Performance of a 3D convolutional neural network in the detection of hypoperfusion at CT pulmonary angiography in patients with chronic pulmonary embolism : a feasibility study

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http://hdl.handle.net/10138/334685

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Vainio , T J V , Mäkelä , T O , Savolainen , S & Kangasniemi , M M 2021 , ' Performance of a 3D convolutional neural network in the detection of hypoperfusion at CT pulmonary angiography in patients with chronic pulmonary embolism : a feasibility study ' , European radiology experimental , vol. 5 , no. 1 , 45 . https://doi.org/10.1186/s41747-021-00235-z

Titel: Performance of a 3D convolutional neural network in the detection of hypoperfusion at CT pulmonary angiography in patients with chronic pulmonary embolism : a feasibility study
Författare: Vainio, Tuomas J V; Mäkelä, Teemu Olavi; Savolainen, Sauli; Kangasniemi, Marko Matti
Upphovmannens organisation: Doctoral Programme in Clinical Research
HUS Medical Imaging Center
Doctoral Programme in Materials Research and Nanosciences
Department of Diagnostics and Therapeutics
Department of Physics
Helsinki In Vivo Animal Imaging Platform (HAIP)
University Management
Sauli Savolainen / Principal Investigator
Clinicum
Datum: 2021-09-24
Språk: eng
Sidantal: 12
Tillhör serie: European radiology experimental
ISSN: 2509-9280
DOI: https://doi.org/10.1186/s41747-021-00235-z
Permanenta länken (URI): http://hdl.handle.net/10138/334685
Abstrakt: Background Chronic pulmonary embolism (CPE) is a life-threatening disease easily misdiagnosed on computed tomography. We investigated a three-dimensional convolutional neural network (CNN) algorithm for detecting hypoperfusion in CPE from computed tomography pulmonary angiography (CTPA). Methods Preoperative CTPA of 25 patients with CPE and 25 without pulmonary embolism were selected. We applied a 48%-12%-40% training-validation-testing split (12 positive and 12 negative CTPA volumes for training, 3 positives and 3 negatives for validation, 10 positives and 10 negatives for testing). The median number of axial images per CTPA was 335 (min-max, 111-570). Expert manual segmentations were used as training and testing targets. The CNN output was compared to a method in which a Hounsfield unit (HU) threshold was used to detect hypoperfusion. Receiver operating characteristic area under the curve (AUC) and Matthew correlation coefficient (MCC) were calculated with their 95% confidence interval (CI). Results The predicted segmentations of CNN showed AUC 0.87 (95% CI 0.82-0.91), those of HU-threshold method 0.79 (95% CI 0.74-0.84). The optimal global threshold values were CNN output probability >= 0.37 and
Subject: ATTENUATION
COMPUTED-TOMOGRAPHY
Computed tomography angiography
DIAGNOSIS
DISEASE
Deep learning
Feasibility Studies
HYPERTENSION
Neural networks (computer)
PERFUSION
PLATFORM
Pulmonary embolism
THROMBOEMBOLISM
114 Physical sciences
Referentgranskad: Ja
Licens: cc_by
Användningsbegränsning: openAccess
Parallelpublicerad version: publishedVersion


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