The DOI matches its Crossref record; 34 of the 35 references it cites were found in a registry or an index.
Artificial intelligence for early prediction of pulmonary hypertension using electrocardiography
Abstract
Background Screening and early diagnosis of pulmonary hypertension (PH) are critical for managing progression and preventing associated mortality; however, there are no tools for this purpose. We developed and validated an artificial intelligence (AI) algorithm for predicting PH using electrocardiography (ECG). Methods This historical cohort study included data from consecutive patients from 2 hospitals. The patients in one hospital were divided into derivation (56,670 ECGs from 24,202 patients) and internal validation (3,174 ECGs from 3,174 patients) datasets, whereas the patients in the other hospital were included in only an external validation (10,865 ECGs from 10,865 patients) dataset. An AI algorithm based on an ensemble neural network was developed using 12-lead ECG signal and demographic information from the derivation dataset. The end-point was the diagnosis of PH. In addition, the interpretable AI algorithm identified which region had the most significant effect on decision making using a sensitivity map. Results During the internal and external validation, the area under the receiver operating characteristic curve of the AI algorithm for detecting PH was 0.859 and 0.902, respectively. In the 2,939 individuals without PH at initial echocardiography, those patients that the AI defined as having a higher risk had a significantly higher chance of developing PH than those in the low-risk group (31.5% vs 5.9%, p Conclusions The AI algorithm demonstrated high accuracy for…
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“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 [31]
In the citing paper
“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 [31]
In the citing paper
“Importantly, among patients without known PH at baseline, those classified as high risk by the AI model had a substantially higher subsequent incidence of PH than those classified as low risk [35].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [35]
In the citing paper
“The model was trained using more than 56,000 ECGs and demonstrated strong diagnostic performance for PH in both internal and external validation cohorts, with reported AUC values of 0.859 and 0.902, respectively.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [35]
In the citing paper
“This suggested that AI-ECG may not only identify existing pulmonary hypertension but may also detect early ECG signatures associated with future disease development.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [35]
In the citing paper
“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 [35]
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
TensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,548
Ensemble methods in machine learning
Dietterich, T. G. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45014-9_1 · Cited by 8,408
Rudski, L., Lai, W. W., Afilalo, J., Hua, L., Handschumacher, M. D., Chandrasekaran, K., Solomon, S. D., Louie, E. K., & Schiller, N. B. (2010) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2010.05.010 · Cited by 7,080
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
Saito, T., & Rehmsmeier, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118432 · Cited by 5,309
Guidelines for the diagnosis and treatment of pulmonary hypertension
Galiè, N., Hoeper, M. M., Humbert, M. J. C., Torbicki, A., Vachiéry, J., Barberà, J. A., Beghetti, M., Corris, P. A., Gaine, S., Gibbs, J. S. R., et al. (2009) · European Heart Journal · doi:10.1093/eurheartj/ehp297 · Cited by 3,853
Cited by
Sorted by citations
