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Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test.

ABSTRACT:

SUBMITTER: Soltan AAS

PROVIDER: S-EPMC7831998 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test.

Soltan Andrew A S AAS Kouchaki Samaneh S Zhu Tingting T Kiyasseh Dani D Taylor Thomas T Hussain Zaamin B ZB Peto Tim T Brent Andrew J AJ Eyre David W DW Clifton David A DA

The Lancet. Digital health 20201211 2

<h4>Background</h4>The early clinical course of COVID-19 can be difficult to distinguish from other illnesses driving presentation to hospital. However, viral-specific PCR testing has limited sensitivity and results can take up to 72 h for operational reasons. We aimed to develop and validate two early-detection models for COVID-19, screening for the disease among patients attending the emergency department and the subset being admitted to hospital, using routinely collected health-care data (la ...[more]

PMID: 33509388

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Project description:AimTo develop and evaluate an artificial intelligence triage system with high sensitivity for detecting referable diabetic retinopathy and maculopathy, while maintaining high specificity for non-referable disease, for clinical implementation within the New Zealand national diabetic retinopathy screening programme.MethodsThe THEIA™ artificial intelligence system for retinopathy and maculopathy screening, was developed at Toku Eyes using routinely collected retinal screening datasets from two of the largest district health boards in Auckland, New Zealand: the Auckland District Health Board and the Counties Manukau District Health Board. All retinal images from consecutive individuals receiving retinal screening between January 2009 and December 2018 were used. Images were labelled as non-sight-threatening, potentially referable or sight-threatening for New Zealand implementation, or as referable (potentially referable + sight-threatening)/non-referable (non-sight-threatening) for global comparison.ResultsData from 32 354 unique people with diabetes (63 843 when including multiple visits) were available, of which 95-97%, 0.9-2.4% and 1.1-3.1% were categorized as non-sight-threatening, potentially referable and sight-threatening, respectively. Using the referable/non-referable categories, THEIA achieved overall sensitivity of 94% (95% CI 92-95) in the Auckland District Health Board and 95% (95% CI 92-97) in the Counties Manukau District Health Board datasets, while preserving specificity of 63% (95% CI 62-64) for the Auckland District Health Board and 61% (95% CI 60-62) for the Counties Manukau District Health Board. Implementing THEIA into a New Zealand national diabetic screening programme could significantly reduce the manual grading load.ConclusionTHEIA, an artificial intelligence tool to assist in clinical decision-making, tailored to the needs of the New Zealand national diabetic screening programme, delivered high sensitivity for detecting referable retinopathy within the multi-ethnic New Zealand population with diabetes.

Dataset Information

Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test.

Publications

Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test.

Similar Datasets

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