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Diagnostic accuracy of an automated artificial intelligence derived right ventricular to left ventricular diameter ratio tool on CT pulmonary angiography to predict pulmonary hypertension at right heart catheterisation.

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Bibliographic Details
Title: Diagnostic accuracy of an automated artificial intelligence derived right ventricular to left ventricular diameter ratio tool on CT pulmonary angiography to predict pulmonary hypertension at right heart catheterisation.
Authors: Charters PFP; Department of Radiology, Royal United Hospitals Bath NHS Foundation Trust, Bath, UK., Rossdale J; Department of Respiratory Medicine, Royal United Hospitals Bath NHS Foundation Trust, Bath, UK; Department of Pharmacy and Pharmacology, University of Bath, UK., Brown W; Department of Radiology, Royal United Hospitals Bath NHS Foundation Trust, Bath, UK., Burnett TA; Department of Radiology, Royal United Hospitals Bath NHS Foundation Trust, Bath, UK., Komber HMEI; Department of Radiology, Royal United Hospitals Bath NHS Foundation Trust, Bath, UK., Thompson C; Department of Radiology, Royal United Hospitals Bath NHS Foundation Trust, Bath, UK., Robinson G; Department of Radiology, Royal United Hospitals Bath NHS Foundation Trust, Bath, UK., MacKenzie Ross R; Department of Respiratory Medicine, Royal United Hospitals Bath NHS Foundation Trust, Bath, UK., Suntharalingam J; Department of Respiratory Medicine, Royal United Hospitals Bath NHS Foundation Trust, Bath, UK; Department of Pharmacy and Pharmacology, University of Bath, UK., Rodrigues JCL; Department of Radiology, Royal United Hospitals Bath NHS Foundation Trust, Bath, UK; Department for Health, University of Bath, Bath, UK. Electronic address: j.rodrigues1@nhs.net.
Source: Clinical radiology [Clin Radiol] 2022 Jul; Vol. 77 (7), pp. e500-e508. Date of Electronic Publication: 2022 Apr 26.
Publication Type: Journal Article
Journal Info: Publisher: Blackwell Scientific Publications Ltd Country of Publication: England NLM ID: 1306016 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1365-229X (Electronic) Linking ISSN: 00099260 NLM ISO Abbreviation: Clin Radiol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1365-229X
DOI:10.1016/j.crad.2022.03.009