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Curry SD, Boochoon KS, Casazza GC, Surdell DL, Cramer JA. Deep learning to predict risk of lateral skull base cerebrospinal fluid leak or encephalocele. Int J Comput Assist Radiol Surg 2024:10.1007/s11548-024-03259-z. [PMID: 39207718 DOI: 10.1007/s11548-024-03259-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 08/21/2024] [Indexed: 09/04/2024]
Abstract
PURPOSE Skull base features, including increased foramen ovale (FO) cross-sectional area, are associated with lateral skull base spontaneous cerebrospinal fluid (sCSF) leak and encephalocele. Manual measurement requires skill in interpreting imaging studies and is time consuming. The goal of this study was to develop a fully automated deep learning method for FO segmentation and to determine the predictive value in identifying patients with sCSF leak or encephalocele. METHODS A retrospective cohort study at a tertiary care academic hospital of 34 adults with lateral skull base sCSF leak or encephalocele were compared with 815 control patients from 2013-2021. A convolutional neural network (CNN) was constructed for image segmentation of axial computed tomography (CT) studies. Predicted FO segmentations were compared to manual segmentations, and receiver operating characteristic (ROC) curves were constructed. RESULTS 295 CTs were used for training and validation of the CNN. A separate dataset of 554 control CTs was matched 5:1 on age and sex with the sCSF leak/encephalocele group. The mean Dice score was 0.81. The sCSF leak/encephalocele group had greater mean (SD) FO cross-sectional area compared to the control group, 29.0 (7.7) mm2 versus 24.3 (7.6) mm2 (P = .002, 95% confidence interval 0.02-0.08). The area under the ROC curve was 0.69. CONCLUSION CNNs can be used to segment the cross-sectional area of the FO accurately and efficiently. Used together with other predictors, this method could be used as part of a clinical tool to predict the risk of sCSF leak or encephalocele.
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Affiliation(s)
- Steven D Curry
- Department of Otolaryngology, Head and Neck Surgery, University of Nebraska Medical Center, 981225 Nebraska Medical Center, Omaha, NE, 68198-1225, USA.
| | - Kieran S Boochoon
- Department of Otolaryngology, Head and Neck Surgery, University of Nebraska Medical Center, 981225 Nebraska Medical Center, Omaha, NE, 68198-1225, USA
| | - Geoffrey C Casazza
- Department of Otolaryngology, Head and Neck Surgery, University of Nebraska Medical Center, 981225 Nebraska Medical Center, Omaha, NE, 68198-1225, USA
| | - Daniel L Surdell
- Department of Neurosurgery, University of Nebraska Medical Center, 988437 Nebraska Medical Center, Omaha, NE, 68198-8437, USA
| | - Justin A Cramer
- Department of Radiology, Mayo Clinic, 5777 E Mayo Boulevard, Phoenix, AZ, 85054, USA
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Srikrishna M, Seo W, Zettergren A, Kern S, Cantré D, Gessler F, Sotoudeh H, Seidlitz J, Bernstock JD, Wahlund LO, Westman E, Skoog I, Virhammar J, Fällmar D, Schöll M. Assessing CT-based Volumetric Analysis via Transfer Learning with MRI and Manual Labels for Idiopathic Normal Pressure Hydrocephalus. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.23.24309144. [PMID: 38978640 PMCID: PMC11230337 DOI: 10.1101/2024.06.23.24309144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background Brain computed tomography (CT) is an accessible and commonly utilized technique for assessing brain structure. In cases of idiopathic normal pressure hydrocephalus (iNPH), the presence of ventriculomegaly is often neuroradiologically evaluated by visual rating and manually measuring each image. Previously, we have developed and tested a deep-learning-model that utilizes transfer learning from magnetic resonance imaging (MRI) for CT-based intracranial tissue segmentation. Accordingly, herein we aimed to enhance the segmentation of ventricular cerebrospinal fluid (VCSF) in brain CT scans and assess the performance of automated brain CT volumetrics in iNPH patient diagnostics. Methods The development of the model used a two-stage approach. Initially, a 2D U-Net model was trained to predict VCSF segmentations from CT scans, using paired MR-VCSF labels from healthy controls. This model was subsequently refined by incorporating manually segmented lateral CT-VCSF labels from iNPH patients, building on the features learned from the initial U-Net model. The training dataset included 734 CT datasets from healthy controls paired with T1-weighted MRI scans from the Gothenburg H70 Birth Cohort Studies and 62 CT scans from iNPH patients at Uppsala University Hospital. To validate the model's performance across diverse patient populations, external clinical images including scans of 11 iNPH patients from the Universitatsmedizin Rostock, Germany, and 30 iNPH patients from the University of Alabama at Birmingham, United States were used. Further, we obtained three CT-based volumetric measures (CTVMs) related to iNPH. Results Our analyses