Prenatal diagnosis of hypoplastic left heart syndrome on ultrasound using artificial intelligence: How does performance compare to a current screening programme?

Day TG, Budd S, Tan J, Matthew J, Skelton E, Jowett V, Lloyd D, Gomez A, Hajnal JV, Razavi R, Kainz B, Simpson JM (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Prenatal Diagnosis Wiley-Blackwell

DOI: 10.1002/pd.6445

Abstract

Background: Artificial intelligence (AI) has the potential to improve prenatal detection of congenital heart disease. We analysed the performance of the current national screening programme in detecting hypoplastic left heart syndrome (HLHS) to compare with our own AI model. Methods: Current screening programme performance was calculated from local and national sources. AI models were trained using four-chamber ultrasound views of the fetal heart, using a ResNet classifier. Results: Estimated current fetal screening programme sensitivity and specificity for HLHS were 94.3% and 99.985%, respectively. Depending on calibration, AI models to detect HLHS were either highly sensitive (sensitivity 100%, specificity 94.0%) or highly specific (sensitivity 93.3%, specificity 100%). Our analysis suggests that our highly sensitive model would generate 45,134 screen positive results for a gain of 14 additional HLHS cases. Our highly specific model would be associated with two fewer detected HLHS cases, and 118 fewer false positives. Conclusion: If used independently, our AI model performance is slightly worse than the performance level of the current screening programme in detecting HLHS, and this performance is likely to deteriorate further when used prospectively. This demonstrates that collaboration between humans and AI will be key for effective future clinical use.

Authors with CRIS profile

Bernhard Kainz

Involved external institutions

Evelina London Children's Hospital GB United Kingdom (GB) King’s College London GB United Kingdom (GB) Great Ormond Street Hospital (GOSH) GB United Kingdom (GB) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB)

How to cite

APA:

Day, T.G., Budd, S., Tan, J., Matthew, J., Skelton, E., Jowett, V.,... Simpson, J.M. (2023). Prenatal diagnosis of hypoplastic left heart syndrome on ultrasound using artificial intelligence: How does performance compare to a current screening programme? Prenatal Diagnosis. https://dx.doi.org/10.1002/pd.6445

MLA:

Day, Thomas G., et al. "Prenatal diagnosis of hypoplastic left heart syndrome on ultrasound using artificial intelligence: How does performance compare to a current screening programme?" Prenatal Diagnosis (2023).

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