Deep learning: a turning point in acute neurology

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Automatic segmentation of cerebral infarcts in follow-up computed tomography images with convolutional neural networks

Background and purpose: Infarct volume is a valuable outcome measure in treatment trials of acute ischemic stroke and is strongly associated with functional outcome. Its manual volumetric assessment is, however, too demanding to be implemented in clinical practice. Objective: To assess the value of convolutional neural networks (CNNs) in the automatic segmentation of infarct volume in follow-up CT images in a large population of patients with acute ischemic stroke. Materials and methods: We included CT images of 1026 patients from a large pooling of patients with acute ischemic stroke. A reference standard for the infarct segmentation was generated by manual delineation. We introduce three CNN models for the segmentati

Lowering blood pressure after acute intracerebral haemorrhage: protocol for a systematic review and meta-analysis using individual patient data from randomised controlled trials participating in the Blood Pressure in Acute Stroke Collaboration (BASC)

INTRODUCTION: Conflicting results from multiple randomised trials indicate that the methods and effects of blood pressure (BP) reduction after acute intracerebral haemorrhage (ICH) are complex. The Blood pressure in Acute Stroke Collaboration is an international collaboration, which aims to determine the optimal management of BP after acute stroke including ICH. METHODS AND ANALYSIS: A systematic review will be undertaken according to the Preferred Reporting Items for Systematic review and Meta-Analysis of Individual Participant Data (IPD) guideline. A search of Cochrane Central Register of Controlled Trials, EMBASE and MEDLINE from inception will be conducted to identify randomised controlled trials of BP management in adults with acute spontaneous (non-traumatic) ICH enrolled within the first 7 days of symptom onset. Authors of studies that meet the inclusion criteria will be invited to share their IPD. The primary outcome will be functional outcome according to the modified Rankin Scale. Safety outcomes will be early neurological deterioration, symptomatic hypotension and serious adverse events. Secondary outcomes will include death and neuroradiological and haemodynamic variables. Meta-analyses of pooled IPD using the intention-to-treat dataset of included trials, including subgroup analyses to assess modification of the effects of BP lowering by time to treatment, treatment strategy and patient's demographic, clinical and prestroke neuroradiological characteristics. ETHICS AND DISSEMINATION: No new patient data will be collected nor is there any deviation from the original purposes of each study where ethical approvals were granted; therefore, further ethical approval is not required. Results will be reported in international peer-reviewed journals. PROSPERO REGISTRATION NUMBER: CRD42019141136

Automatic segmentation of cerebral infarcts in follow-up computed tomography images with convolutional neural networks

Background and purpose: Infarct volume is a valuable outcome measure in treatment trials of acute ischemic stroke and is strongly associated with functional outcome. Its manual volumetric assessment is, however, too demanding to be implemented in clinical practice. Objective: To assess the value of convolutional neural networks (CNNs) in the automatic segmentation of infarct volume in follow-up CT images in a large population of patients with acute ischemic stroke. Materials and methods: We included CT images of 1026 patients from a large pooling of patients with acute ischemic stroke. A reference standard for the infarct segmentation was generated by manual delineation. We introduce three CNN models for the segmentation of subtle, intermediate, and severe hypodense lesions. The fully automated infarct segmentation was defined as the combination of the results of these three CNNs. The results of the three-CNNs approach were compared with the results from a single CNN approach and with the reference standard segmentations. Results: The median infarct volume was 48 mL (IQR 15–125 mL). Comparison between the volumes of the three-CNNs approach and manually delineated infarct volumes showed excellent agreement, with an intraclass correlation coefficient (ICC) of 0.88. Even better agreement was found for severe and intermediate hypodense infarcts, with ICCs of 0.98 and 0.93, respectively. Although the number of patients used for training in the single CNN approach was much larger, the accuracy of the three-CNNs approach strongly outperformed the single CNN approach, which had an ICC of 0.34. Conclusion: Convolutional neural networks are valuable and accurate in the quantitative assessment of infarct volumes, for both subtle and severe hypodense infarcts in follow-up CT images. Our proposed three-CNNs approach strongly outperforms a more straightforward single CNN approach

Early lowering of blood pressure after acute intracerebral haemorrhage: a systematic review and meta-analysis of individual patient data

Objective: To summarise evidence of the effects of blood pressure (BP)-lowering interventions after acute spontaneous intracerebral haemorrhage (ICH). Methods: A pre-specified systematic review of the Cochrane Central Register of Controlled Trials, EMBASE and MEDLINE databases from inception to 23 June 2020 to identify randomised controlled trials that compared active BP-lowering agents vs. placebo or intensive vs. guideline BP-lowering targets for adults 6ml) and proportional (>33%) haematoma growth at 24 hours. Meta-analysis used a one-stage approach, adjusted using generalised linear mixed models with pre-specified covariables and trial as a random effect. Results: Of 7094 studies identified, 50 trials involving 11,494 patients were eligible and 16 (32.0%) shared patient-level data from 6,221 (54.1%) patients (mean age 64.2 [SD 12.9], 2,266 [36.4%] females) with a median time from symptom onset to randomisation of 3.8 hours (IQR 2.6−5.3). Active/intensive BP-lowering interventions had no effect on the primary outcome compared to placebo/guideline treatment (adjusted OR for unfavourable shift in modified Rankin scale scores: 0.97, 95% confidence interval 0.88 to 1.06; p=0.50), but there was significant heterogeneity by strategy (pinteraction=0.031) and agent (pinteraction<0.0001). Active/intensive BP-lowering interventions clearly reduced absolute and relative haematoma growth. Interpretation: Overall, a broad range of interventions to lower BP within 7 days of ICH onset had no overall benefit on functional recovery, despite reducing bleeding. The treatment effect appeared to vary according to strategy and agent

Clinical potential of automated convolutional neural network-based hematoma volumetry after aneurysmal subarachnoid hemorrhage

Objectives Cerebrospinal fluid hemoglobin has been positioned as a potential biomarker and drug target for aneurysmal subarachnoid hemorrhage-related secondary brain injury (SAH-SBI). The maximum amount of hemoglobin, which may be released into the cerebrospinal fluid, is defined by the initial subarachnoid hematoma volume (ISHV). In patients without external ventricular or lumbar drain, there remains an unmet clinical need to predict the risk for SAH-SBI. The aim of this study was to explore automated segmentation of ISHV as a potential surrogate for cerebrospinal fluid hemoglobin to predict SAH-SBI. Methods This study is based on a retrospective analysis of imaging and clinical data from 220 consecutive patients with aneurysmal subarachnoid hemorrhage collected over a five-year period. 127 annotated initial non-contrast CT scans were used to train and test a convolutional neural network to automatically segment the ISHV in the remaining cohort. Performance was reported in terms of Dice score and intraclass correlation. We characterized the associations between ISHV and baseline cohort characteristics, SAH-SBI, ventriculoperitoneal shunt dependence, functional outcome, and survival. Established clinical (World Federation of Neurosurgical Societies, Hunt & Hess) and radiological (modified Fisher, Barrow Neurological Institute) scores served as references. Results A strong volume agreement (0.73 Dice, range 0.43 - 0.93) and intraclass correlation (0.89, 95% CI, 0.81-0.94) were shown. While ISHV was not associated with the use of antithrombotics or cardiovascular risk factors, there was strong evidence for an association with a lower Glasgow Coma Scale at hospital admission. Aneurysm size and location were not associated with ISHV, but the presence of intracerebral or intraventricular hemorrhage were independently associated with higher ISHV. Despite strong evidence for a positive association between ISHV and SAH-SBI, the discriminatory ability of ISHV for SAH-SBI was insufficient. The discriminatory ability of ISHV was, however, higher regarding ventriculoperitoneal shunt dependence and functional outcome at three-months follow-up. Multivariate survival analysis provided strong evidence for an independent negative association between survival probability and both ISHV and intraventricular hemorrhage. Conclusions The proposed algorithm demonstrates strong performance in volumetric segmentation of the ISHV on the admission CT. While the discriminatory ability of ISHV for SAH-SBI was similar to established clinical and radiological scores, it showed a high discriminatory ability for ventriculoperitoneal shunt dependence and functional outcome at three-months follow-up

Clinical potential of automated convolutional neural network-based hematoma volumetry after aneurysmal subarachnoid hemorrhage

Objectives: Cerebrospinal fluid hemoglobin has been positioned as a potential biomarker and drug target for aneurysmal subarachnoid hemorrhage-related secondary brain injury (SAH-SBI). The maximum amount of hemoglobin, which may be released into the cerebrospinal fluid, is defined by the initial subarachnoid hematoma volume (ISHV). In patients without external ventricular or lumbar drain, there remains an unmet clinical need to predict the risk for SAH-SBI. The aim of this study was to explore automated segmentation of ISHV as a potential surrogate for cerebrospinal fluid hemoglobin to predict SAH-SBI. Methods: This study is based on a retrospective analysis of imaging and clinical data from 220 consecutive patients with aneurysmal subarachnoid hemorrhage collected over a five-year period. 127 annotated initial non-contrast CT scans were used to train and test a convolutional neural network to automatically segment the ISHV in the remaining cohort. Performance was reported in terms of Dice score and intraclass correlation. We characterized the associations between ISHV and baseline cohort characteristics, SAH-SBI, ventriculoperitoneal shunt dependence, functional outcome, and survival. Established clinical (World Federation of Neurosurgical Societies, Hunt & Hess) and radiological (modified Fisher, Barrow Neurological Institute) scores served as references. Results: A strong volume agreement (0.73 Dice, range 0.43 - 0.93) and intraclass correlation (0.89, 95% CI, 0.81-0.94) were shown. While ISHV was not associated with the use of antithrombotics or cardiovascular risk factors, there was strong evidence for an association with a lower Glasgow Coma Scale at hospital admission. Aneurysm size and location were not associated with ISHV, but the presence of intracerebral or intraventricular hemorrhage were independently associated with higher ISHV. Despite strong evidence for a positive association between ISHV and SAH-SBI, the discriminatory ability of ISHV for SAH-SBI was insufficient. The discriminatory ability of ISHV was, however, higher regarding ventriculoperitoneal shunt dependence and functional outcome at three-months follow-up. Multivariate survival analysis provided strong evidence for an independent negative association between survival probability and both ISHV and intraventricular hemorrhage. Conclusions: The proposed algorithm demonstrates strong performance in volumetric segmentation of the ISHV on the admission CT. While the discriminatory ability of ISHV for SAH-SBI was similar to established clinical and radiological scores, it showed a high discriminatory ability for ventriculoperitoneal shunt dependence and functional outcome at three-months follow-up