Skip to main content

Automated vetting of radiology referrals: exploring natural language processing and traditional machine learning approaches

Abstract

Background

With a significant increase in utilisation of computed tomography (CT), inappropriate imaging is a significant concern. Manual justification audits of radiology referrals are time-consuming and require financial resources. We aimed to retrospectively audit justification of brain CT referrals by applying natural language processing and traditional machine learning (ML) techniques to predict their justification based on the audit outcomes.

Methods

Two human experts retrospectively analysed justification of 375 adult brain CT referrals performed in a tertiary referral hospital during the 2019 calendar year, using a cloud-based platform for structured referring. Cohen’s kappa was computed to measure inter-rater reliability. Referrals were represented as bag-of-words (BOW) and term frequency-inverse document frequency models. Text preprocessing techniques, including custom stop words (CSW) and spell correction (SC), were applied to the referral text. Logistic regression, random forest, and support vector machines (SVM) were used to predict the justification of referrals. A test set (300/75) was used to compute weighted accuracy, sensitivity, specificity, and the area under the curve (AUC).

Results

In total, 253 (67.5%) examinations were deemed justified, 75 (20.0%) as unjustified, and 47 (12.5%) as maybe justified. The agreement between the annotators was strong (κ = 0.835). The BOW + CSW + SC + SVM outperformed other binary models with a weighted accuracy of 92%, a sensitivity of 91%, a specificity of 93%, and an AUC of 0.948.

Conclusions

Traditional ML models can accurately predict justification of unstructured brain CT referrals. This offers potential for automated justification analysis of CT referrals in clinical departments.

Key points

  • • Unjustified exposure to CT scans increases lifetime radiation risk of stochastic effects.

  • • Along with budgetary needs, auditing justification of radiology referrals is time-consuming.

  • • CDS ensures audit consistency and less discrepancies between human experts.

  • • ML algorithms used the clinical indications section of radiology referrals for classification.

  • • ML algorithms can accurately predict justification of brain CT referrals.

Background

Computed tomography (CT) scans are associated with relatively high radiation doses, and as a result, patients are potentially at greater lifetime risk of developing a radiation-induced cancer [1]. Many patients undergo multiple CT scans; therefore, their cumulative risk of developing a radiation-induced cancer is significantly higher [2]. Since 2009, the number of CT examinations carried out in Ireland has almost doubled [3]. A similar trend is seen in the UK where the CT scan frequency between 2012 [4] and 2019 [5] increased by approximately 74%, while a 20% increase occurred in the USA in the 2006–2016 period [6]. Increasing CT frequency poses additional population dose burden, as CT is the largest dose contributor [7]. National audits from Northern Ireland [8], Sweden [9], and Luxembourg [10], report 6%, 19%, and 39% of unjustified CT examinations, respectively. Furthermore, individual local audits within Europe indicate poor justification practice with the rate of unjustified CT examinations between 7 and 30% [11,12,13,14]. To improve justification of CT examinations in the European Union, the European Commission recently funded a 3-year project to co-ordinate audits of justification of CT examinations and to develop a common methodology for auditing justification of CT referrals [15]. Avoiding unnecessary exposure to ionising radiation is the primary evidence-based intervention that reduces cancer risk [1, 2]. The information contained in radiology referrals is manually used during justification, for assessing their compliance with clinical imaging guidelines which aim to improve patient care through evidence-based recommendations of radiology resources. Clinical decision support (CDS) systems are not yet commonly used in radiology, and the unstructured form of electronic radiology referrals requires preprocessing.

