@article{4e0b98bf6ab643c08e5958b435e905cd,
title = "Prenatal diagnosis of hypoplastic left heart syndrome on ultrasound using artificial intelligence: How does performance compare to a current screening programme?",
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.",
author = "Day, {Thomas G.} and Samuel Budd and Jeremy Tan and Jacqueline Matthew and Emily Skelton and Victoria Jowett and David Lloyd and Alberto Gomez and Hajnal, {Jo V.} and Reza Razavi and Bernhard Kainz and Simpson, {John M.}",
note = "Funding Information: We would like to thank the expert clinicians of the Evelina London Children's Hospital Fetal Cardiology Unit, who performed the ultrasound scans used in this study, and also recorded many of the data points used in the estimates of current UK screening performance. This work was supported by the Wellcome Trust [IEH Award, 102431], by core funding from the Wellcome/EPSRC Centre for Medical Engineering [WT203148/Z/16/Z] and by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London and the NIHR Clinical Research Facility. TGD and JM are supported by NIHR Doctoral Fellowships (NIHR301448 and NIHR300555 respectively). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. The funding bodies had no influence in the study design, data collection, analysis, interpretation, preparation of the manuscript, or decision to submit or publish. Publisher Copyright: {\textcopyright} 2023 The Authors. Prenatal Diagnosis published by John Wiley & Sons Ltd.",
year = "2023",
doi = "10.1002/pd.6445",
language = "English",
journal = "Prenatal Diagnosis",
issn = "0197-3851",
publisher = "John Wiley and Sons Ltd",
}