Deep learning and the electrocardiogram: review of the current state-of-the-art
Somani, S. S., Russak, A. J., Richter, F., Zhao, S., Vaid, A., Chaudhry, F. F., Freitas, J. K. D., Naik, N., Miotto, R., Nadkarni, G. N., et al. (2020) · EP Europace · doi:10.1093/europace/euaa377 · Cited by 276
Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives
Sun, X., Yin, Y., Yang, Q., & Huo, T. (2023) · European journal of medical research · doi:10.1186/s40001-023-01065-y · Cited by 180
Ayano, Y. M., Schwenker, F., Dufera, B. D., & Debelee, T. G. (2022) · Diagnostics · doi:10.3390/diagnostics13010111 · Cited by 139
Yim, D., Khuntia, J., Parameswaran, V., & Meyers, A. D. (2024) · JMIR Medical Informatics · doi:10.2196/52073 · Cited by 140
State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review
Πετμεζάς, Γ., Stefanopoulos, L., Kilintzis, V., Tzavelis, A., Rogers, J. A., Katsaggelos, A. K., & Maglaveras, N. (2022) · JMIR Medical Informatics · doi:10.2196/38454 · Cited by 126
Lee, S., Chu, Y., Ryu, J., Park, Y. J., Yang, S., & Koh, S. B. (2022) · Yonsei Medical Journal · doi:10.3349/ymj.2022.63.s93 · Cited by 88
A review of evaluation approaches for explainable AI with applications in cardiology
Salih, A. M., Galazzo, I. B., Gkontra, P., Rauseo, E., Lee, A. M., Lekadir, K., Radeva, P. I., Petersen, S. E., & Menegaz, G. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10852-w · Cited by 82
Artificial intelligence for detecting electrolyte imbalance using electrocardiography
Kwon, J., Jung, M., Kim, K., Jo, Y., Shin, J. H., Cho, Y., Lee, Y., Ban, J., Jeon, K., Lee, S. Y., et al. (2021) · Annals of Noninvasive Electrocardiology · doi:10.1111/anec.12839 · Cited by 63
Self-citationSystematic Review on Machine-Learning Algorithms Used in Wearable-Based eHealth Data Analysis
Site, A., Nurmi, J., & Lohan, E. S. (2021) · IEEE Access · doi:10.1109/access.2021.3103268 · Cited by 63
Huang, S.-W., Tsai, H., Hung, S.-J., Ko, W., & Wang, J. (2020) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0008960 · Cited by 67
Artificial Intelligence Technologies in Cardiology
Ledziński, Ł., & Grześk, G. (2023) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd10050202 · Cited by 72
Shu, S., Ren, J., & Song, J. (2021) · Circulation Journal · doi:10.1253/circj.cj-20-1121 · Cited by 60
The year in cardiovascular medicine 2021: digital health and innovation
Vardas, P. E., Asselbergs, F. W., van Smeden, M., & Friedman, P. A. (2021) · European Heart Journal · doi:10.1093/eurheartj/ehab874 · Cited by 52
Liu, C.-M., Shih, E. S. C., Chen, J.-Y., Huang, C.-H., Wu, I.-C., Chen, P.-F., Higa, S., Yagi, N., Hu, Y.-F., Hwang, M.-J., et al. (2022) · JACC: Asia · doi:10.1016/j.jacasi.2022.02.008 · Cited by 46
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
Artificial intelligence in cardiovascular medicine: An updated review of the literature
Zargarzadeh, A., Javanshir, E., Ghaffari, A., Mosharkesh, E., & Anari, B. (2023) · Journal of Cardiovascular and Thoracic Research · doi:10.34172/jcvtr.2023.33031 · Cited by 41
An electrocardiogram-based AI algorithm for early detection of pulmonary hypertension
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., et al. (2024) · European Respiratory Journal · doi:10.1183/13993003.00192-2024 · Cited by 38
Didden, E., Lee, E., Wyckmans, J., Quinn, D. A., & Perchenet, L. (2023) · Pulmonary Circulation · doi:10.1002/pul2.12188 · Cited by 37
Kogan, E., Didden, E., Lee, E., Nnewihe, A. N., Stamatiadis, D., Mataraso, S. J., Quinn, D. A., Rosenberg, D., Chehoud, C., & Bridges, C. R. (2022) · International Journal of Cardiology · doi:10.1016/j.ijcard.2022.12.016 · Cited by 34
The biomedical applications of artificial intelligence: an overview of decades of research
