RLDS: An explainable residual learning diagnosis system for fetal congenital heart disease
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The RLDS architecture has three classes of CHDs (HRHS, HLHS, and highly RAS) versus the normal control.
“Unfortunately, the RLDS architecture has three classes of CHDs (HRHS, HLHS, and highly RAS) versus the normal control.”
From Deep Learning for Improving the Effectiveness of Routine Prenatal Screening for Major Congenital Heart Diseases (2022) · cites this paper as [13]
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Wan, W., Luo, T., Zhang, X., Guo, C., & Wang, X. (2026) · BMC Pediatrics · doi:10.1186/s12887-026-06711-y
Rodríguez, S., Perez, A., Echeverry, L. M., Castillo, A., Ramírez, N. A., Escobar, M., Monroy, S. G., Vega, D., Rodríguez, N., Castro-Páez, C., et al. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1726484 · Cited by 1 · see citing works
Congenital heart disease diagnosis using machine learning: a systematic literature review
Khan, K., Syed, I., Ullah, F., & Khan, R. U. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1757468 · Cited by 5 · see citing works
Mohan, A. (2026) · Journal of diagnostic medical sonography · doi:10.1177/87564793261428478 · Cited by 1 · see citing works
Baig, M. U., Maram, B., Prasan, U. D., & Easwaramoorthy, S. V. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-18135-0_18
- Authors
- Qiao, S., Pang, S., Luo, G., Pan, S., Yu, Z., Chen, T., & Lv, Z.
- Affiliations
- China University of Petroleum, East China (CN)
- Qingdao Women and Children's Hospital (CN)
- Qingdao University (CN)
- Corresponding author
- Pang, Shanchen
- Pan, Silin
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2022-03
- Volume
- 128
- Pages
- 205-218
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W3206139678
- Cited by
- 42
- References
- 66
- Citations by year
- 2026: 7 · 2025: 5 · 2024: 6 · 2023: 8 · 2022: 16
- FWCI
- 6.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Future Generation Computer Systems (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Epidemiology › Congenital Heart Disease Studies
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- fetal congenital heart disease, prenatal diagnosis, fetal echocardiography, residual learning, explainable AI, feature visualization, convolutional neural networks, fetal cardiac anatomy, diagnostic accuracy
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