demonstrated strong volumetric correlations (ϱ=0.91, p<0.001) between automatically and manually derived CT-VCSF measurements in iNPH patients. The CTVMs exhibited high accuracy in differentiating iNPH patients from controls in external clinical datasets with an AUC of 0.97 and in the Uppsala University Hospital datasets with an AUC of 0.99. Discussion CTVMs derived through deep learning, show potential for assessing and quantifying morphological features in hydrocephalus. Critically, these measures performed comparably to gold-standard neuroradiology assessments in distinguishing iNPH from healthy controls, even in the presence of intraventricular shunt catheters. Accordingly, such an approach may serve to improve the radiological evaluation of iNPH diagnosis/monitoring (i.e., treatment responses). Since CT is much more widely available than MRI, our results have considerable clinical impact.
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Affiliation(s)
- Meera Srikrishna
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Physiology and Neuroscience, University of Gothenburg, Gothenburg, Sweden
| | - Woosung Seo
- Department of Surgical Sciences, Neuroradiology, Uppsala University, Uppsala, Sweden
| | - Anna Zettergren
- Neuropsychiatric Epidemiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), University of Gothenburg, Gothenburg, Sweden
| | - Silke Kern
- Neuropsychiatric Epidemiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), University of Gothenburg, Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Daniel Cantré
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center Rostock, Rostock, Germany
| | - Florian Gessler
- Department of Neurosurgery, University Medicine of Rostock, 18057 Rostock, Germany
| | - Houman Sotoudeh
- Department of Neuroradiology, University of Alabama, Birmingham, AL, United States
| | - Jakob Seidlitz
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, United States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, United States
| | - Joshua D. Bernstock
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Lars-Olof Wahlund
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Eric Westman
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Ingmar Skoog
- Neuropsychiatric Epidemiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), University of Gothenburg, Gothenburg, Sweden
| | - Johan Virhammar
- Department of Medical Sciences, Neurology, Uppsala University, Uppsala, Sweden
| | - David Fällmar
- Department of Surgical Sciences, Neuroradiology, Uppsala University, Uppsala, Sweden
| | - Michael Schöll
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Physiology and Neuroscience, University of Gothenburg, Gothenburg, Sweden
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK
- Department of Psychiatry, Cognition and Aging Psychiatry, Sahlgrenska University Hospital, Mölndal, Sweden
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Butler M, Shah P, Ozgen B, Michals EA, Geraghty JR, Testai FD, Maharathi B, Loeb JA. Automated segmentation of ventricular volumes and subarachnoid hemorrhage from computed tomography images: Evaluation of a rule-based pipeline approach. Neuroradiol J 2024:19714009241260791. [PMID: 38869365 DOI: 10.1177/19714009241260791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024] Open
Abstract
Changes in ventricular size, related to brain edema and hydrocephalus, as well as the extent of hemorrhage are associated with adverse outcomes in patients with subarachnoid hemorrhage (SAH). Frequently, these are measured manually using consecutive non-contrast computed tomography scans. Here, we developed a rule-based approach which incorporates both intensity and spatial normalization and utilizes user-defined thresholds and anatomical templates to segment both lateral ventricle (LV) and SAH blood volumes automatically from CT images. The algorithmic segmentations were evaluated against two expert neuroradiologists on representative slices from 20 admission scans from aneurysmal SAH patients. Previous methods have been developed to automate this time-consuming task, but they lack user feedback and are hard to implement due to large-scale data and complex design processes. Our results using automatic ventricular segmentation aligned well with expert reviewers with a median Dice coefficient of 0.81, AUC of 0.91, sensitivity of 81%, and precision of 84%. Automatic segmentation of SAH blood was most reliable near the base of the brain with a median Dice coefficient of 0.51, an AUC of 0.75, precision of 68%, and sensitivity of 50%. Ultimately, we developed a rule-based method that is easily adaptable through user feedback, generates spatially normalized segmentations that are comparable regardless of brain morphology or acquisition conditions, and automatically segments LV with good overall reliability and basal SAH blood with good precision. Our approach could benefit longitudinal studies in patients with SAH by streamlining assessment of edema and hydrocephalus progression, as well as blood resorption.
