Promises of artificial intelligence in neuroradiology: a systematic technographic review.

PURPOSE: To conduct a systematic review of the possibilities of artificial intelligence (AI) in neuroradiology by performing an objective, systematic assessment of available applications. To analyse the potential impacts of AI applications on the work of neuroradiologists. METHODS: We identified AI applications offered on the market during the period 2017-2019. We systematically collected and structured information in a relational database and coded for the characteristics of the applications, their functionalities for the radiology workflow and their potential impacts in terms of 'supporting', 'extending' and 'replacing' radiology tasks. RESULTS: We identified 37 AI applications in the domain of neuroradiology from 27 vendors, together offering 111 functionalities. The majority of functionalities 'support' radiologists, especially for the detection and interpretation of image findings. The second-largest group of functionalities 'extends' the possibilities of radiologists by providing quantitative information about pathological findings. A small but noticeable portion of functionalities seek to 'replace' certain radiology tasks. CONCLUSION: Artificial intelligence in neuroradiology is not only in the stage of development and testing but also available for clinical practice. The majority of functionalities support radiologists or extend their tasks. None of the applications can replace the entire radiology profession, but a few applications can do so for a limited set of tasks. Scientific validation of the AI products is more limited than the regulatory approval.

Contextual Structured Reporting in Radiology: Implementation and Long-Term Evaluation in Improving the Communication of Critical Findings.

Structured reporting contributes to the completeness of radiology reports and improves quality. Both the content and the structure are essential for successful implementation of structured reporting. Contextual structured reporting is tailored to a specific scenario and can contain information retrieved from the context. Critical findings detected by imaging need urgent communication to the referring physician. According to guidelines, the occurrence of this communication should be documented in the radiology reports and should contain when, to whom and how was communicated. In free-text reporting, one or more of these required items might be omitted. We developed a contextual structured reporting template to ensure complete documentation of the communication of critical findings. The WHEN and HOW items were included automatically, and the insertion of the WHO-item was facilitated by the template. A pre- and post-implementation study demonstrated a substantial improvement in guideline adherence. The template usage improved in the long-term post-implementation study compared with the short-term results. The two most often occurring categories of critical findings are "infection / inflammation" and "oncology", corresponding to the a large part of urgency level 2 (to be reported within 6 h) and level 3 (to be reported within 6 days), respectively. We conclude that contextual structured reporting is feasible for required elements in radiology reporting and for automated insertion of context-dependent data. Contextual structured reporting improves guideline adherence for communication of critical findings.

The natural language processing of radiology requests and reports of chest imaging: Comparing five transformer models' multilabel classification and a proof-of-concept study.

BACKGROUND: Radiology requests and reports contain valuable information about diagnostic findings and indications, and transformer-based language models are promising for more accurate text classification. METHODS: In a retrospective study, 2256 radiologist-annotated radiology requests (8 classes) and reports (10 classes) were divided into training and testing datasets (90% and 10%, respectively) and used to train 32 models. Performance metrics were compared by model type (LSTM, Bertje, RobBERT, BERT-clinical, BERT-multilingual, BERT-base), text length, data prevalence, and training strategy. The best models were used to predict the remaining 40,873 cases' categories of the datasets of requests and reports. RESULTS: The RobBERT model performed the best after 4000 training iterations, resulting in AUC values ranging from 0.808 [95% CI (0.757-0.859)] to 0.976 [95% CI (0.956-0.996)] for the requests and 0.746 [95% CI (0.689-0.802)] to 1.0 [95% CI (1.0-1.0)] for the reports. The AUC for the classification of normal reports was 0.95 [95% CI (0.922-0.979)]. The predicted data demonstrated variability of both diagnostic yield for various request classes and request patterns related to COVID-19 hospital admission data. CONCLUSION: Transformer-based natural language processing is feasible for the multilabel classification of chest imaging request and report items. Diagnostic yield varies with the information in the requests.

Correlation between serum ferritin levels and liver iron concentration determined by MR imaging: impact of hematologic disease and inflammation.

Liver iron concentration was determined in 28 patients by magnetic resonance imaging using the method of Gandon et al. (Non-invasive assessment of hepatic iron stores by MRI. Lancet 2004;363:357-362). The result showed a significant correlation with blood plasma ferritin content (Spearman's r=.66; P<.001) and a slightly improving correlation coefficient when limited to those patients not known to have inflammation (r=.82; n=17; P<.001). Zooming in on patients with hematologic disease also had a beneficial effect on the correlation between liver iron content and plasma ferritin level (r=.79; n=13; P=.001). It is concluded that in patients without inflammation and in patients with hematologic disease, the content of ferritin in blood is a better predictor of liver iron content than in other patient categories.

Non-invasive liver iron concentration measurement by MRI: comparison of two validated protocols.

In the non-invasive determination of the liver iron concentration several validated MRI methods are available, two of which are compared in this study. Twenty-eight patients were examined by MRI and evaluated by the methods of Kreeftenberg et al. [Kreeftenberg Jr HG, Mooyaart EL, Huizenga JR, Sluiter WJ, Kreeftenberg Sr HG. Quantification of liver iron concentration with magnetic resonance imaging by combining T1-, T2-weighted spin echo sequences and a gradient echo sequence. Neth J Med 2000;56:133-7] and Gandon et al. [Gandon Y, Olivie D, Guyader D, et al. Non-invasive assessment of hepatic iron stores by MRI. Lancet 2004;363:357-62]. It is concluded that the latter shows a better inter- and intra-observer correlation and is more accurate because of the automated preselection of one of five sequences most sensitive in the estimated liver iron concentration range. In the Kreeftenberg method combining the results of three suboptimal sequences, leads to underestimation of the liver iron concentration.