Accessory pathway analysis using a multimodal deep learning model
Open accessAbstract
Cardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.
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.
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
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 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
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 16,369 · see citing works
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267 · see citing works
Deep Learning Face Representation from Predicting 10,000 Classes
Sun, Y. J., Wang, X., & Tang, X. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.244 · Cited by 1,995 · see citing works
The Relationship Between Variable Selection and Data Agumentation and a Method for Prediction
Allen, D. M. (1974) · Technometrics · doi:10.1080/00401706.1974.10489157 · Cited by 1,469 · see citing works
Frazier, P. I. (2018) · doi:10.1287/educ.2018.0188 · Cited by 887 · see citing works
Transfusion: Understanding Transfer Learning for Medical ImagingPreprint
Raghu, M., Zhang, C., Kleinberg, J. M., & Samy Bengio, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.07208 · Cited by 845 · see citing works
Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review
Poggio, T., Mhaskar, H., Rosasco, L., Miranda, B., & Liao, Q. (2017) · International Journal of Automation and Computing · doi:10.1007/s11633-017-1054-2 · Cited by 559 · see citing works
Rosenbaum, F. F., Hecht, H. H., Wilson, F. N., & Johnston, F. D. (1945) · American Heart Journal · doi:10.1016/0002-8703(45)90333-4 · Cited by 326 · see citing works
Kusunose, K., Abe, T., Haga, A., Fukuda, D., Yamada, H., Harada, M., & Sata, M. (2019) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2019.02.024 · Cited by 226 · see citing works
Zhu, H., Cheng, C., Yin, H., Li, X., Zuo, P., Ding, J., Lin, F., Wang, J., Zhou, B., Li, Y., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30107-2 · Cited by 206 · see citing works
Chiang, C., Chen, S., Teo, W. S., Tsai, D.-S., Wu, T.-J., Cheng, C., Chiou, C.-W., TAI, C., Lee, S.-H., Chen, C.-Y., et al. (1995) · The American Journal of Cardiology · doi:10.1016/s0002-9149(99)80798-x · Cited by 127 · see citing works
Traffic Light Detection and Recognition for Self Driving Cars Using Deep Learning
Kulkarni, R., Dhavalikar, S., & Bangar, S. (2018) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea.2018.8697819 · Cited by 102 · see citing works
Localization of accessory pathways from the 12-lead electrocardiogram using a new algorithm
Xie, B., HEALD, S. C., Bashir, Y., Katritsis, D. G., MURGATROYD, F. D., Camm, A. J., Rowland, E., & Ward, D. E. (1994) · The American Journal of Cardiology · doi:10.1016/0002-9149(94)90090-6 · Cited by 100 · see citing works
Antz, M., WEIß, C., Volkmer, M., Hebe, J., Ernst, S., Ouyang, F. O., & Kück, K. (2002) · Journal of Cardiovascular Electrophysiology · doi:10.1046/j.1540-8167.2002.00231.x · Cited by 42 · see citing works
WPW Pattern in the Asymptomatic Individual
Klein, G. J., Gula, L. J., Krahn, A. D., Skanes, A. C., & Yee, R. K. (2009) · Circulation Arrhythmia and Electrophysiology · doi:10.1161/circep.109.859827 · Cited by 36 · see citing works
Taguchi, N., Yoshida, N., Inden, Y., Yamamoto, T., Miyata, S., Fujita, M., Yokoi, K., Kyo, S., Shimano, M., Hirai, M., et al. (2013) · Journal of Arrhythmia · doi:10.1016/j.joa.2013.10.006 · Cited by 32 · see citing works
