Rule-based natural language processing for automation of stroke data extraction: a validation study.

PURPOSE: Data extraction from radiology free-text reports is time consuming when performed manually. Recently, more automated extraction methods using natural language processing (NLP) are proposed. A previously developed rule-based NLP algorithm showed promise in its ability to extract stroke-related data from radiology reports. We aimed to externally validate the accuracy of CHARTextract, a rule-based NLP algorithm, to extract stroke-related data from free-text radiology reports. METHODS: Free-text reports of CT angiography (CTA) and perfusion (CTP) studies of consecutive patients with acute ischemic stroke admitted to a regional stroke center for endovascular thrombectomy were analyzed from January 2015 to 2021. Stroke-related variables were manually extracted as reference standard from clinical reports, including proximal and distal anterior circulation occlusion, posterior circulation occlusion, presence of ischemia or hemorrhage, Alberta stroke program early CT score (ASPECTS), and collateral status. These variables were simultaneously extracted using a rule-based NLP algorithm. The NLP algorithm's accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) were assessed. RESULTS: The NLP algorithm's accuracy was > 90% for identifying distal anterior occlusion, posterior circulation occlusion, hemorrhage, and ASPECTS. Accuracy was 85%, 74%, and 79% for proximal anterior circulation occlusion, presence of ischemia, and collateral status respectively. The algorithm confirmed the absence of variables from radiology reports with an 87-100% accuracy. CONCLUSIONS: Rule-based NLP has a moderate to good performance for stroke-related data extraction from free-text imaging reports. The algorithm's accuracy was affected by inconsistent report styles and lexicon among reporting radiologists.

The Impact of the COVID-19 Pandemic on Radiology Resident Education: Where Do We Go From Here?

The Coronavirus Disease of 2019 (COVID-19) pandemic caused a dramatic shift in radiology resident education. Primarily, physical distancing prompted a general transition to virtual learning. Common changes made by radiology residency programs included virtual rounds and readouts, the use of simulation technology, and case-based learning which utilized pedagogical approaches such as the flipped classroom for teaching residents. Virtual learning appears to be a suitable alternative to traditional, in-person learning, and may have a place post-pandemic as part of a blended curriculum with in-person and virtual components. The extent of disruption to radiology resident education varied based on the local impact of COVID-19 and the prevalence of redeployment, as did residents' mental health and wellbeing. Accessibility of mental health resources for residents was highlighted as an issue that programs need to address during these difficult times. Moreover, the pandemic resulted in unavoidable reductions in procedural exposure which programs mitigated through the use of simulation technologies and virtual learning resources. Professional development activities such as mentorship and career planning were also dramatically impacted by the pandemic and remains a challenge that programs need to consider moving forward post-pandemic. The purpose of this review is to outline the changes made to radiology resident education as a result of the COVID-19 pandemic and suggest what changes may be worthwhile to continue.

Hepatic perfusional changes on CT and MRI: a radiology primer.

Hepatic perfusional changes are common in response to, or as a result of, a multitude of pathological processes. These can be neoplastic, inflammatory, fibrotic, or ischemic in origin, to name a few. The liver, having a dual blood supply, is a unique organ to study using contrast-enhanced CT and MRI imaging due to its varied appearance on multiphasic imaging. Knowledge of the CT and MRI appearance of hepatic perfusional changes, in addition to the clinical presentation, can often result in an accurate differential diagnosis. Many of the conditions that cause these changes in hepatic blood flow result in similar appearances on imaging. As a result, it is important that radiologists be aware of common pitfalls when dealing with hepatic perfusional changes to prevent misdiagnosis or delayed diagnosis. As such, this review will focus on some of the various causes of hepatic perfusional changes and how to accurately identify and diagnose them based on their CT and MRI appearance.