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Affiliation(s)
- Mitchell Butler
- Department of Neurology and Rehabilitation, University of Illinois College of Medicine, Chicago, IL, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Parin Shah
- Department of Neurology and Rehabilitation, University of Illinois College of Medicine, Chicago, IL, USA
| | - Burce Ozgen
- Department of Radiology, University of Illinois at Chicago College of Medicine, Chicago, IL, USA
| | - Edward A Michals
- Department of Radiology, University of Illinois at Chicago College of Medicine, Chicago, IL, USA
| | - Joseph R Geraghty
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Fernando D Testai
- Department of Neurology and Rehabilitation, University of Illinois College of Medicine, Chicago, IL, USA
| | - Biswajit Maharathi
- Department of Neurology and Rehabilitation, University of Illinois College of Medicine, Chicago, IL, USA
| | - Jeffrey A Loeb
- Department of Neurology and Rehabilitation, University of Illinois College of Medicine, Chicago, IL, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, USA
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Bajaj S, Khunte M, Moily NS, Payabvash S, Wintermark M, Gandhi D, Malhotra A. Value Proposition of FDA-Approved Artificial Intelligence Algorithms for Neuroimaging. J Am Coll Radiol 2023; 20:1241-1249. [PMID: 37574094 DOI: 10.1016/j.jacr.2023.06.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 06/21/2023] [Accepted: 06/30/2023] [Indexed: 08/15/2023]
Abstract
PURPOSE The number of FDA-cleared artificial intelligence (AI) algorithms for neuroimaging has grown in the past decade. The adoption of these algorithms into clinical practice depends largely on whether this technology provides value in the clinical setting. The objective of this study was to analyze trends in FDA-cleared AI algorithms for neuroimaging and understand their value proposition as advertised by the AI developers and vendors. METHODS A list of AI algorithms cleared by the FDA for neuroimaging between May 2008 and August 2022 was extracted from the ACR Data Science Institute AI Central database. Product information for each device was collected from the database. For each device, information on the advertised value as presented on the developer's website was collected. RESULTS A total of 59 AI neuroimaging algorithms were cleared by the FDA between May 2008 and August 2022. Most of these algorithms (24 of 59) were compatible with noncontrast CT, 21 with MRI, 9 with CT perfusion, 8 with CT angiography, 3 with MR perfusion, and 2 with PET. Six algorithms were compatible with multiple imaging techniques. Of the 59 algorithms, websites were located that discussed the product value for 55 algorithms. The most widely advertised value proposition was improved quality of care (38 of 55 [69.1%]). A total of 24 algorithms (43.6%) proposed saving user time, 9 (15.7%) advertised decreased costs, and 6 (10.9%) described increased revenue. Product websites for 26 algorithms (43.6%) showed user testimonials advertising the value of the technology. CONCLUSIONS The results of this study indicate a wide range of value propositions advertised by developers and vendors of AI algorithms for neuroimaging. Most vendors advertised that their products would improve patient care. Further research is necessary to determine whether the value claimed by developers is actually demonstrated in clinical practice.
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Affiliation(s)
- Suryansh Bajaj
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Mihir Khunte
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | | | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Max Wintermark
- Chair, Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Dheeraj Gandhi
- Director, Interventional Neuroradiology, University of Maryland School of Medicine, Baltimore, Maryland
| | - Ajay Malhotra
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut.