Naskar, S., Sharma, S., Kuotsu, K., Halder, S., Pal, G., Saha, S., Mondal, S., Biswas, U. K., Jana, M., & Bhattacharjee, S. (2025) · Journal of drug targeting · doi:10.1080/1061186x.2024.2448711 · Cited by 35
Liu, C., Hsieh, M.-E., Hu, Y., Wei, T.-Y., Wu, I., Chen, P., Lin, Y., Higa, S., Yagi, N., Chen, S., et al. (2022) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.121.008360 · Cited by 33
Artificial intelligence using electrocardiography: strengths and pitfalls
Kwon, J., Jo, Y., Lee, S. Y., & Kim, K. (2021) · European Heart Journal · doi:10.1093/eurheartj/ehab090 · Cited by 31
Self-citationDeep-Learning Model for Screening Sepsis Using Electrocardiography
Kwon, J.-M., Lee, Y. R., Jung, M.-S., Lee, Y.-J., Jo, Y.-Y., Kang, D.-Y., Lee, S. Y., Cho, Y.-H., Shin, J.-H., Ban, J.-H., et al. (2021) · Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine · doi:10.1186/s13049-021-00953-8 · Cited by 28
Self-citationFinding 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
Role of artificial intelligence in defibrillators: a narrative review
Brown, G., Conway, S., Ahmad, M., Adegbie, D., Patel, N., Myneni, V., Alradhawi, M., Kumar, N. S., Obaid, D. R., Pimenta, D., et al. (2022) · Open Heart · doi:10.1136/openhrt-2022-001976 · Cited by 27
Advanced Hybrid Machine Learning Model for Accurate Detection of Cardiovascular Disease
Navita, N., Mittal, P., Sharma, Y. K., Lilhore, U. K., Simaiya, S., Saleem, K., & Ghith, E. S. (2025) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-025-00771-1 · Cited by 26
The leading global health challenges in the artificial intelligence era
Zaidan, A. M. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1328918 · Cited by 31
Nechita, L., Nechita, A., Voipan, A. E., Voipan, D., Debita, M., Fulga, A., Fulga, I., & Mușat, C. L. (2024) · Diagnostics · doi:10.3390/diagnostics14171839 · Cited by 24
Tang, P., Wang, Q., Ouyang, H., Yang, S., & Hua, P. (2023) · Aging · doi:10.18632/aging.204688 · Cited by 25
Liu, P., Hsing, S.-C., Tsai, D., Lin, C. S., Lin, C.-S., Wang, C., & Fang, W. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01225-4 · Cited by 20
Deep Learning Based Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms
Ragnarsdóttir, H., Özkan, E., Michel, H., Chin-Cheong, K., Manduchi, L., Wellmann, S. M., & Vogt, J. E. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-01996-x · Cited by 21
A 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
Lou, Y.-S., Lin, C., Fang, W., Lee, C.-C., & Lee, C. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107359 · Cited by 20
Sun, D., Hu, Y., Li, Y., Yu, X., Chen, X., Shen, P., Tang, X., Wang, Y., Lai, C., Kang, B., et al. (2023) · Journal of Advanced Research · doi:10.1016/j.jare.2023.10.013 · Cited by 18
Schlesinger, D. E., Diamant, N., Raghu, A., Reinertsen, E., Young, K., Batra, P., Pomerantsev, E. V., & Stultz, C. M. (2022) · JACC Advances · doi:10.1016/j.jacadv.2022.100003 · Cited by 18
Advancing cardiovascular medicine with machine learning: Progress, potential, and perspective
Barrios, J. P., & Tison, G. H. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100869 · Cited by 21
Jo, Y., Kwon, J., Jeon, K., Cho, Y., Shin, J. H., Lee, Y., Jung, M., Ban, J., Kim, K., Lee, S. Y., et al. (2021) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztab025 · Cited by 18
Self-citationKwon, J., Kim, K., Jo, Y., Jung, M., Cho, Y., Shin, J. H., Lee, Y., Ban, J., Lee, S. Y., Park, J., et al. (2022) · International Urology and Nephrology · doi:10.1007/s11255-022-03165-w · Cited by 20
Self-citationFadilah, A., Putri, V. Y. S., Puling, I. M. D. R., & Willyanto, S. E. (2024) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2024.1422327 · Cited by 18
Bellfield, R. A. A., Ortega‐Martorell, S., Lip, G. Y. H., Oxborough, D., & Olier, I. (2022) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd9110382 · Cited by 19