UEDA, H. M. D., NAMEKI, C., SARUTA, H., KAWAMURA, H., Yoshida, A., & Tsuzuku, A. (1957) · Japanese Circulation Journal-english Edition · doi:10.1253/circj1947.21.8_361 · Cited by 25 · see citing works
Self-citationAtrial Fibrillation in Patients with Wolff–Parkinson–White Syndrome:
Derejko, P., Szumowski, Ł. J., Sanders, P., Krupa, W., Bodalski, R., Orczykowski, M., Urbanek, P., ZAKRZEWSKA, J., Lim, H. S., Lau, D. H., et al. (2011) · Journal of Cardiovascular Electrophysiology · doi:10.1111/j.1540-8167.2011.02203.x · Cited by 20 · see citing works
CT scan AI-aided triage for patients with COVID-19 in China
Vardhanabhuti, V. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30222-3 · Cited by 13 · see citing works
Nishimori, M., Kiuchi, K., Mori, S., Kurose, J., Izawa, Y., Kouno, S., Nakagawa, H., Shimoyama, S., Fukuzawa, K., & Hirata, K. (2018) · HeartRhythm Case Reports · doi:10.1016/j.hrcr.2018.04.009 · Cited by 5 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Multimodal machine learning in precision health: A scoping review
Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., Wang, F., Cheng, F., & Luo, Y. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00712-8 · Cited by 528 · see citing works
Review of multimodal machine learning approaches in healthcare
Krones, F., Marikkar, U., Parsons, G., Szmul, A., & Mahdi, A. (2025) · Information Fusion · doi:10.1016/j.inffus.2024.102690 · Cited by 235 · see citing works
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 120 · see citing works
Reviewing Multimodal Machine Learning and Its Use in Cardiovascular Diseases Detection
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Electronics · doi:10.3390/electronics12071558 · Cited by 75 · see citing works
Artificial Intelligence in Pediatric Cardiology: A Scoping Review
Sethi, Y., Patel, N., Kaka, N., Desai, A., Kaiwan, O., Sheth, M., Sharma, R., Huang, H. Y. R., Chopra, H., Khandaker, M. U., et al. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11237072 · Cited by 53 · see citing works
Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review
Ahmad, H. K., Milne, M., Buchlak, Q. D., Ektas, N., Sanderson, G., Chamtie, H., Karunasena, S., Chiang, J., Holt, X., Tang, C. H. M., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13040743 · Cited by 50 · see citing works
Palermi, S., Vecchiato, M., Saglietto, A., Niederseer, D., Oxborough, D., Ortega‐Martorell, S., Olier, I., Castelletti, S., Baggish, A. L., Maffessanti, F., et al. (2024) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwae008 · Cited by 50 · see citing works
Hamriti, M. E., BRAUN, M. U., Molatta, S., Imnadze, G., Khalaph, M., Lucas, P., Nolting, J. K., Isgandarova, K., Sciacca, V., Fink, T., et al. (2022) · EP Europace · doi:10.1093/europace/euac216 · Cited by 34 · see citing works
Self-citationUtilizing multimodal artificial intelligence to advance cardiovascular diseases
Yang, X.-Y., Li, Y., Wang, J., Jia, Y., Zhang, Y., & Chen, M. (2025) · Precision Clinical Medicine · doi:10.1093/pcmedi/pbaf016 · Cited by 39 · see citing works
Avula, V. R., Wu, K. C., & Carrick, R. T. (2023) · JACC Advances · doi:10.1016/j.jacadv.2023.100686 · Cited by 28 · see citing works
Multimodal Machine Learning Approaches in Predictive Healthcare Analytics: A Comprehensive Survey
Vavekanand, R., Kumar, T., Kumar, S., Kumar, G., & Laghari, A. A. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10560-4 · Cited by 29 · see citing works