Natural language processing (NLP) refers to a set of techniques for preparing text data and converting it into a structured form suitable for subsequent machine learning [16, 17]. It is common in NLP and machine learning research to represent natural text as a term frequency-inverse document frequency (TF-IDF) model, or a bag-of-words (BOW) model where feature values correspond to term frequency in a document. The BOW model converts each text document into an n-dimensional vector where n is the number of unique terms or vocabulary size, and the vector values are the occurrence counts or term frequency (TF) of those terms. It is usual to correct the counts of common words with an inverse document frequency (IDF) term, and the combination of both, TF-IDF, is a reasonable model for representing the importance of words in a text corpus [18]. Machine learning is an automated process of detecting underlying patterns within data. In predictive data analytics, classification models that identify the relationship between a set of descriptive features and a target feature on retrospectively collected data are built. When the ground truth labels are known, models are in the category of supervised machine learning [19]. Although traditional machine learning (ML) techniques are often surpassed by deep learning methods, they do offer high performance on smaller textual datasets with great potential for a real-time CDS [20,21,22]. Past research in the field of radiological NLP has mainly focused on the radiological report [16, 17, 23, 24]. There seems to be a lack of research on the use of radiological NLP in referring practices; hence, our study aims to explore the potential of CDS in radiology by applying NLP-based models to the most common CT referral nationally—brain CT [25] to predict justification according to audit outcomes. The optimal text preprocessing pipeline for unstructured clinical text in radiology referrals is not known. Different text preprocessing pipelines for unstructured brain CT referrals were developed. Subsequently, ML approaches for classification of CT referrals from a single tertiary referral hospital according to the vetting outcomes were evaluated.

Methods

This retrospective research underwent a local data protection impact assessment and was exempt from full ethics review. The research was granted an ethics exemption (REERN: LS E-21-82-Potocnik-Stowe). All anonymised data were encrypted and stored on a university cloud storage in a comma-separated values format. Data science tasks were performed using Python (version 3.7.11).

Data collection

The study included all adult brain CT examinations obtained from a tertiary referral hospital based in Dublin, Ireland, in a single calendar year (2019). Anonymised referrals were extracted from the radiology information system (Carestream Vue, Rochester, NY, USA). These included outpatient, inpatient, and emergency referrals with different numbers of referrals in each of these categories. For each referral, patient gender, patient age, scan priority level, and unstructured clinical indications were recorded. The referrals at this clinical site were handwritten and thus were manually transcribed in an encrypted Excel spreadsheet without alterations being made to the grammatical structure of the unstructured text.

Data annotation

The dataset was manually inspected and independently annotated by a consultant neuroradiologist (10 years of experience) and a radiographer (5 years of experience). Both annotators used xRefer (xWave, Dublin, Ireland), a cloud-based CDS tool for structured referring (version 1.12.1-uat) [26] based on referral criteria for imaging developed by the European Society of Radiology [27] in cooperation with the American College of Radiology (ACR) [28], to audit justification of the referrals. Given the patient baseline characteristics, xRefer outputs justification scores associated with imaging modalities and examination types for a selected structured clinical indication. Considering the ACR scoring system, each referral could fall into one of the three categories: justified (score: 7–9), maybe justified (score: 4–6), and unjustified (score: 0–3). This process of semi-automated vetting with xRefer comprised of four steps. Firstly, 79 (17.4%) referrals were discarded due to inadequate clinical information. Information was deemed inadequate if a structured clinical indication could not be extracted from the unstructured clinical text due to brief wording or excessive variations in interpretation. To obtain justification scores for each referral, patient gender and age were also included. Secondly, upon manual interpretation of the unstructured clinical text and identifying one or more structured clinical indications, the identified structured indication was found in the platform’s database via its search engine. The structured indication of interest was confirmed to produce justification scores. Thirdly, if the identified structured indication was not found in the xRefer’s database, or the CDS scores were unavailable for a selected structured indication, such referrals were labelled based on the consultant’s opinion. When two or more structured indications were identified on a single referral, only the highest scoring indication was taken into consideration for justification purposes. If applicable, an alternate, more appropriate imaging method was proposed. Lastly, discrepancies in labels obtained using xRefer’s CDS system were addressed individually by both annotators and a final decision on a label was made by consensus. The vetting pipeline is summarised in Fig. 1. The annotated referrals were grouped into five categories depending on adequacy of information and availability of CDS with xRefer: (1) all referrals, (2) referrals with adequate information, (3) referrals with adequate information and with CDS scores, (4) referrals with adequate information and without CDS scores, and (5) referrals with adequate information and no matching structured indication. Cohen’s kappa (κ) was computed for each group to determine the inter-rater agreement.

Fig. 1
figure 1

Semi-automated vetting pipeline for brain CT referrals

Classification task

Considering the categorisation of referrals in the national audits across Europe [8,9,10], and due to the nature of unstructured writing and a non-representative multiclass dataset, the referrals of questionable justification have been considered as unjustified. The new dataset served as an experimental dataset for binary classification.