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Wang X, Liu S, Yang N, Chen F, Ma L, Ning G, Zhang H, Qiu X, Liao H. A Segmentation Framework With Unsupervised Learning-Based Label Mapper for the Ventricular Target of Intracranial Germ Cell Tumor. IEEE J Biomed Health Inform 2023; 27:5381-5392. [PMID: 37651479 DOI: 10.1109/jbhi.2023.3310492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Intracranial germ cell tumors are rare tumors that mainly affect children and adolescents. Radiotherapy is the cornerstone of interdisciplinary treatment methods. Radiation of the whole ventricle system and the local tumor can reduce the complications in the late stage of radiotherapy while ensuring the curative effect. However, manually delineating the ventricular system is labor-intensive and time-consuming for physicians. The diverse ventricle shape and the hydrocephalus-induced ventricle dilation increase the difficulty of automatic segmentation algorithms. Therefore, this study proposed a fully automatic segmentation framework. Firstly, we designed a novel unsupervised learning-based label mapper, which is used to handle the ventricle shape variations and obtain the preliminary segmentation result. Then, to boost the segmentation performance of the framework, we improved the region growth algorithm and combined the fully connected conditional random field to optimize the preliminary results from both regional and voxel scales. In the case of only one set of annotated data is required, the average time cost is 153.01 s, and the average target segmentation accuracy can reach 84.69%. Furthermore, we verified the algorithm in practical clinical applications. The results demonstrate that our proposed method is beneficial for physicians to delineate radiotherapy targets, which is feasible and clinically practical, and may fill the gap of automatic delineation methods for the ventricular target of intracranial germ celltumors.
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Gerken A, Walluscheck S, Kohlmann P, Galinovic I, Villringer K, Fiebach JB, Klein J, Heldmann S. Deep learning-based segmentation of brain parenchyma and ventricular system in CT scans in the presence of anomalies. FRONTIERS IN NEUROIMAGING 2023; 2:1228255. [PMID: 37554647 PMCID: PMC10406198 DOI: 10.3389/fnimg.2023.1228255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 07/18/2023] [Indexed: 08/10/2023]
Abstract
INTRODUCTION The automatic segmentation of brain parenchyma and cerebrospinal fluid-filled spaces such as the ventricular system is the first step for quantitative and qualitative analysis of brain CT data. For clinical practice and especially for diagnostics, it is crucial that such a method is robust to anatomical variability and pathological changes such as (hemorrhagic or neoplastic) lesions and chronic defects. This study investigates the increase in overall robustness of a deep learning algorithm that is gained by adding hemorrhage training data to an otherwise normal training cohort. METHODS A 2D U-Net is trained on subjects with normal appearing brain anatomy. In a second experiment the training data includes additional subjects with brain hemorrhage on image data of the RSNA Brain CT Hemorrhage Challenge with custom reference segmentations. The resulting networks are evaluated on normal and hemorrhage test casesseparately, and on an independent test set of patients with brain tumors of the publicly available GLIS-RT dataset. RESULTS Adding data with hemorrhage to the training set significantly improves the segmentation performance over an algorithm trained exclusively on normally appearing data, not only in the hemorrhage test set but also in the tumor test set. The performance on normally appearing data is stable. Overall, the improved algorithm achieves median Dice scores of 0.98 (parenchyma), 0.91 (left ventricle), 0.90 (right ventricle), 0.81 (third ventricle), and 0.80 (fourth ventricle) on the hemorrhage test set. On the tumor test set, the median Dice scores are 0.96 (parenchyma), 0.90 (left ventricle), 0.90 (right ventricle), 0.75 (third ventricle), and 0.73 (fourth ventricle). CONCLUSION Training on an extended data set that includes pathologies is crucial and significantly increases the overall robustness of a segmentation algorithm for brain parenchyma and ventricular system in CT data, also for anomalies completely unseen during training. Extension of the training set to include other diseases may further improve the generalizability of the algorithm.
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Affiliation(s)
- Annika Gerken
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Sina Walluscheck
- Fraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany
| | - Peter Kohlmann
- Fraunhofer Institute for Digital Medicine MEVIS, Berlin, Germany
| | - Ivana Galinovic
- Center for Stroke Research Berlin (CSB) Charité, Universitätsmedizin, Berlin, Berlin, Germany
| | - Kersten Villringer
- Center for Stroke Research Berlin (CSB) Charité, Universitätsmedizin, Berlin, Berlin, Germany
| | - Jochen B. Fiebach
- Center for Stroke Research Berlin (CSB) Charité, Universitätsmedizin, Berlin, Berlin, Germany
| | - Jan Klein
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Stefan Heldmann
- Fraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany
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Jha TR, Quigley MF, Mozaffari K, Lathia O, Hofmann K, Myseros JS, Oluigbo C, Keating RF. Prediction of shunt failure facilitated by rapid and accurate volumetric analysis: a single institution's preliminary experience. Childs Nerv Syst 2022; 38:1907-1912. [PMID: 35595938 DOI: 10.1007/s00381-022-05552-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 05/01/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Shunt malfunction is a common complication and often presents with hydrocephalus. While the diagnosis is often supported by radiographic studies, subtle changes in CSF volume may not be detectable on routine evaluation. The purpose of this study was to develop a novel automated volumetric software for evaluation of shunt failure in pediatric patients, especially in patients who may not manifest a significant change in their ventricular size. METHODS A single-institution retrospective review of shunted patients was conducted. Ventricular volume measurements were performed using manual and automated methods by three independent analysts. Manual measurements were produced using OsiriX software, whereas automated measurements were produced using the proprietary software. A p value < 0.05 was considered statistically significant. RESULTS Twenty-two patients met the inclusion criteria (13 males, 9 females). Mean age of the cohort was 4.9 years (range 0.1-18 years). Average measured CSF volume was similar between the manual and automated methods (169.8 mL vs 172.5 mL, p = 0.56). However, the average time to generate results was significantly shorter with the automated algorithm compared to the manual method (2244 s vs 38.3 s, p < 0.01). In 3/5 symptomatic patients whose neuroimaging was interpreted as stable, the novel algorithm detected the otherwise radiographically undetectable CSF volume changes. CONCLUSION The automated software accurately measures the ventricular volumes in pediatric patients with hydrocephalus. The application of this technology is valuable in patients who present clinically without obvious radiographic changes. Future studies with larger cohorts are needed to validate our preliminary findings and further assess the utility of this technology.
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Affiliation(s)
- Tushar R Jha
- Division of Neurosurgery, Children's National Hospital, Washington, DC, USA
| | - Mark F Quigley
- Division of Neurosurgery, Children's National Hospital, Washington, DC, USA
| | - Khashayar Mozaffari
- Division of Neurosurgery, Children's National Hospital, Washington, DC, USA.
| | - Orgest Lathia
- Division of Neurosurgery, Children's National Hospital, Washington, DC, USA
| | - Katherine Hofmann
- Division of Neurosurgery, Children's National Hospital, Washington, DC, USA
| | - John S Myseros
- Division of Neurosurgery, Children's National Hospital, Washington, DC, USA
| | - Chima Oluigbo
- Division of Neurosurgery, Children's National Hospital, Washington, DC, USA
| | - Robert F Keating
- Division of Neurosurgery, Children's National Hospital, Washington, DC, USA
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Chavva IR, Crawford AL, Mazurek MH, Yuen MM, Prabhat AM, Payabvash S, Sze G, Falcone GJ, Matouk CC, de Havenon A, Kim JA, Sharma R, Schiff SJ, Rosen MS, Kalpathy-Cramer J, Iglesias Gonzalez JE, Kimberly WT, Sheth KN. Deep Learning Applications for Acute Stroke Management. Ann Neurol 2022; 92:574-587. [PMID: 35689531 DOI: 10.1002/ana.26435] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/27/2022] [Accepted: 06/04/2022] [Indexed: 11/08/2022]
Abstract
Brain imaging is essential to the clinical care of patients with stroke, a leading cause of disability and death worldwide. Whereas advanced neuroimaging techniques offer opportunities for aiding acute stroke management, several factors, including time delays, inter-clinician variability, and lack of systemic conglomeration of clinical information, hinder their maximal utility. Recent advances in deep machine learning (DL) offer new strategies for harnessing computational medical image analysis to inform decision making in acute stroke. We examine the current state of the field for DL models in stroke triage. First, we provide a brief, clinical practice-focused primer on DL. Next, we examine real-world examples of DL applications in pixel-wise labeling, volumetric lesion segmentation, stroke detection, and prediction of tissue fate postintervention. We evaluate recent deployments of deep neural networks and their ability to automatically select relevant clinical features for acute decision making, reduce inter-rater variability, and boost reliability in rapid neuroimaging assessments, and integrate neuroimaging with electronic medical record (EMR) data in order to support clinicians in routine and triage stroke management. Ultimately, we aim to provide a framework for critically evaluating existing automated approaches, thus equipping clinicians with the ability to understand and potentially apply DL approaches in order to address challenges in clinical practice. ANN NEUROL 2022.
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Affiliation(s)
- Isha R Chavva
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Anna L Crawford
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Mercy H Mazurek
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Matthew M Yuen
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | | | - Sam Payabvash
- Department of Radiology, Yale School of Medicine, New Haven, CT
| | - Gordon Sze
- Department of Radiology, Yale School of Medicine, New Haven, CT
| | - Guido J Falcone
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Charles C Matouk
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT
| | - Adam de Havenon
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Jennifer A Kim
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Richa Sharma
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Steven J Schiff
- Departments of Neurosurgery, Engineering Science and Mechanics and Physics, Penn State University, University Park, PA
| | - Matthew S Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA
| | - Juan E Iglesias Gonzalez
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA
| | - W Taylor Kimberly
- Department of Neurology, Division of Neurocritical Care, Massachusetts General Hospital, Boston, MA
| | - Kevin N Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT
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Zhou X, Ye Q, Yang X, Chen J, Ma H, Xia J, Del Ser J, Yang G. AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus. Neural Comput Appl 2022; 35:1-10. [PMID: 35228779 PMCID: PMC8866920 DOI: 10.1007/s00521-022-07048-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 01/31/2022] [Indexed: 11/16/2022]
Abstract
Based on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multimodal and high-performance automatic ventricle segmentation method to achieve an efficient and accurate automatic measurement of the ventricular volume. First, we extract the brain CT and MRI images of 143 definite NPH patients. Second, we manually label the ventricular volume (VV) and intracranial volume (ICV). Then, we use the machine learning method to extract features and establish automatic ventricle segmentation model. Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV. In CT images, the Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), Pearson correlation, and Bland-Altman analysis of the automatic and manual segmentation result of the VV were 0.95, 0.99, 0.99, and 4.2 ± 2.6, respectively. The results of ICV were 0.96, 0.99, 0.99, and 6.0 ± 3.8, respectively. The whole process takes 3.4 ± 0.3 s. In MRI images, the DSC, ICC, Pearson correlation, and Bland-Altman analysis of the automatic and manual segmentation result of the VV were 0.94, 0.99, 0.99, and 2.0 ± 0.6, respectively. The results of ICV were 0.93, 0.99, 0.99, and 7.9 ± 3.8, respectively. The whole process took 1.9 ± 0.1 s. We have established a multimodal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume of NPH patients. This can help clinicians quickly and accurately understand the situation of NPH patient's ventricles.
