The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights | Paper Citation Record

Observation 0a691dd1-7bcc-4550-971b-2772e653c48a · outbound

This paper cites Allen, R.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights Allen, R

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This paper cites Sable, Michelle Marie Echko, Lauren B.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights Sable, Michelle Marie Echko, Lauren B

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Observation f33ebaef-8983-465b-a91c-cd011a0abeea · outbound

This paper cites ”Current treatment outcomes of congenital heart disease and future perspectives.” The Lancet Child & Adolescent Health 7, no.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Current treatment outcomes of congenital heart disease and future perspectives.” The Lancet Child & Adolescent Health 7, no

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This paper cites Improved surgical outcome after fetal diagnosis of hypoplastic left heart syndrome.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights Improved surgical outcome after fetal diagnosis of hypoplastic left heart syndrome

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This paper cites Detection of transposition of the great arteries in fetuses reduces neonatal morbidity and mortality.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights Detection of transposition of the great arteries in fetuses reduces neonatal morbidity and mortality

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This paper cites Prenatal detection of transposition of the great arteries reduces mortality and morbidity.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights Prenatal detection of transposition of the great arteries reduces mortality and morbidity

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This paper cites Prenatal diagnosis, birth location, surgical center, and neonatal mortality in infants with hypoplastic left heart syndrome.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights Prenatal diagnosis, birth location, surgical center, and neonatal mortality in infants with hypoplastic left heart syndrome

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The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Epidemiology of Congenital Heart Disease in Kazakhstan: Data from the Unified National Electronic Healthcare System 2014-2021.” J Clin Med Kaz 21.3 (2024): 49-55

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The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights Unresolved cited work

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The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Congenital heart disease in adults.” bmj 354 (2016)

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This paper cites A., Saleem Ullah Shahid, and Uzma Irfan.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights A., Saleem Ullah Shahid, and Uzma Irfan

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The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Prevalence of congenital heart disease at live birth in China.” The Journal of pediatrics 204 (2019): 53-58

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The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Explainable and interpretable artificial intelligence in medicine: a systematic bibliometric review.” Discover Artificial Intelligence 4.1 (2024): 15

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The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Artificial intelligence in clinical diagnosis: opportunities, challenges, and hype.” Jama (2023)

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The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning (Supplementary Material).”

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The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Deep convolutional neural network based analysis of liver tissues using computed tomography images.” Symmetry 14.2 (2022): 383

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The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Deep learning: a breakthrough in medical imaging.” Current Medical Imaging 16.8 (2020): 946-956

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The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights and Zhang, L., 2022

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The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Applications of artificial intelligence-powered prenatal diagnosis for congenital heart disease.” Frontiers in Cardiovas- cular Medicine 11 (2024): 1345761

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The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”How Will Artificial Intelligence Shape the Future of Decision-Making in Congenital Heart Disease?.” Journal of Clinical Medicine 13.10 (2024): 2996

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Observation 7f35dfc0-3f54-4f55-90d8-8674f9ba798b · outbound

This paper cites ”Can artificial intelligence-assisted auscultation become the Heimdallr for diagnosing congenital heart disease?.” European Heart Journal-Digital Health 2.1 (2021): 117-118.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Can artificial intelligence-assisted auscultation become the Heimdallr for diagnosing congenital heart disease?.” European Heart Journal-Digital Health 2.1 (2021): 117-118

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Observation 3be1d530-b7db-4cd4-b250-14782f727464 · outbound

This paper cites an unresolved cited work.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights Unresolved cited work

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.247554Z

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Observation 03c8f74c-94f9-4dbb-8907-f566a1e7f02c · outbound

This paper cites ”Current status of screening, diagnosis, and treatment of neonatal congenital heart disease in China.” World Journal of Pediatrics 14 (2018): 313-314.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Current status of screening, diagnosis, and treatment of neonatal congenital heart disease in China.” World Journal of Pediatrics 14 (2018): 313-314

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.251607Z

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Observation 73dcbdec-e0ec-4434-a46c-0bba153fb539 · outbound

This paper cites ”Development of digital stethoscope for telemedicine.” Journal of medical engineering & technology 40.1 (2016): 20-24.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Development of digital stethoscope for telemedicine.” Journal of medical engineering & technology 40.1 (2016): 20-24

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.255713Z

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Observation 89d9844c-e3dd-4a9b-847f-ee7d3bff63d1 · outbound

This paper cites ”Classic imaging signs of congenital cardiovascular abnormalities.” Radiographics 27.5 (2007): 1323-1334.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Classic imaging signs of congenital cardiovascular abnormalities.” Radiographics 27.5 (2007): 1323-1334

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.259873Z

Source-reported events for the cited work

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Observation 0ff4089d-c37e-4ece-89ab-ce0d5cb1b7a5 · outbound

This paper cites & Tian, J.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights & Tian, J

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.263997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 663a9024-aa6c-4d22-be38-e3f9020d8630 · outbound

This paper cites & Brun, H.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights & Brun, H

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.268351Z

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Observation 2a9af26d-f2a2-4602-8c32-7bf8a0bf70a0 · outbound

