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

• Published in: BMC medical informatics and decision making Vol. 21; no. 1; p. 84
• Main Authors: Dahl, Fredrik A, Rama, Taraka, Hurlen, Petter, Brekke, Pål H, Husby, Haldor, Gundersen, Tore, Nytrø, Øystein, Øvrelid, Lilja
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 04-03-2021; BioMed Central; BMC
• Subjects: Adult; Adults; Analysis; Artificial intelligence; Artificial neural networks; Child; Children; Classification; Computational linguistics; Computed tomography; Datasets; Diagnosis, Radioscopic; Evaluation; Health aspects; Humans; Language processing; Learning algorithms; Machine learning; Motivation; Natural language interfaces; Natural language processing; Neural networks; Neural Networks, Computer; Pediatric radiology; Pediatrics; Performance evaluation; Quality assurance; Quality control; Radiography; Radiology; Recurrent neural networks; Reliability analysis; Reproducibility; Reproducibility of Results; Support vector machines; Tomography; Tomography, X-Ray Computed; X-ray computed; Norway; Tomography; Natural language processing; Reproducibility of results; X-ray computed; Machine learning
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-021-01451-8

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.
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'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's data set. Models were evaluated on the remaining CT-children reports and the adult data sets.
Test-retest reliability: Cohen'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.
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.