Pulmonary Embolism
Uzelac, B., & Stankovic, S. D. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27020813
Convolutional Neural Network Applications In Cardiac Imaging
Fernandes, L. L. A., Dantas, J. L. F., Lima, M. G. V. D. M., Donato, G., & de Melo, M. D. T. (2025) · ARQUIVOS BRASILEIROS DE CARDIOLOGIA - IMAGEM CARDIOVASCULAR · doi:10.36660/abcimg.20250058i
As Aplicações das Redes Neurais Convolucionais na Imagem Cardíaca
Fernandes, L. L. A., DANTAS, J. P., Lima, M. G. V. D. M., Donato, G., & de Melo, M. D. T. (2025) · ARQUIVOS BRASILEIROS DE CARDIOLOGIA - IMAGEM CARDIOVASCULAR · doi:10.36660/abcimg.20250058
Social networks are not just for friends: untangling care patterns in pulmonary hypertension
Zeder, K., & Maron, B. A. (2026) · Annals of the American Thoracic Society · doi:10.1093/annalsats/aaoag003
Liu, L., Nie, Z., Ganggayah, M. D., & Krishnasamy, S. (2026) · doi:10.2196/preprints.74956
Artificial Intelligence Applications in Pulmonary Hypertension: Current Status and Future Directions
Kohli, A., del Valle, K. T., & DuBrock, H. M. (2026) · Current Pulmonology Reports · doi:10.1007/s13665-026-00408-4
Self-citationPrice, L., Zeder, K., & Prisco, S. Z. (2026) · Pulmonary Circulation · doi:10.1002/pul2.70275
Xu, F., Lyu, Y., Li, Z. C., Fan, G., Ma, J., Zhang, X., Xu, X. W., Qiao, L., Liang, Y., Qi, Y., et al. (2026) · Pulmonary Circulation · doi:10.1002/pul2.70312
Taigonyrov, A., Rai, H. M., Jamwal, P. K., Pal, A., & Razaque, A. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10657-w
Egom, E. E. A., Mfopa, A., Djoulde, I., Ibrahim, N., Mountchissi, C., Ambe, L. A., Medou, F. M., Moustapha, & Kamgang, R. (2026) · Pulmonary Circulation · doi:10.1002/pul2.70341
Qi, Y., Ma, K., Han, X., Xu, D., Zhang, X., & Xiang, M. (2026) · Bioengineering · doi:10.3390/bioengineering13070736
Ferrantelli, S., Cuore, A. D., Cassataro, G., Dell’Ajra, L., Norrito, R. L., Geraci, G., Carmina, G., Minà, C., Polizzi, V., Ciancio, N., et al. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27177763
Artificial Intelligence in Pulmonary Hypertension: Current State and Future Prospects
Li, Z., Xu, X., Li, J., 方瑀紳, He, Y., Tang, J., Yang, Z., Xie, H., & Yuan, H. (2026) · Reviews in Cardiovascular Medicine · doi:10.31083/rcm48114
Nazareth, K. R., Jamileh, E., Muhammad, F., Farooq, O., Sequeira, A., Awais, A., & Antoun, I. (2026) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztag143
- Authors
- DuBrock, H. M., Wagner, T. E., Carlson, K., Carpenter, C. L., Awasthi, S., Attia, Z. I., Frantz, R. P., Friedman, P. A., Kapa, S., Annis, J., Brittain, E. L., Hemnes, A. R., Asirvatham, S. J., Babu, M., Prasad, A., Yoo, U., Barve, R., Selej, M., Agron, P., Kogan, E., Quinn, D., Dunnmon, P., Khan, N., & Soundararajan, V.
- Affiliations
- Mayo Clinic (US)
- Nference (United States) (US)
- Vanderbilt University (US)
- Vanderbilt University Medical Center (US)
- Nference (India) (IN)
- Janssen Research & Development (United States) (US)
- Corresponding author
- DuBrock, Hilary M.
- ORCID
- Venue
- European Respiratory Journal
- Publisher
- European Respiratory Society (ERS)
- Published
- 2024-06-27
- Volume
- 64
- Issue
- 1
- Pages
- 2400192
- ISSN
- 0903-1936 (ISSN-L)
- 1399-3003
- PMC
- PMC11269769
- OpenAlex
- W4400083696
- Cited by
- 38
- References
- 36
- Citations by year
- 2026: 18 · 2025: 17 · 2024: 3
- FWCI
- 11.38
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- European Respiratory Journal (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- nference, Inc
- Janssen Research and Development
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Pulmonary Hypertension Research and Treatments
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- pulmonary hypertension, early detection, electrocardiogram, artificial intelligence, convolutional neural networks, right heart catheterization, echocardiography, external validation
- MeSH terms
- Adult, Aged, Algorithms, Artificial Intelligence, Echocardiography, Electrocardiography, Female, Cardiac Catheterization, Humans, Hypertension, Pulmonary, Male, Middle Aged, Retrospective Studies, ROC Curve, Neural Networks, Computer, Early Diagnosis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