Text preprocessing

The unstructured clinical indications within each referral represent features, as they contain the most information that contributes towards examination justification. The text was preprocessed using Python’s Natural Language Toolkit (NLTK, version 3.6.5) library. All sentences were converted to lowercase and tokenised with the NLTK WordTokenizer. A default stop word list, containing NLTK’s stop words and punctuation marks, was applied to filter redundant tokens. Furthermore, custom stop words were identified by manually inspecting each referral and a token list with associated counts. To reduce noise, unigrams with document frequency equal to one were ignored. Misspelled tokens were corrected with Pyspellchecker (version 0.6.2) incorporating a custom medical dictionary containing 442 clinical terms that were identified in our vocabulary. Rare clinical terms were replaced by one of their synonyms. Features were represented as a BoW model with CountVectorizer and a TF-IDF model with TfidfVectorizer from the Scikit-learn library.

Model evaluation

The experimental dataset for binary classification was randomly divided (80/20) into a training set and a test set. The training set contained 300 (80% of the whole dataset) referrals that were used to train the prediction models with default hyperparameter settings and balanced class weights to penalise misclassification errors for the minority class. The test set contained 75 (20% of the whole dataset) referrals that were used to demonstrate classifier performance on the new, previously unseen referrals. Weighted accuracy score, sensitivity, specificity, and area under the curve (AUC) were computed for each classifier. Scikit-learn (sklearn, version 1.0.1) was used to evaluate combinations of different text preprocessing techniques and classifiers.

Results

Justification audit

Table 1 demonstrates the kappa scores, as well as the number of referrals belonging to each group associated with information adequacy and availability of CDS with xRefer. There was a significant difference (p < 0.01) in the inter-rater agreement between referrals that fall under CDS and those without CDS.

Table 1 Referral grouping and associated inter-rater agreement between the two human experts

In total, 253 (67.5%) examinations were considered justified, 75 (20.0%) unjustified, and 47 (12.5%) maybe justified. In total, 96 (25.6%) CT scans could have been replaced by magnetic resonance imaging (MRI). Symptoms of dizziness, syncope/fainting, vision changes, chronic headache, headache in cancer patients, maxillofacial headache, sub-dural haemorrhage, tinnitus, and dementia may indicate a need for an MRI. All oncology referrals (6) were inappropriate, as they required a brain CT scan for non-small cell lung cancer (NSCLC) or prostate cancer staging post-treatment cycle. iGuide imaging guidelines suggest that a brain CT is justified in cases of NSCLC staging only if MRI is contraindicated, and neurological symptoms are present; however, there was no such information provided by the referrer. The absence of CDS scores for prostate cancer diagnosis and staging meant that such requests were determined by consultant opinion which concurred with iGuide recommendations for NSCLC imaging. Five (1.3%) patients needed a facial bone CT scan, rather than a brain CT scan: four patients had sustained facial trauma without accompanying suspicion of intracerebral and/or intracranial complication. One patient exhibited symptoms of cerebrospinal fluid leak post-lumbar decompression, and CT cisternography was more appropriate. Another patient with vocal cord paralysis for 12 weeks, should have had a brain and neck CT instead of brain CT according to iGuide. Three (0.8%) of the 375 CT examinations could have been replaced by ultrasound (US). Patients experiencing nausea and/or vomiting without known head injury should have undergone abdominal US scan initially. All indications, except for vision changes, specified on referrals of questionable justification indicate a need for a head MRI. Symptoms of vision changes indicate that an MRI of the orbits is the investigation of choice.

ML algorithms

Table 2 shows classifier performance in combination with different text preprocessing pipelines on the binary dataset. A combination of BOW representation and SVM outperformed the rest of the models. The best performing models were BOW + DSW + SVM and BOW + CSW + SC + SVM with superior AUC of 0.942 and 0.948, respectively.

Table 2 Binary classifier evaluation metrics on test set

Discussion

Based on the Kappa scores, our approach to auditing justification of radiology referrals with xRefer shows better consistency and less discrepancies between the two annotators, compared to auditing radiology referrals without CDS, regardless of the gap in clinical expertise and knowledge. Since the performance of a prediction model depends significantly on the quality of annotations, the audit findings suggest that xRefer can be used to conduct a retrospective, semi-automated, evidence-based justification analysis of CT referrals, although the platform has been primarily created for structured referring.