Zhao, W., Huang, Z., Diao, X., Yang, Z., Zhao, Z., Xia, Y. F., Zhao, Q., Sun, Z., Xi, Q., Huo, Y., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01593-3 · Cited by 14
Chinni, B. K., & Manlhiot, C. (2024) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2024.07.026 · Cited by 19
Bart, N., Pepe, S., Gregory, A. T., & Denniss, A. R. (2023) · Heart Lung and Circulation · doi:10.1016/j.hlc.2023.07.005 · Cited by 11
Kaur, S., Bansal, K., & Kumar, Y. (2022) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i56241.2022.10072978 · Cited by 10
Chen, C., Zhao, H. Y., Zheng, S. H., Ramachandra, R. A., He, X., Zhang, Y. H., & Sudarshan, V. K. (2023) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2023.100097 · Cited by 10
Bellfield, R. A. A., Ortega‐Martorell, S., Lip, G. Y. H., Oxborough, D., & Olier, I. (2024) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2024.03.005 · Cited by 9
Guo, L. T., Khobragade, N., Kieu, S., Ilyas, S., Nicely, P. N., Asiedu, E. K., Lima, F. V., Currie, C., Lastowski, E., & Choudhary, G. (2025) · Journal of the American Heart Association · doi:10.1161/jaha.124.036882 · Cited by 9
Jia, Y., Li, Y., Luosang, G., Wang, J., Peng, G., Pu, X., Jiang, W., Li, W., Zhao, Z., Peng, Y., et al. (2024) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztae007 · Cited by 9
Argiento, P., D'Agostino, A. G., Castaldo, R., Franzese, M., Mazzola, M., Grünig, E., Saldamarco, L., Valente, V., Schiavo, A., Maffei, E., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.11.031 · Cited by 8
Kishikawa, R., Kodera, S., Setoguchi, N., Tanabe, K., Kushida, S., Nanasato, M., Maki, H., Fujita, H., Kato, N., Watanabe, H., et al. (2025) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztae097 · Cited by 8
The Management of Mild Pulmonary Hypertension in Clinical Practice
Zeder, K., Brittain, E. L., Kovács, G., & Maron, B. A. (2024) · Annals of the American Thoracic Society · doi:10.1513/annalsats.202312-1079fr · Cited by 8
Morland, K., Gerges, C., Elwing, J., Visovatti, S., Weatherald, J. C., Gillmeyer, K. R., Sahay, S., Mathai, S. C., Boucly, A., Williams, P., et al. (2023) · Pulmonary Circulation · doi:10.1002/pul2.12317 · Cited by 6
Luo, D., Zheng, X., Yang, Z., Li, H., Fei, H., & Zhang, C. (2023) · The Journal of Heart and Lung Transplantation · doi:10.1016/j.healun.2023.05.003 · Cited by 7
Ramirez-Bautista, J. A., Chaparro-Cárdenas, S. L., Esmer, C., & Huerta-Ruelas, J. A. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105463 · Cited by 7
Manzi, G., Papa, S., Mariani, M. V., Scoccia, G., Filomena, D., Malerba, C., Adamo, F. I., Caputo, A., Lazzari, C. D., Lazzari, B. D., et al. (2022) · Vascular Pharmacology · doi:10.1016/j.vph.2022.107024 · Cited by 7
Interpretable Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms
Ragnarsdóttir, H., Manduchi, L., Michel, H., Laumer, F., Wellmann, S. M., Özkan, E., & Vogt, J. E. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16788-1_32 · Cited by 7
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
Zeng, Y., Ling, G., Zhang, H., Cao, W., Zheng, X., Deng, X., Lan, L., Sun, R., Liu, X., Tian, L., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02233-6 · Cited by 6
Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension
Varzideh, F., Mone, P., Kansakar, U., Pande, S., Jankauskas, S. S., & Santulli, G. (2026) · Hypertension · doi:10.1161/hypertensionaha.126.26094 · Cited by 6
Using deep learning to diagnose pulmonary hypertension
van der Bijl, P., & Bax, J. J. (2022) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jeac148 · Cited by 6
Aşkın, L., Polat, E., Hoşoğlu, Y., & Tanrıverdi, O. (2024) · Gaziantep Islam Science and Technology University · doi:10.46871/eams.1438927 · Cited by 7
