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

Author:

Day Thomas G.12ORCID,Budd Samuel2,Tan Jeremy2,Matthew Jacqueline2,Skelton Emily23,Jowett Victoria4,Lloyd David12,Gomez Alberto2,Hajnal Jo V.2,Razavi Reza12,Kainz Bernhard56,Simpson John M.12

Affiliation:

1. Department of Congenital Heart Disease Evelina Children's Healthcare Guy's and St Thomas' NHS Foundation Trust London UK

2. School of Biomedical Engineering and Imaging Sciences King's College London London UK

3. School of Health Sciences University of London London UK

4. Great Ormond Street Hospital for Children NHS Foundation Trust London UK

5. Department of Computing Imperial College London London UK

6. Department of Artificial Intelligence in Biomedical Engineering Friedrich‐Alexander University Erlangen‐Nürnberg Erlangen Germany

Abstract

AbstractBackgroundArtificial 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.MethodsCurrent 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.ResultsEstimated 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.ConclusionIf 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.

Funder

Wellcome Trust

Publisher

Wiley

Subject

Genetics (clinical),Obstetrics and Gynecology

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