The rate of unjustified examinations (20.0%) and those of questionable justification (12.5%) indicate that clinical imaging guidelines have not been adapted and implemented within clinical practice adequately. Several factors, such as lack of awareness of clinical imaging guidelines, time, patient pressure, along with privatisation of healthcare and prioritising economic gain [29], may influence justification decisions. This correlates with our findings such as clinical indications of ‘investigations’ and ‘dizziness/Lt tinnitus’ specified on two unjustified referrals.

Regular audits of justification are necessary to ensure better implementation of clinical imaging guidelines, prevent unnecessary patient radiation dose, limit wasteful use of resources, and improve patient care. Retrospective audits are costly, time-consuming, and often not feasible. In this study, an iGuide interpreter was developed for automated justification analysis of clinical indications specified on brain CT referrals. The NLP model can automatically predict justification of brain CT referrals, consequently offering a potential for automated clinical text analysis that could be used as a CDS tool to assist referrers and practitioners with justifying brain CT referrals, retrospectively and in real time. This would allow more frequent, cost-free justification audits, and better implementation of imaging referral guidelines within clinical practice to ensure appropriate justification. To our knowledge, this is the first study evaluating ML methods in predicting justification of radiology referrals. Similar work has been done where NLP and ML approaches were applied to the conclusions section of CT reports to automatically predict downstream radiology resource utilisation in patients undergoing surveillance for hepatocellular carcinoma [21]. The study demonstrates that even with minimal text preprocessing, a linear model can achieve an accuracy of 92.2%. In contrast, our study involves CT referrals and more data preprocessing which may or may not improve classifier performance. Our linear model achieved a weighted accuracy of 92.8% with minimal data preprocessing and 92.2% after introducing custom stop words and spell correction with Pyspellchecker, but the second model had a higher sensitivity and AUC. There are three important data preprocessing elements that affect classifier performance:

  1. 1.

    Data representation—as a group, BOW models tend to outperform TF-IDF models. We note that terms associated with unjustified referrals also occur in justified referrals and vice versa (Table 3). In some cases, justified and unjustified terms occur together within a single referral. All of this neutralises the effect of a TF-IDF approach.

  2. 2.

    A classifier might deem clinically irrelevant terms important for classification of CT referrals and when these are ignored, the classifier’s performance may decrease. This also challenges machine learning ethics with the question of including clinically irrelevant terms to achieve better accuracy.

  3. 3.

    Spelling correction We corrected misspelled clinical terms with Pyspellchecker. The spell-checking algorithm uses Levenshtein distance and is limited to an edit distance of two; therefore, the algorithm is unable to correct misspelled terms if more than two permutations are needed. In some instances, misspelled terms were wrongly corrected, so the algorithm’s output was either ignored due to producing noise, or accepted as a spurious term. For example, one referral reads: “prostate cancer-re staging” and the algorithm corrected the sentence into “prostate sancerre staging”. On the other hand, a misspelled “eposide” was replaced with “epoxide” instead of “episode”. We note cases where the algorithm did not make any corrections as the authentic word was misspelled, given a context and yet, grammatically correct: “head ache” should have been combined into a single word “headache”.

Table 3 Document frequency of terms associated with justified and unjustified referrals

Implementing a custom stop word list and spell-checking algorithm may improve classifier performance. 43.5% of referrals in our training set were unjustified. Our test set contained 45 justified and 30 unjustified referrals. This is a mild degree of imbalance. SVM models tend to be less sensitive to class imbalance as they try defining a hyperplane that separates examples belonging to each class in a high-dimensional space, thereby achieving higher accuracy compared to other models which strive towards minimising the error rate [30].

In terms of false predictions, BOW + CSW + SC + SVM made two false-positive and four false-negative predictions. An analysis of false positives revealed that certain terms, such as “fall” and “headstrike”, downgraded the model’s performance because they are associated with both classes, more commonly with a positive class. When referrers provide clinical indications, a rationale needs to be included. Relatively often, the rationale is inappropriate or absent. For example:

  1. 1.

    Fall, headstrike, takes aspirin.

  2. 2.

    CT brain—fall headstrike uses NOAC.

  3. 3.

    Fall with headstrike—bruise L forehead.

A rationale behind the first two referrals justifies a brain CT scan as there is a high suspicion of intracerebral haemorrhage due to anticoagulant therapy. In contrary, a bruised forehead alone is not sufficient to justify a brain CT scan after falling; however, the third referral has been misclassified as a false positive.

