Neural classification of Norwegian radiology reports: using NLP to detect findings in CT-scans of children

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10441634" target="_blank" >RIV/00216208:11320/21:10441634 - isvavai.cz</a>

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=pVsFtbjlfn" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=pVsFtbjlfn</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s12911-021-01451-8" target="_blank" >10.1186/s12911-021-01451-8</a>

Result language

angličtina

Original language name

Neural classification of Norwegian radiology reports: using NLP to detect findings in CT-scans of children

Original language description

Background With a motivation of quality assurance, machine learning techniques were trained to classify Norwegian radiology reports of paediatric CT examinations according to their description of abnormal findings. Methods 13.506 reports from CT-scans of children, 1000 reports from CT scan of adults and 1000 reports from X-ray examination of adults were classified as positive or negative by a radiologist, according to the presence of abnormal findings. Inter-rater reliability was evaluated by comparison with a clinician&apos;s classifications of 500 reports. Test-retest reliability of the radiologist was performed on the same 500 reports. A convolutional neural network model (CNN), a bidirectional recurrent neural network model (bi-LSTM) and a support vector machine model (SVM) were trained on a random selection of the children&apos;s data set. Models were evaluated on the remaining CT-children reports and the adult data sets. Results Test-retest reliability: Cohen&apos;s Kappa = 0.86 and F1 = 0.919. Inter-rater reliability: Kappa = 0.80 and F1 = 0.885. Model performances on the Children-CT data were as follows. CNN: (AUC = 0.981, F1 = 0.930), bi-LSTM: (AUC = 0.978, F1 = 0.927), SVM: (AUC = 0.975, F1 = 0.912). On the adult data sets, the models had AUC around 0.95 and F1 around 0.91. Conclusions The models performed close to perfectly on its defined domain, and also performed convincingly on reports pertaining to a different patient group and a different modality. The models were deemed suitable for classifying radiology reports for future quality assurance purposes, where the fraction of the examinations with abnormal findings for different sub-groups of patients is a parameter of interest.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30224 - Radiology, nuclear medicine and medical imaging

Project

—

Continuities

—

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

BMC Medical Informatics and Decision Making

ISSN

1472-6947

e-ISSN

—

Volume of the periodical

21

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

8

Pages from-to

84

UT code for WoS article

000626515600002

EID of the result in the Scopus database

2-s2.0-85102700796