Beyond traditional orthopaedic data analysis: AI, multimodal models and continuous monitoring
Oettl, F. C., Zsidai, B., Oeding, J. F., Hirschmann, M. T., FELDT, R. H., Tischer, T., Samuelsson, K., & ESSKA Artificial Intelligence Working Group (2025) · Knee Surgery Sports Traumatology Arthroscopy · doi:10.1002/ksa.12657 · Cited by 24 · see citing works
Artificial Intelligence in Pediatric Electrocardiography: A Comprehensive Review
Leone, D. M., O’Sullivan, D. M., & Jaimes, K. B. (2024) · Children · doi:10.3390/children12010025 · Cited by 23 · see citing works
Deep learning methods for screening patients' S-ICD implantation eligibility
Dunn, A. J., ElRefai, M., Roberts, P. R., Coniglio, S., Wiles, B. M., & Zemkoho, A. B. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102139 · Cited by 19 · see citing works
Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia
Guo, R., Tian, X., Bazoukis, G., Tse, G., Hong, S., Chen, K., & Liu, T. (2024) · Pacing and Clinical Electrophysiology · doi:10.1111/pace.14995 · Cited by 17 · see citing works
Nakasone, K., Nishimori, M., Kiuchi, K., Shinohara, M., Fukuzawa, K., Takami, M., Hamriti, M. E., Sommer, P., Sakai, J., Nakamura, T., et al. (2022) · Circulation Journal · doi:10.1253/circj.cj-22-0065 · Cited by 12 · see citing works
Self-citationRăileanu, G., & de Jong, J. S. S. G. (2023) · Current Problems in Cardiology · doi:10.1016/j.cpcardiol.2023.102097 · Cited by 10 · see citing works
Automatic detection of cardiac conditions from photos of electrocardiogram captured by smartphones
Wong, C., Lau, Y. M., Lui, H. W., Chan, W.-F., San, W. C., Zhou, M., Cheng, Y., Huang, D., Lai, W. H., Lau, Y. M., et al. (2024) · Heart · doi:10.1136/heartjnl-2023-323822 · Cited by 7 · see citing works
Hennecken, J., Arends, B. K. O., Mast, T. P., Dekker, L., van der Harst, P., Blaauw, Y., Dichtl, W., Senoner, T., Hassink, R. J., Loh, P. K. K., et al. (2025) · European Journal of Clinical Investigation · doi:10.1111/eci.14385 · Cited by 6 · see citing works
Kurath‐Koller, S., Manninger, M., Oeffl, N., Köestenberger, M., Sallmon, H., Will, J. C., & Scherr, D. (2022) · Children · doi:10.3390/children9121962 · Cited by 6 · see citing works
Gillette, K. K., Winkler, B., Kurath‐Koller, S., Scherr, D., Vigmond, E. J., Bär, M., & Plank, G. (2024) · EP Europace · doi:10.1093/europace/euae223 · Cited by 5 · see citing works
Manninger, M., Lercher, I., Hermans, A. N. L., Isaksen, J. L., Prassl, A. J., Zirlik, A., Vernooy, K., Chaldoupi, S., LUERMANS, J. G. L. M., ter Bekke, R. M. A., et al. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108798 · Cited by 4 · see citing works
Raj, S., & Bayappu, N. (2024) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815305128124010008 · Cited by 4 · see citing works
Κόλλιας, Γ., & Pürerfellner, H. (2026) · Herzschrittmachertherapie + Elektrophysiologie · doi:10.1007/s00399-025-01128-z · Cited by 2 · see citing works
Saraç, U., Paydaş, A. B., Çeliktaş, Z., Kuyucuoğlu, T., Şap, F., Baysal, T., & Oflaz, M. B. (2026) · Indian Pediatrics · doi:10.1007/s13312-026-00396-3 · Cited by 2 · see citing works
Abdelrazik, A., Eldesouky, M., Antoun, I., Thu, K. M., Mavilakandy, A., Su, T. S., Lau, E. Y. M., Altoukhy, S., Lazdam, M., Vali, Z., et al. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15114058 · Cited by 1 · see citing works
Jia, H., Zhu, W., & Lv, J. (2026) · Frontiers in Pediatrics · doi:10.3389/fped.2026.1932796 · Cited by 1 · see citing works