This paper cites an unresolved cited work.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights Unresolved cited work

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.272617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98ee8d68-14f6-4707-847c-294476a3219e · outbound

This paper cites & Renumadhavi, C.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights & Renumadhavi, C

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.276919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20c714dc-39a9-464a-8878-c869e3b6f73b · outbound

This paper cites ”An open access database for the evaluation of heart sound algorithms.” Physiological measurement 37.12 (2016): 2181.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”An open access database for the evaluation of heart sound algorithms.” Physiological measurement 37.12 (2016): 2181

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.280855Z

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Observation 354602a8-7e0e-4e98-8d92-69c5c426d376 · outbound

This paper cites ”CHD-CXR: a de-identified publicly available dataset of chest x-ray for congenital heart disease.” Frontiers in Cardiovascular Medicine 11 (2024): 1351965.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”CHD-CXR: a de-identified publicly available dataset of chest x-ray for congenital heart disease.” Frontiers in Cardiovascular Medicine 11 (2024): 1351965

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.285022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d97f8e39-f55e-4f55-88e0-5049e70a016b · outbound

This paper cites an unresolved cited work.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights Unresolved cited work

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.289212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3217db45-79e8-4d3b-9f24-a02a12551121 · outbound

This paper cites Echocardiography in heart failure: beyond diagnosis.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights Echocardiography in heart failure: beyond diagnosis

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.293350Z

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Unavailable: canonical work link unavailable.

Observation 87c91033-0b3b-4b72-810f-a196eb92dab9 · outbound

This paper cites & Mitchell, J.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights & Mitchell, J

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.297484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4e9c546-8147-4626-bbb6-6aea4df01b05 · outbound

This paper cites & Lang, R.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights & Lang, R

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.301843Z

Source-reported events for the cited work

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Observation 20002700-918b-41f9-97c8-6cd7e71a03bb · outbound

This paper cites and Wang, B., 2021.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights and Wang, B., 2021

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.305826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e41eb397-e688-498f-b738-af0fcb066279 · outbound

This paper cites & Yan, B.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights & Yan, B

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.309980Z

Source-reported events for the cited work

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Observation 33c6930b-b471-4716-896e-e1d84b59d88c · outbound

This paper cites ”Deep learning for improving the effectiveness of routine prenatal screening for major congenital heart diseases.” Journal of clinical medicine 11.21 (2022): 6454.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Deep learning for improving the effectiveness of routine prenatal screening for major congenital heart diseases.” Journal of clinical medicine 11.21 (2022): 6454

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.313933Z

Source-reported events for the cited work

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Observation 21cddbdf-af26-49e7-b777-73310b9c2b9c · outbound

This paper cites & Others Pulse oximetry with clinical assessment to screen for congenital heart disease in neonates in China: a prospective study.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights & Others Pulse oximetry with clinical assessment to screen for congenital heart disease in neonates in China: a prospective study

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.317984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16d91b11-b603-41d6-bc8e-cf5c609cac5c · outbound

This paper cites & Ewer, A.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights & Ewer, A

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.322042Z

Source-reported events for the cited work

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Observation 6b2a2f55-b443-42d5-b51c-32550096fbb7 · outbound

This paper cites & Ewer, A.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights & Ewer, A

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.326026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b27834ce-91de-4976-909b-ef2de3cf7308 · outbound

This paper cites & Kulczycki, I.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights & Kulczycki, I

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.330083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 352b70a3-689f-4fb6-a74a-3a1c82ab8db3 · outbound

This paper cites an unresolved cited work.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights Unresolved cited work

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.334319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb359f31-d24f-4b7e-a118-28bc0fc7fe23 · outbound

This paper cites & Bijnens, B.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights & Bijnens, B

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.338280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dea0714d-089e-4f51-ac25-45114d21fe5f · outbound

This paper cites & Cohen, G.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights & Cohen, G

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.342412Z

Source-reported events for the cited work

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Observation 46693cc3-b636-4e1b-9be2-0d00bce5a09f · outbound

This paper cites ”Development and Validation of a Deep-Learning Network for Detecting Congenital Heart Disease from Multi-View Multi-Modal Transthoracic Echocardiograms.” Research 7 (2024): 0319.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Development and Validation of a Deep-Learning Network for Detecting Congenital Heart Disease from Multi-View Multi-Modal Transthoracic Echocardiograms.” Research 7 (2024): 0319

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Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.346427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ddef818-dda0-4390-94ee-be122d8f8df9 · outbound

This paper cites ”Utility of deep learning networks for the generation of artificial cardiac magnetic resonance images in congenital heart disease.” BMC medical imaging 20 (2020): 1-8.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Utility of deep learning networks for the generation of artificial cardiac magnetic resonance images in congenital heart disease.” BMC medical imaging 20 (2020): 1-8

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:35:03.034659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2f6c3e93-d496-432a-9b97-dcf3c7007b8b · outbound

This paper cites ”Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts.” Nature Communications 15.1 (2024): 976.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts.” Nature Communications 15.1 (2024): 976

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:35:03.020830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b0a879d2-0801-451d-a7d2-ec1aaf6eb455 · outbound

This paper cites ”Fetal electrocardiography and deep learning for prenatal detection of congenital heart disease.” 2019 Computing in Cardiology (CinC).

