Journal article
The Hydronephrosis Severity Index guides paediatric antenatal hydronephrosis management based on artificial intelligence applied to ultrasound images alone
Scientific reports, Vol.14(1), 22748
10/01/2024
DOI: 10.1038/s41598-024-72271-9
PMCID: PMC11442661
PMID: 39349526
Abstract
Antenatal hydronephrosis (HN) impacts up to 5% of pregnancies and requires close, frequent follow-up monitoring to determine who may benefit from surgical intervention. To create an automated HN Severity Index (HSI) that helps guide clinical decision-making directly from renal ultrasound images. We applied a deep learning model to paediatric renal ultrasound images to predict the need for surgical intervention based on the HSI. The model was developed and studied at four large quaternary free-standing paediatric hospitals in North America. We evaluated the degree to which HSI corresponded with surgical intervention at each hospital using area under the receiver-operator curve, area under the precision-recall curve, sensitivity, and specificity. HSI predicted subsequent surgical intervention with > 90% AUROC, > 90% sensitivity, and > 70% specificity in a test set of 202 patients from the same institution. At three external institutions, HSI corresponded with AUROCs ≥ 90%, sensitivities ≥ 80%, and specificities > 50%. It is possible to automatically and reliably assess HN severity directly from a single ultrasound. The HSI stratifies low- and high-risk HN patients thus helping to triage low-risk patients while maintaining very high sensitivity to surgical cases. HN severity can be predicted from a single patient ultrasound using a novel image-based artificial intelligence system.
Details
- Title: Subtitle
- The Hydronephrosis Severity Index guides paediatric antenatal hydronephrosis management based on artificial intelligence applied to ultrasound images alone
- Creators
- Lauren Erdman - IntelMandy Rickard - Hospital for Sick ChildrenErik Drysdale - Hospital for Sick ChildrenMarta Skreta - Allen Institute for Artificial IntelligenceStanley Bryan Hua - University of TorontoKunj Sheth - Lucile Packard Children's HospitalDaniel Alvarez - Lucile Packard Children's HospitalKyla N Velaer - Lucile Packard Children's HospitalMichael E Chua - Hospital for Sick ChildrenJoana Dos Santos - Hospital for Sick ChildrenDaniel Keefe - Hospital for Sick ChildrenNorman D Rosenblum - University of TorontoMegan A Bonnett - Lucile Packard Children's HospitalJohn Weaver - Children's Hospital of PhiladelphiaAlice Xiang - Children's Hospital of PhiladelphiaYong Fan - University of PennsylvaniaBernarda Viteri - Children's Hospital of PhiladelphiaChristopher S Cooper - University of IowaGregory E Tasian - Children's Hospital of PhiladelphiaArmando J Lorenzo - University of TorontoAnna Goldenberg - Allen Institute for Artificial Intelligence
- Resource Type
- Journal article
- Publication Details
- Scientific reports, Vol.14(1), 22748
- DOI
- 10.1038/s41598-024-72271-9
- PMID
- 39349526
- PMCID
- PMC11442661
- NLM abbreviation
- Sci Rep
- ISSN
- 2045-2322
- eISSN
- 2045-2322
- Publisher
- NATURE PORTFOLIO
- Grant note
We would like to thank the Bitove Family and the Hospital for Sick Children's Women's Auxilliary Volunteers for their generous financial support for this project.
- Language
- English
- Date published
- 10/01/2024
- Academic Unit
- Stead Family Department of Pediatrics; Urology; Medicine Administration
- Record Identifier
- 9984721239502771
Metrics
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