• Digital Humanities and Cultural Heritage
• American Journal of Neuroradiology close
• EBRAINS close

BACKGROUND AND PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has led to decreases in neuroimaging volume. Our aim was to quantify the change in acute or subacute ischemic strokes detected on CT or MR imaging during the pandemic using natural language processing of radiology reports. MATERIALS AND METHODS: We retrospectively analyzed 32,555 radiology reports from brain CTs and MRIs from a comprehensive stroke center, performed from March 1 to April 30 each year from 2017 to 2020, involving 20,414 unique patients. To detect acute or subacute ischemic stroke in free-text reports, we trained a random forest natural language processing classifier using 1987 randomly sampled radiology reports with manual annotation. Natural language processing classifier generalizability was evaluated using 1974 imaging reports from an external dataset. RESULTS: The natural language processing classifier achieved a 5-fold cross-validation classification accuracy of 0.97 and an F1 score of 0.74, with a slight underestimation (−5%) of actual numbers of acute or subacute ischemic strokes in cross-validation. Importantly, cross-validation performance stratified by year was similar. Applying the classifier to the complete study cohort, we found an estimated 24% decrease in patients with acute or subacute ischemic strokes reported on CT or MR imaging from March to April 2020 compared with the average from those months in 2017–2019. Among patients with stroke-related order indications, the estimated proportion who underwent neuroimaging with acute or subacute ischemic stroke detection significantly increased from 16% during 2017–2019 to 21% in 2020 (P = .01). The natural language processing classifier performed worse on external data. CONCLUSIONS: Acute or subacute ischemic stroke cases detected by neuroimaging decreased during the COVID-19 pandemic, though a higher proportion of studies ordered for stroke were positive for acute or subacute ischemic strokes. Natural language processing approaches can help automatically track acute or subacute ischemic stroke numbers for epidemiologic studies, though local classifier training is important due to radiologist reporting style differences.

BACKGROUND AND PURPOSE: Whereas fMRI postprocessing tools used in research are accurate but unwieldy, those used for clinical practice are user-friendly but are less accurate. We aimed to determine whether commercial software for fMRI postprocessing is accurate enough for clinical practice. METHODS: Ten volunteers underwent fMRI while performing motor and language tasks (hand, foot, and orolingual movements; verbal fluency; semantic judgment; and oral comprehension). We compared visual concordance, image quality (noise), voxel size, and radiologist preference for the activation maps obtained by using Neuro3D software (provided with our MR imaging scanner) and by using the SPM program commonly used in research. RESULTS: Maps obtained with the 2 methods were classified as “partially overlapping” for 70% for motor and 72% for language paradigm experiments and as “overlapping” in 30% of motor and in 15% of language paradigm experiments. CONCLUSIONS: fMRI is a helpful and robust tool in clinical practice for planning neurosurgery. Widely available commercial fMRI software can provide reliable information for therapeutic management, so sophisticated, less widely available software is unnecessary in most cases.

BACKGROUND AND PURPOSE: Automatic assessment of brain volumes is needed in research and clinical practice. Manual tracing is still the criterion standard but is time-consuming. It is important to validate the automatic tools to avoid the problems of clinical studies drawing conclusions on the basis of brain volumes estimated with methodologic errors. The objective of this study was to evaluate a new commercially available fully automatic software for MR imaging of brain volume assessment. Automatic and expert manual brain volumes were compared. MATERIALS AND METHODS: MR imaging (3T, axial T2 and FLAIR) was performed in 41 healthy elderly volunteers (mean age, 70 ± 6 years) and 20 patients with hydrocephalus (mean age, 73 ± 7 years). The software Q(Brain) was used to manually and automatically measure the following brain volumes: ICV, BTV, VV, and WMHV. The manual method has been previously validated and was used as the reference. Agreement between the manual and automatic methods was evaluated by using linear regression and Bland-Altman plots. RESULTS: There were significant differences between the automatic and manual methods regarding all volumes. The mean differences were ICV = 49 ± 93 mL (mean ± 2SD, n = 61), BTV = 11 ± 70 mL, VV = −6 ± 10 mL, and WMHV = 2.4 ± 9 mL. The automatic calculations of brain volumes took approximately 2 minutes per investigation. CONCLUSIONS: The automatic tool is promising and provides rapid assessment of brain volumes. However, the software needs improvement before it is incorporated into research or daily use. Manual segmentation remains the reference method.