In terms of false negatives, CT referrals include terms associated with both categories, but more frequently appear in unjustified referrals.

  1. 1.

    Ongoing headaches.

  2. 2.

    Fall down stairs HI Headaches.

  3. 3.

    Ongoing headaches over 12/12. The headaches tend to radiate from the back of his neck. ?Aetiology of headaches. The DDX include tension-type headaches, but the history of episodic blurred vision and ataxia make the diagnosis more difficult.

The first referral reads as a chronic headache and without any further rationale it was deemed as unjustified. The second referral raises a concern over a post-traumatic headache associated with a traumatic brain injury [28] which justifies a brain CT scan. The third referral, misclassified as a false negative, contains focal neurological symptoms in conjunction with a chronic headache; therefore, imaging with CT is indicated.

Our study had certain limitations. First, the benefits of CDS for auditing justification of CT referrals with xRefer were not demonstrated to a full extent, as the comparison groups of CDS and non-CDS referrals are significantly different in size. Second, our data were sourced from a single clinical site; therefore, it is premature to make assumptions that NLP-based models can generalise to unseen datasets. Third, as our dataset was small and imbalanced, referrals of questionable justification were considered as unjustified. While this is acceptable [8,9,10], it is desirable for an iGuide interpreter to classify referrals into one of the three possible classes as per iGuide categorisation. Fourth, trained models do not take patient gender, age, urgency level, and prior imaging into account. Lastly, the results of this study may not reflect true prediction capabilities as hyperparameters were not optimised. Nevertheless, the models achieved high accuracy regardless of the limitations of the dataset.

Conclusions

Unjustified exposure to CT scans is a global problem that increases lifetime radiation risk of stochastic effects, resource wastage, and CT waiting lists. Our NLP-based models can accurately predict justification of brain CT referrals and, as a result, offer potential for automation of this time-consuming, often costly process. More NLP research is needed to address justification of other types of CT scans and different imaging modalities to explore the potential of automated justification analysis in clinical departments.

Availability of data and materials

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

ACR:

American College of Radiology

AUC:

Area under the curve

BOW:

Bag-of-words

CDS:

Clinical decision support

CSW:

Custom stop words

CT:

Computed tomography

DSW:

Default stop words

LR:

Logistic regression

ML:

Traditional machine learning

MRI:

Magnetic resonance imaging

NLP:

Natural language processing

NLTK:

Natural language toolkit

NSCLC:

Non-small cell lung cancer

RF:

Random forest

SC:

Spell checker

SVM:

Support vector machine

TF-IDF:

Term frequency-inverse document frequency

US:

Ultrasound

References

  1. Shao Y-H, Tsai K, Kim S, Wu Y-J, Demissie K (2020) Exposure to tomographic scans and cancer risks. JNCI Cancer Spectr 4(1):pkz072. https://doi.org/10.1093/jncics/pkz072

    Article  PubMed  Google Scholar 

  2. Rehani MM, Yang K, Melick ER et al (2020) Patients undergoing recurrent CT scans: assessing the magnitude. Eur Radiol 30(4):1828–1836. https://doi.org/10.1007/s00330-019-06523-y

    Article  PubMed  Google Scholar 

  3. Gilligan P, Darcy L, Maguire G et al (2018) Irish national 2017 ct population and dose reference level survey: a novel gender and aged based survey using spreadsheet templates and clinical indications, EuroSafe imaging 2018. Austria Centre Vienna, 28 February–4 March. European Congress of Radiology, Vienna, pp 1–20. https://doi.org/10.1594/esi2018/ESI-0055

  4. National Health Service England (2013) Diagnostic imaging dataset statistical release: annual experimental statistics. The Government Statistical Service, London. https://www.england.nhs.uk/statistics/statistical-work-areas/diagnostic-imaging-dataset/diagnostic-imaging-dataset-2012-13-data-2/. Accessed 23 June 2021

  5. National Health Service England and National Health Service Improvement (2020) Diagnostic imaging dataset statistical release: provisional monthly statistics, September 2018 to September 2019. Performance Analysis Team, London. https://www.england.nhs.uk/statistics/wp-content/uploads/sites/2/2020/01/Provisional-Monthly-Diagnostic-Imaging-Dataset-Statistics-2020-01-23.pdf. Accessed 23 June 2021