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Fetal electrocardiography and deep learning for prenatal detection of congenital heart disease.” 2019 Computing in Cardiology (CinC)

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:35:03.006655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 54b969f2-cd38-4162-9961-3707a5e581bb · outbound

This paper cites ”Predicting congenital heart defects: A comparison of three data mining methods.” PloS one 12.5 (2017): e0177811.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Predicting congenital heart defects: A comparison of three data mining methods.” PloS one 12.5 (2017): e0177811

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:35:02.993479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a17f6be8-05d6-4c4b-bcb1-7c786fb15856 · outbound

This paper cites & Alzahrani, A.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights & Alzahrani, A

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:35:02.980542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5bbd62c3-8247-4fbf-a3c3-4b335ec26591 · outbound

This paper cites ”Head pose estimation: A survey of the last ten years.” Signal Processing: Image Communication 99 (2021): 116479.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Head pose estimation: A survey of the last ten years.” Signal Processing: Image Communication 99 (2021): 116479

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:35:02.966309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c8740a55-6d59-41b9-9f29-da107ba0771a · outbound

This paper cites ”A unified framework for head pose, age and gender classification through end-to-end face segmentation.” Entropy 21.7 (2019): 647.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”A unified framework for head pose, age and gender classification through end-to-end face segmentation.” Entropy 21.7 (2019): 647

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:35:02.952377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2ce7addb-50ef-416b-8c09-f460de91728e · outbound

This paper cites ”Head pose estimation through multi-class face segmentation.” 2017 IEEE International Conference on Multimedia and Expo (ICME).

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Head pose estimation through multi-class face segmentation.” 2017 IEEE International Conference on Multimedia and Expo (ICME)

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:35:02.939384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bf65537f-208f-4a79-ad4a-89794b16a182 · outbound

This paper cites ”Automatic gender classification through face segmentation.” Symmetry 11.6 (2019): 770.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Automatic gender classification through face segmentation.” Symmetry 11.6 (2019): 770

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:35:02.925869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a64cb019-eac8-4764-9b26-7f7158ec0e41 · outbound

This paper cites ”Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning.” Computers, Mate- rials & Continua 79.1 (2024).

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning.” Computers, Mate- rials & Continua 79.1 (2024)

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:35:02.912983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 145e4b1f-c3a8-4f20-a25a-1c2fea2dd0f0 · outbound

This paper cites U., Khan, S., Khan, K.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights U., Khan, S., Khan, K

Reference 91

Resolution: metadata mismatch; raw_fallback, observed 2026-08-10T21:35:02.027426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b7032dea-ad4a-4cdd-8e5b-e5571c3057a6 · outbound

This paper cites U., Khan, S., Khan, K., Khan, M.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights U., Khan, S., Khan, K., Khan, M

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.399288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 145ec3a8-7333-4a8c-9ae1-b36f00ca3c57 · outbound

This paper cites ”Recognition of inscribed cursive Pashtu numeral through optimized deep learning.” PeerJ Computer Science 10 (2024): e2124.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights ”Recognition of inscribed cursive Pashtu numeral through optimized deep learning.” PeerJ Computer Science 10 (2024): e2124

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:35:02.899344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 12a47dd7-f435-464e-82a9-cd004d3535af · outbound

This paper cites U., Ullah, I., Khan, A.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights U., Ullah, I., Khan, A

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.407264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bc2ec81-0798-430d-a17c-38e498d4dcac · outbound

This paper cites & Zhou, M.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights & Zhou, M

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:35:02.885431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0d2bff04-6b31-4538-b6b4-dc8b81361c40 · outbound

This paper cites & Khan, R.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights & Khan, R

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:35:02.871862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c6b3454f-761a-4f56-9bb6-e8d76b0a04e2 · outbound

This paper cites U., Chung, T.-S., Attique, M., Khan, K., El Khediri, S.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights U., Chung, T.-S., Attique, M., Khan, K., El Khediri, S

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:35:02.857940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3578f8ba-cc07-4138-a4b8-0ff53d2ac78b · outbound

This paper cites an unresolved cited work.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights Unresolved cited work

Reference 98

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:35:02.843552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 51adc2fa-f37f-4ee3-a7d6-b3494d21e828 · outbound

This paper cites an unresolved cited work.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights Unresolved cited work

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-10T21:35:01.428272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf730fd1-28ad-45f7-b00f-2376b8f66fa2 · outbound

This paper cites & Iˇsgum, I.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights & Iˇsgum, I

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:35:02.829800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 52cfd9ae-fbb7-436f-b3f0-7dfb765478a5 · outbound

This paper cites & Schuller, B.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights & Schuller, B

Reference 101

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:35:02.816669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 79e9794e-e5eb-43a0-a31b-5f54b6a7057b · outbound

This paper cites & Tan, R.

The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights & Tan, R

Reference 102

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:35:02.803537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers