Abstract 135: Examining the Information Loss Between Neuroimages and Neuroimaging Reports for Detection of Silent Brain Infarcts and White Matter Disease Using Artificial Intelligence Technologies

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Lester Y Leung ◽  
Sunyang Fu ◽  
Jason Nelson ◽  
David F Kallmes ◽  
Patrick H Luetmer ◽  
...  
Keyword(s):  
White Matter ◽  
Language Processing ◽  
Interrater Reliability ◽  
Medical Center ◽  
Brain Infarction ◽  
White Matter Disease ◽  
Standardized Protocol ◽  
Cohort Identification ◽  
Silent Brain Infarction ◽  
Good For

Background: Real world studies of silent brain infarction (SBI) and white matter disease (WMD) are impeded by challenges in cohort identification. Natural language processing (NLP) from imaging reports may facilitate future studies. However, electronic health records can be heterogeneous and the process of interpreting neuroimages and generating reports can vary. Understanding knowledge representation and relationships between neuroimages and imaging reports is crucial for using NLP to facilitate disease management and cohort identification. Methods: A balanced sample of head neuroimages (CT, MRI) of patients >50 years without clinical histories of symptomatic stroke, TIA, or dementia were obtained at Mayo Clinic and Tufts Medical Center. A team of 4 radiology residents performed report interpretation (RI) on 1000 reports according to a standardized protocol for the presence of SBIs, the presence of WMD, and WMD grade. A random subsample of 400 was doubly read for interrater reliability. For benchmarking, a team of 4 neuroradiologists directly reviewed and described findings on a subsample of 182 images, each doubly read. We assessed interrater reliability for direct review (DR) and RI, and agreement between these 2 information sources. An NLP algorithm was developed to review and extract findings from 1000 imaging reports. Results: For DR, interrater reliability was moderate for SBIs and WMD (k = 0.53, 95% CI 0.43-0.64 and k = 0.47, 95% CI 0.33-0.61) and good for WMD grade (Spearman 0.71, p<0.001). For RI, interrater reliability for SBIs, WMD and WMD grade was good (k = 0.88, 95% CI 0.80-0.97; k = 0.98, 95% CI 0.97-1.00; and Spearman = 0.985, p<0.001, respectively). Agreement between DR and RI was good for SBIs (k = 0.77, 95% CI 0.67-0.86) and WMD (k = 0.65, 95% CI 0.54-0.77). Spearman rank correlations comparing WMD grade interpretation DR to RI was 0.60 (p<0.001). In identifying the presence of SBIs and WMDs, the accuracy of the NLP algorithm was 0.991 and 0.994, respectively. Conclusion: For the presence of SBI and WMD, and WMD grade, agreement between RI and DR was similar to agreement between 2 neuroradiologists directly reviewing neuroimages. It is highly feasible to use NLP to identify patients with SBIs and WMDs for clinical studies.

scholarly journals Agreement between neuroimages and reports for natural language processing-based detection of silent brain infarcts and white matter disease

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lester Y. Leung ◽  
Sunyang Fu ◽  
Patrick H. Luetmer ◽  
David F. Kallmes ◽  
Neel Madan ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
White Matter ◽  
Natural Language ◽  
Language Processing ◽  
Interrater Reliability ◽  
Clinical Care ◽  
Routine Care ◽  
White Matter Disease ◽  
Brain Infarcts ◽  
Cohort Identification

Abstract Background There are numerous barriers to identifying patients with silent brain infarcts (SBIs) and white matter disease (WMD) in routine clinical care. A natural language processing (NLP) algorithm may identify patients from neuroimaging reports, but it is unclear if these reports contain reliable information on these findings. Methods Four radiology residents reviewed 1000 neuroimaging reports (RI) of patients age > 50 years without clinical histories of stroke, TIA, or dementia for the presence, acuity, and location of SBIs, and the presence and severity of WMD. Four neuroradiologists directly reviewed a subsample of 182 images (DR). An NLP algorithm was developed to identify findings in reports. We assessed interrater reliability for DR and RI, and agreement between these two and with NLP. Results For DR, interrater reliability was moderate for the presence of SBIs (k = 0.58, 95 % CI 0.46–0.69) and WMD (k = 0.49, 95 % CI 0.35–0.63), and moderate to substantial for characteristics of SBI and WMD. Agreement between DR and RI was substantial for the presence of SBIs and WMD, and fair to substantial for characteristics of SBIs and WMD. Agreement between NLP and DR was substantial for the presence of SBIs (k = 0.64, 95 % CI 0.53–0.76) and moderate (k = 0.52, 95 % CI 0.39–0.65) for the presence of WMD. Conclusions Neuroimaging reports in routine care capture the presence of SBIs and WMD. An NLP can identify these findings (comparable to direct imaging review) and can likely be used for cohort identification.

INCREASED RISK FOR SILENT BRAIN INFARCTION AND DEEP WHITE MATTER LESION IN ALCOHOLIC PATIENTS.

2004 ◽  
Vol 28 (Supplement) ◽  
pp. 22A
Author(s):  
Sachio Matsushita ◽  
Go Suzuki ◽  
Toshifumi Matsui ◽  
Toshihiro Masaki ◽  
Hiroyuki Arai ◽  
...  
Keyword(s):  
White Matter ◽  
White Matter Lesion ◽  
Brain Infarction ◽  
Deep White Matter ◽  
Increased Risk ◽  
Silent Brain Infarction

Abstract P57: Risk Factors for Incidentally Discovered Silent Brain Infarcts and White Matter Disease in a Real World Cohort Identified by Artificial Intelligence

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Lester Y Leung ◽  
Yichen Zhou ◽  
Sunyang Fu ◽  
Chengyi Zheng ◽  
Hongfang Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Ischemic Stroke ◽  
White Matter ◽  
Language Processing ◽  
Real World ◽  
Stroke Risk ◽  
Advanced Age ◽  
White Matter Disease ◽  
Brain Infarcts ◽  
Increased Risk

Introduction: Silent brain infarcts (SBIs) and white matter disease (WMD) are highly prevalent and associated with increased risk of ischemic stroke in patients with traditional stroke risk factors (RFs) in prospective cohort studies. Their frequency and associations with stroke RFs have not been well described in real world populations. Methods: This was a cross-sectional study of patients age ≥ 50 in the Kaiser Permanente-Southern California (KPSC) health system between 2009-2019 with a head CT or MRI for non-stroke indications and no history of ischemic stroke, transient ischemic attack, or dementia. A natural language processing (NLP) algorithm developed at Mayo Clinic and Tufts Medical Center was applied to the KPSC EHR to identify individuals with reported SBIs or WMD. Multivariable Poisson regression with robust error variance was used to estimate risk ratios of demographics, stroke RFs (from the Framingham Stroke Risk Score), and scan modality on the presence of SBIs or WMD. Results: Among 262,875 individuals, the NLP identified 13,154 (5.0%) with SBIs and 78,330 (29.8%) with WMD. Stroke RFs were highly prevalent in this cohort. The majority underwent CTs (74.8%) instead of MRIs as their initial neuroimaging. After adjustment for demographics and RFs, advanced age demonstrated a strong association with increased risk of SBIs and WMD (table). MRI was associated with a reduced risk of reported SBIs (ARR: 0.87, 95% CI 0.83-0.91) and an increased risk of reported WMD (ARR 2.86, 95% CI 2.83-2.90). Despite being prevalent, traditional stroke RFs had weak associations with increased risk of SBIs or increased risk of WMD. Conclusions: Advanced age is strongly associated with incidentally discovered SBIs and WMD on neuroimaging studies obtained in routine care. The development of SBIs and WMD may not be fully attributable to traditional stroke RFs.

High Prevalence of Silent Brain Infarction in Patients Presenting with Mechanical Heart Valve Thrombosis

2012 ◽  
Vol 12 (5) ◽  
pp. 345-348 ◽  
Author(s):  
Parag Barwad ◽  
Amol Raheja ◽  
Raghunandan Venkat ◽  
Shyam S. Kothari ◽  
Vinay Bahl ◽  
...  
Keyword(s):  
Heart Valve ◽  
Brain Infarction ◽  
Mechanical Heart Valve ◽  
High Prevalence ◽  
Valve Thrombosis ◽  
Silent Brain Infarction ◽  
Heart Valve Thrombosis

scholarly journals The analysis of the connection between plaque morphology of the asymptomatic carotid stenosis and ischemic brain lesions

2013 ◽  
Vol 70 (11) ◽  
pp. 993-998 ◽  
Author(s):  
Djordje Milosevic ◽  
Janko Pasternak ◽  
Vladan Popovic ◽  
Dragan Nikolic ◽  
Pavle Milosevic ◽  
...  
Keyword(s):  
Risk Factors ◽  
Carotid Stenosis ◽  
Carotid Plaque ◽  
Brain Infarction ◽  
Statistical Significance ◽  
Brain Parenchyma ◽  
Plaque Morphology ◽  
Asymptomatic Carotid Stenosis ◽  
Asymptomatic Carotid ◽  
Silent Brain Infarction

Background/Aim. A certain percentage of patients with asymptomatic carotid stenosis have an unstable carotid plaque. For these patients it is possible to register by modern imaging methods the existence of lesions of the brain parenchyma - the silent brain infarction. These patients have a greater risk of ischemic stroke. The aim of this study was to analyze the connection between the morphology of atherosclerotic carotid plaque in patients with asymptomatic carotid stenosis and the manifestation of silent brain infarction, and to analyze the influence of risk factors for cardiovascular diseases on the occurrence of silent brain infarction and the morphology of carotid plaque. Methods. This retrospective study included patients who had been operated for high grade (> 70%) extracranial atherosclerotic carotid stenosis at the Clinic for Vascular and Transplantation Surgery of the Clinical Center of Vojvodina over a period of 5 years. The patients analyzed had no clinical manifestation of cerebrovascular insufficiency of the carotid artery territory up to the time of operation. The classification of carotid plaque morphology was carried out according to the Gray-Weale classification, after which all the types were subcategorized into two groups: stable and unstable. Brain lesions were verified using preoperative imaging of the brain parenchyma by magnetic resonance. We analyzed ipsilateral lesions of the size > or = 3 mm. Results. Out of a 201 patients 78% had stable plaque and 22% unstable one. Unstable plaque was prevalent in the male patients (male/female ratio = 24.8% : 17.8%), but without a statistically significant difference (p > 0.05). The risk factors (hypertension, nicotinism, hyperlipoproteinemia, and diabetes mellitus) showed no statistically significant impact on carotid plaque morphology and the occurrence of silent brain infarction. Silent brain infarction was detected in 30.8% of the patients. Unstable carotid plaque was found in a larger percentage of patients with silent brain infarction (36.4% : 29.3%) but without a significant statistical difference (p > 0.05). Conclusions. Even though silent brain infarction is more frequent in patients with unstable plaque of carotid bifurication, the difference is of no statistical significance. The effects of the number and type of risk factors bear no statistical significance on the incidence of morphological asymptomatic carotid plaque.

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