  6. National Council on Radiation Protection and Measurements (NCRP) (2019) Report no. 184—medical radiation exposure of patients in the United States. Bethesda, MD: NCRP. https://ncrponline.org/shop/reports/report-no-184-medical-radiation-exposure-of-patients-in-the-united-states-2019/. Accessed 25 June 2021

  7. O’Connor C, Cunningham N, Kelleher K et al (2014) Radiation doses received by the Irish population. Radiological Protection Institute Ireland, Dublin

  8. Public Health England (2018) A retrospective review of justification of computed tomography examinations in Northern Ireland. Medical Exposures Group, London

    Google Scholar 

  9. Almen A, Wolfram L, Sven R (2009) National survey on justification of CT-examinations in Sweden. Swedish Radiation Safety Authority. https://inis.iaea.org/collection/NCLCollectionStore/_Public/40/029/40029225.pdf?r=1. Accessed 26 June 2021

  10. Bouëtté A, Karoussou-Schreiner A, Pointe HDL et al (2019) National audit on the appropriateness of CT and MRI examinations in Luxembourg. Insights Imaging 10(1):1–12. https://doi.org/10.1186/s13244-019-0731-9

    Article  Google Scholar 

  11. Sobiecka A, Bekiesińska-Figatowska M, Rutkowska M, Latos T, Walecki J (2016) Clinically unjustified diagnostic imaging—a worrisome tendency in today’s medical practice. Pol J Radiol 81:325–330. https://doi.org/10.12659/PJR.896847

    Article  PubMed  PubMed Central  Google Scholar 

  12. Dowley A, Foley SJ, Potočnik J et al (2021) A comparison of computed tomography (CT) justification practices between core hours and on-call hours in irish ct centres [presentation], RPS 200—professional issues: radioprotection. Austria Center Vienna. 3 March. https://connect.myesr.org/course/professional-issues-radioprotection/

  13. Oikarinen H, Meriläinen S, Pääkkö E, Karttunen A, Nieminen MT, Tervonen O (2009) Unjustified CT examinations in young patients. Eur Radiol 19(5):1161–1165. https://doi.org/10.1007/s00330-008-1256-7

    Article  PubMed  Google Scholar 

  14. Vilar-Palop J, Hernández-Aguado I, Maria P-V, Vilar J (2018) Appropriate use of medical imaging in two Spanish public hospitals: a cross-sectional analysis. BMJ Open 8(3):1–11. https://doi.org/10.1136/bmjopen-2017-019535

    Article  Google Scholar 

  15. European Commission (2020) ENER/D3/2020-74 European co-ordinated action on improving justification of computed tomography. https://etendering.ted.europa.eu/cft/cft-display.html?cftId=6801/ Accessed 17 July 2021

  16. Cai T, Giannopoulos AA, Yu S (2016) Natural language processing technologies in radiology research and clinical applications. Radiographics 36(1):176–191. https://doi.org/10.1148/rg.2016150080

    Article  PubMed  Google Scholar 

  17. Pons E, Braun LMM, Myriam Hunink MG, Kors JA (2016) Natural language processing in radiology: a systematic review. Radiology 279(2):329–343. https://doi.org/10.1148/radiol.16142770

    Article  PubMed  Google Scholar 

  18. Fattahi J, Mejri M (2021) SpaML: a bimodal ensemble learning spam detector based on NLP techniques. In: IEEE 5th international conference on cryptography, security and privacy (CSP). Beijing Normal University, 8–10 January. IEEE, Zhuhai, pp 107–112. https://doi.org/10.1109/CSP51677.2021.9357595

  19. Kelleher JD, Mac Namee B, D’Arcy A (2015) Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. The MIT Press, London

    Google Scholar 

  20. Carrodeguas E, Lacson R, Swanson W, Khorasani R (2018) Use of machine learning to identify follow-up recommendations in radiology reports. J Am Coll Radiol 16(3):336-343.g. https://doi.org/10.1016/j.jacr.2018.10.020

    Article  PubMed  PubMed Central  Google Scholar 

  21. Brown AD, Kachura JR (2019) Natural language processing of radiology reports in patients with hepatocellular carcinoma to predict radiology resource utilization. J Am Coll Radiol 16(6):840–844. https://doi.org/10.1016/j.jacr.2018.12.004

    CAS  Article  PubMed  Google Scholar 

  22. Li MD, Lang M, Deng F et al (2021) Analysis of stroke detection during the COVID-19 pandemic using natural language processing of radiology reports. AJNR Am J Neuroradiol 42(3):429–434. https://doi.org/10.3174/ajnr.A6961

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. Casey A, Davidson E, Poon M et al (2021) A systematic review of natural language processing applied to radiology reports. BMC Med Inform Decis Mak 21(1):179–216. https://doi.org/10.1186/s12911-021-01533-7

    Article  PubMed  PubMed Central  Google Scholar 

  24. Sorin V, Barash Y, Konen E, Klang E (2020) Deep learning for natural language processing in radiology—fundamentals and a systematic review. J Am Coll Radiol 17(5):639–648. https://doi.org/10.1016/j.jacr.2019.12.026

    Article  PubMed  Google Scholar 

  25. Radiological Protection Institute Ireland (RPII) (2014) Radiation doses received by the Irish population. RPII, Dublin. https://inis.iaea.org/collection/NCLCollectionStore/_Public/46/045/46045338.pdf. Accessed 14 July 2021

  26. xWave Technologies (2021) INTRODUCING: xRefer. https://www.xwave.ie/. Accessed 17 July 2021

  27. European Society of Radiology (2021) ESR iGuide. https://www.esriguide.org/. Accessed 17 July 2021

  28. American College of Radiology (2021) ACR appropriateness criteria. https://www.acr.org/Clinical-Resources/ACR-Appropriateness-Criteria

  29. Gransjøen AM, Wiig S, Lysdahl KB, Hofmann BM (2018) Barriers and facilitators for guideline adherence in diagnostic imaging: an explorative study of GPs’ and radiologists’ perspectives. BMC Health Serv Res 18(1):556–563. https://doi.org/10.1186/s12913-018-3372-7

    Article  PubMed  PubMed Central  Google Scholar 

  30. Drucker H, Donghui W, Vapnik VN (1999) Support vector machines for spam categorization. IEEE Trans Neural Netw 10(5):1048–1054. https://doi.org/10.1109/72.788645

    CAS  Article  PubMed  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

Authors

Contributions

JP helped in substantial contributions to the conception and design of the work (putting a project idea into action, designing project methodology), transcribing anonymised data, data analysis (auditing justification of brain CT referrals, AI tasks), data interpretation (audit outcomes, classifier evaluation), drafting the work, proofreading, final approval of the version to be published, agreement to be accountable for all aspects of the work. ET contributed to substantial contribution to the design of the work (advising on CT and justification literature review, methodology, and discussion), critical revision, proofreading, final approval of the version to be published, agreement to be accountable for all aspects of the work. RK performed substantial contribution to the design of the work (introducing xWave, xWave access), data analysis (auditing justification of brain CT referrals), critical revision, final approval of the version to be published, agreement to be accountable for all aspects of the work. SF was involved in substantial contribution to the design of the work (advising on CT and justification literature review and discussion), critical revision, proofreading, final approval of the version to be published, agreement to be accountable for all aspects of the work. AL helped in substantial contribution to the design of the work (advising on NLP and ML literature review, methodology, and discussion), critical revision, final approval of the version to be published, agreement to be accountable for all aspects of the work. JS contributed to substantial contribution to the conception of the work (project idea). Since the co-author is deceased, a final approval of the version to be published could not be obtained, as well as agreement to be accountable for all aspects of work. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jaka Potočnik.

Ethics declarations

Ethical approval and consent to participate

This retrospective research underwent a local data protection impact assessment and was exempt from full ethics review. The research was granted an ethics exemption from the Human research Ethics Committee—Sciences (REERN: LS-E-21-82-Potocnik-Stowe).

Consent for publication

Not applicable.

Competing interests

RK discloses competing interests regarding his involvement with xWave. All remaining authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Potočnik, J., Thomas, E., Killeen, R. et al. Automated vetting of radiology referrals: exploring natural language processing and traditional machine learning approaches. Insights Imaging 13, 127 (2022). https://doi.org/10.1186/s13244-022-01267-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13244-022-01267-8

Keywords

  • Machine learning
  • Natural language processing
  • Justification audit
  • Radiology referral
  • Clinical decision support