Molinari, M., Setti, S., Brunetti, N. D., Nunno, N. D., Cattabiani, M. A., & Molinari, G. (2024) · IJC Heart & Vasculature · doi:10.1016/j.ijcha.2024.101553 · Cited by 1 · see citing works
Artificial intelligence in electrophysiology
Feeny, A. K., Tandon, A. A., & Nguyen, H. H. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-90534-3.00017-2
Detecting Wolff-Parkinson-White from Lead-I ECG Using Transfer Learning and WaveletsPreprint
Bharadwaj, S., Altman, S., Wang, J. J., & Alam, R. (2024) · medRxiv · doi:10.1101/2024.09.26.24314432
Nagpal, N., Mehra, H., Sehgal, N., Vij, S., & Virmani, D. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-2015-9_48
Explainable deep learning-based classification of Wolff-Parkinson-White electrocardiographic signals
Ragonesi, A., Fresca, S., Gillette, K., Kurath-Koller, S., Plank, G., & Zappon, E. (2026) · Frontiers in Physiology · doi:10.3389/fphys.2026.1855555
Antoun, I., Alkhayer, A., Abdelrazik, A., Eldesouky, M., Thu, K. M., Ibrahim, M. N., Somani, R., & Ng, G. A. (2026) · Journal of Cardiovascular Electrophysiology · doi:10.1111/jce.70319
Tail-AwareNet: An ECG-Based classification network for accessory pathway locations
Ma, Y., Feng, Y., Wang, J., Zheng, Z., Dong, R., Luo, Y., Li, S., Huang, Z., Zhu, J., Wei, W., et al. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110458
Multimodal Fusion and Adaptive Learning for Cardiopulmonary Classification
Abed, M. T., & Zhao, X. (2026) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3807503.3819467
Томчик, Н. В., & Paramonova, N. S. (2026) · Healthcare · doi:10.65249/1027-7218-2026-7-67-75
- Authors
- Nishimori, M., Kiuchi, K., Nishimura, K., Kusano, K., Yoshida, A., Adachi, K., Hirayama, Y., Miyazaki, Y., Fujiwara, R., Sommer, P., El Hamriti, M., Imada, H., Takemoto, M., Takami, M., Shinohara, M., Toh, R., Fukuzawa, K., & Hirata, K.-I.
- Affiliations
- Kobe University Hospital (JP)
- National Cerebral and Cardiovascular Center Research Institute (JP)
- National Cerebral and Cardiovascular Center (JP)
- Kita Harima Medical Center (JP)
- Akashi Medical Center (JP)
- Saiseikai Nakatsu Hospital (JP)
- University Hospitals of the Ruhr-University of Bochum (DE)
- AKO City Hospital (JP)
- Kobe University (JP)
- Corresponding author
- Kiuchi, Kunihiko
- ORCID
- Venue
- Scientific Reports
- Published
- 2021-04-13
- Volume
- 11
- Issue
- 1
- Pages
- 8045-8045
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC8044112
- OpenAlex
- W3153585807
- Cited by
- 36
- References
- 23
- Citations by year
- 2026: 10 · 2025: 5 · 2024: 9 · 2023: 5 · 2022: 6 · 2021: 1
- FWCI
- 3.52
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Institutional Repositories DataBase (IRDB) (acceptedVersion, cc-by)
- Kobe University Repository Kernel (Kobe University) (submittedVersion, cc-by)
- Kobe University Repository Kernel (Kobe University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac Arrhythmias and Treatments
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
- Keywords
- Wolff-Parkinson-White syndrome, electrocardiogram, chest radiography, multimodal deep learning, convolutional neural networks, artificial intelligence diagnosis, electrophysiological study
- MeSH terms
- Deep Learning, Adolescent, Adult, Algorithms, Electrocardiography, Female, Humans, Male, Middle Aged, Retrospective Studies, Radiography, Thoracic, Wolff-Parkinson-White Syndrome, Neural Networks, Computer, Young Adult, Accessory Atrioventricular Bundle
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