Wander, G., Hopkins, T., Scatola, A., Ruby, G., Johnson, M. R., Mehta‐Lee, S. S., Glicksberg, B. S., & Krittanawong, C. (2025) · Trends in Cardiovascular Medicine · doi:10.1016/j.tcm.2025.09.002 · Cited by 7
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
Averjanovaitė, V., Gumbienė, L., Zeleckienė, I., & Šileikienė, V. (2023) · Medicina · doi:10.3390/medicina60010058 · Cited by 4
Ledziński, Ł., Grześk, G., Ziołkowski, M., Waligóra, M., Kurzyna, M., Mularek‐Kubzdela, T., Smukowska-Gorynia, A., Skoczylas, I., Chrzanowski, Ł., Błaszczak, P., et al. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14238325 · Cited by 5
Deep learning prediction of survival in patients with heart failure using chest radiographs
Han, J., Liao, S., Zhu, X., Liu, W., Xu, Y., Ge, R., & Zhu, Y. (2024) · The International Journal of Cardiovascular Imaging · doi:10.1007/s10554-024-03177-w · Cited by 4
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
Artificial Intelligence as an Emerging Tool for Cardiologists
Ledziński, Ł., & Grześk, G. (2023) · International Electronic Conference on Biomedicines (IECBM) · doi:10.3390/ecb2023-14339 · Cited by 4
Chen, C., Zhao, H. Y., Zheng, S. H., Ramachandra, R. A., He, X., Zhang, Y. H., & Sudarshan, V. K. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4132904 · Cited by 3
A Review of Evaluation Approaches for Explainable AI With Applications in CardiologyPreprint
Salih, A. M., Galazzo, I. B., Gkontra, P., Rauseo, E., Lee, A. M., Lekadir, K., Radeva, P. I., Petersen, S. E., & Menegaz, G. (2023) · doi:10.36227/techrxiv.24573304.v1 · Cited by 4
XAI: Focusing on Fertility Assessment
Santosh, K., & Roy, D. G. (2025) · Studies in computational intelligence · doi:10.1007/978-981-95-1248-5_5 · Cited by 2
Advances in the Interpretation of the Electrocardiogram by Artificial Intelligence
Lobodzinski, S. S., & Piotrowicz, R. (2026) · Diagnostics · doi:10.3390/diagnostics16142167 · Cited by 2
Grzeszczyk, M. K., Satława, T., Lungu, A., Swift, A. J., Narracott, A. J., Hose, R., Trzcinski, T. P., & Sitek, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08757-8_2 · Cited by 2
Risk Factors for Adverse Outcomes in Connective Tissue Disease-Associated Pulmonary Hypertension
Matusov, G., Shams, M., Ibrahim, K., Hovsepyan, A., & Matusov, Y. P. (2025) · Reviews in Cardiovascular Medicine · doi:10.31083/rcm26877 · Cited by 1
Artificial Intelligence in Cardiovascular Diagnosis: Innovations and Impact on Disease Screenings
Ahmad, A., Ahmad, S., Ahmad, R., Bodi, J., Mohamed, A. I., & Wasim, A. (2025) · Journal of Pharmacy And Bioallied Sciences · doi:10.4103/jpbs.jpbs_557_25 · Cited by 3
An Overview of Artificial Intelligence in Primary Care and Administrative Medicine
Morelli, V. (2025) · Primary Care Clinics in Office Practice · doi:10.1016/j.pop.2025.07.008 · Cited by 1
Gene Therapy and Epigenetic Modulation in Chronic Pain: A Future Without Opioids?
Idahor, C. O., Okorie, M. A., Mokobia, S., Erhuanga, P., Ogbonna, N., Ogunfuwa, O., Etoroma, O. M., Abhulimen, I. J., & Chinedu, E. (2025) · Cureus · doi:10.7759/cureus.94508 · Cited by 1
Eshraghi, R., Yazdani, M. S., & Bahrami, A. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1011128 · Cited by 1
Qi, Y., Liang, J., Ma, K., Hu, X. Y., Zhang, Y., Han, X., Xu, D., Zhang, X., Cui, X., Xiang, M., et al. (2026) · Interdisciplinary medicine · doi:10.1002/inmd.70099 · Cited by 1
Deep Learning Model for Detection of Hypoalbuminemia Using ElectrocardiographyPreprint
Kwon, J., Lee, S., Lee, Y., Jo, Y., Jung, M., Cho, Y., Shin, J. H., Ban, J., Kim, K., Park, J., et al. (2021) · Preprints.org · doi:10.20944/preprints202101.0408.v1
Self-citationState-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint)Preprint
Πετμεζάς, Γ., Stefanopoulos, L., Kilintzis, V., Tzavelis, A., Rogers, J. A., Katsaggelos, A. K., & Maglaveras, N. (2022) · doi:10.2196/preprints.38454 · Cited by 1
Overcoming Diagnostic Delays in Pulmonary Hypertension With Deep-Learning ECG Analysis
Langlais, É. L., & Avram, R. (2023) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2023.02.007 · Cited by 1
Artificial intelligence to improve the diagnosis of pulmonary hypertension: promises and pitfalls
Singh, N., & Mehta, S. (2024) · Heart · doi:10.1136/heartjnl-2023-323693 · Cited by 1
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
Wang, G., Wang, Y., & Zhao, R. (2022) · Scientific Programming · doi:10.1155/2022/8350079
Explainable machine learning for decision support in healthcare: A scoping review (Preprint)Preprint
Shulha, M., Rahimi, S. A., Sandhu, A., Sharma, G., D’Souza, V., Harmouche, R., & Hovdebo, J. (2022) · doi:10.2196/preprints.39196
Usmani, S., Ahmad, M., & Bray, J. J. H. (2022) · JACC Advances · doi:10.1016/j.jacadv.2022.100108
Pulmonary hypertension in the primary care setting
Lua, S., & Church, C. (2022) · Prescriber · doi:10.1002/psb.2022
Inteligencia artificial en neumología
Vargas-Ramírez, L., & Ayazo, R. B. (2022) · Medicina · doi:10.56050/01205498.1646
Chen, C., Zhao, H. Y., Zheng, S. H., Ramachandra, R. A., He, X., Zhang, Y. H., & Sudarshan, V. K. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4182263
Moradi, S., Simyar, D., & Safari, M. (2023) · Biomedical engineering · doi:10.5772/intechopen.109231 · Cited by 1
Sobolev, A. V., Blinova, E. V., Анатольевна, С. Т., & Drozdov, D. V. (2024) · Medical alphabet · doi:10.33667/2078-5631-2024-30-28-35
Lima, L. D. C. A., Silva, I. S. S. E., Sanglard, B. S. A. S. E. S., Sanglard, P. A. D. L., Lima, G. F., Almeida, Â. F., de Moura, H. G. P., de Souza, H. G., Santos, J., Verbo, M. P. C., et al. (2025) · Revista Contemporânea · doi:10.56083/rcv5n2-043
Pulmonary hypertension: diagnostic aspects-what is the role of imaging?
Ali, H., & Guha, A. (2025) · Current Opinion in Cardiology · doi:10.1097/hco.0000000000001241
Spiel, M. H. (2025) · Trends in Cardiovascular Medicine · doi:10.1016/j.tcm.2025.09.003
Trusov, Y. A., Shamsueva, K. T., Kolkhidova, M. Z., Inderbieva, A. T., Baryshnikova, E. D., Khusnutdinova, K. A., Rasponomaryova, A. E., Shabazgerieva, A. R., Radzhabov, K. K., Sanakoev, S. A., et al. (2025) · Digital Diagnostics · doi:10.17816/dd691113 · Cited by 1
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 · Cited by 1
HDNet: A Deep Learning Model for Diagnosing Hypertension Using Electrocardiogram Images
Qin, J., Wei, Y., Wang, Z., Jin, J., Zhao, W., & Pei, L. (2025) · IEEE Smart World Congress (SWC) · doi:10.1109/swc65939.2025.00253
Liu, L., Nie, Z., Ganggayah, M. D., & Krishnasamy, S. (2026) · doi:10.2196/preprints.74956
Metadata
- Authors
- Kwon, J.-M., Kim, K.-H., Medina-Inojosa, J., Jeon, K.-H., Park, J., & Oh, B.-H.
- Affiliations
- Sejong General Hospital (KR)
- Corresponding author
- Kim, Kyung-Hee
- Venue
- The Journal of Heart and Lung Transplantation
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 39
- Issue
- 8
- Pages
- 805-814
- ISSN
- 1053-2498 (ISSN-L)
- OpenAlex
- W3013435687
- Cited by
- 108
- References
- 35
- Citations by year
- 2026: 16 · 2025: 20 · 2024: 18 · 2023: 19 · 2022: 25 · 2021: 8 · 2020: 2
- FWCI
- 8.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Journal of Heart and Lung Transplantation (publishedVersion)
- PubMed (publishedVersion)
- 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, electrocardiogram, artificial intelligence, early prediction, neural network ensembles, explainable AI, sensitivity maps, 12-lead ECG, single-lead ECG, risk stratification, echocardiography
- MeSH terms
- Aged, Algorithms, Artificial Intelligence, Electrocardiography, Female, Follow-Up Studies, 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