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Affiliation(s)
- Xi Zhou
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People’s Hospital, 3002 SunGang Road West, Shenzhen, 518035 Guangdong Province China
| | - Qinghao Ye
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA USA
| | - Xiaolin Yang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People’s Hospital, 3002 SunGang Road West, Shenzhen, 518035 Guangdong Province China
| | - Jiakun Chen
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People’s Hospital, 3002 SunGang Road West, Shenzhen, 518035 Guangdong Province China
| | - Haiqin Ma
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People’s Hospital, 3002 SunGang Road West, Shenzhen, 518035 Guangdong Province China
| | - Jun Xia
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People’s Hospital, 3002 SunGang Road West, Shenzhen, 518035 Guangdong Province China
| | - Javier Del Ser
- University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
- TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain
| | - Guang Yang
- Royal Brompton Hospital, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
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Zhou X, Xia J. Application of Evans Index in Normal Pressure Hydrocephalus Patients: A Mini Review. Front Aging Neurosci 2022; 13:783092. [PMID: 35087391 PMCID: PMC8787286 DOI: 10.3389/fnagi.2021.783092] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Accepted: 12/20/2021] [Indexed: 11/13/2022] Open
Abstract
With an ever-growing aging population, the prevalence of normal pressure hydrocephalus (NPH) is increasing. Clinical symptoms of NPH include cognitive impairment, gait disturbance, and urinary incontinence. Surgery can improve symptoms, which leads to the disease's alternative name: treatable dementia. The Evans index (EI), defined as the ratio of the maximal width of the frontal horns to the maximum inner skull diameter, is the most commonly used index to indirectly assess the condition of the ventricles in NPH patients. EI measurement is simple, fast, and does not require any special software; in clinical practice, an EI >0.3 is the criterion for ventricular enlargement. However, EI's measurement methods, threshold setting, correlation with ventricle volume, and even its clinical value has been questioned. Based on the EI, the z-EI and anteroposterior diameter of the lateral ventricle index were derived and are discussed in this review.
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11
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Huang D, Bai H, Wang L, Hou Y, Li L, Xia Y, Yan Z, Chen W, Chang L, Li W. The Application and Development of Deep Learning in Radiotherapy: A Systematic Review. Technol Cancer Res Treat 2021; 20:15330338211016386. [PMID: 34142614 PMCID: PMC8216350 DOI: 10.1177/15330338211016386] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such as convolutional neural networks (CNN), has provided radiation oncologists with many promising tools that can simplify the complex radiotherapy process in the clinical work of radiation oncology, improve the accuracy and objectivity of diagnosis, and reduce the workload, thus enabling clinicians to spend more time on advanced decision-making tasks. As the development of DL gets closer to clinical practice, radiation oncologists will need to be more familiar with its principles to properly evaluate and use this powerful tool. In this paper, we explain the development and basic concepts of AI and discuss its application in radiation oncology based on different task categories of DL algorithms. This work clarifies the possibility of further development of DL in radiation oncology.
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Affiliation(s)
- Danju Huang
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Han Bai
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Li Wang
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Yu Hou
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Lan Li
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Yaoxiong Xia
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Zhirui Yan
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Wenrui Chen
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Li Chang
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Wenhui Li
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
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12
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Sin Ç, Akkaya N, Aksoy S, Orhan K, Öz U. A deep learning algorithm proposal to automatic pharyngeal airway detection and segmentation on CBCT images. Orthod Craniofac Res 2021; 24 Suppl 2:117-123. [DOI: 10.1111/ocr.12480] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 01/23/2021] [Accepted: 02/17/2021] [Indexed: 12/22/2022]
Affiliation(s)
- Çağla Sin
- Faculty of Dentistry Department of Orthodontics Near East University Mersin10 Turkey
| | - Nurullah Akkaya
- Department of Computer Engineering Applied Artificial Intelligence Research Centre Near East University Mersin10 Turkey
| | - Seçil Aksoy
- Faculty of Dentistry Department of Dentomaxillofacial Radiology Near East University Mersin10 Turkey
| | - Kaan Orhan
- Faculty of Dentistry Department of Dentomaxillofacial Radiology Ankara University Ankara Turkey
- Medical Design Application and Research Center (MEDITAM) Ankara University Ankara Turkey
| | - Ulaş Öz
- Faculty of Dentistry Department of Orthodontics Near East University Mersin10 Turkey
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13
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Maragkos GA, Filippidis AS, Chilamkurthy S, Salem MM, Tanamala S, Gomez-Paz S, Rao P, Moore JM, Papavassiliou E, Hackney D, Thomas AJ. Automated Lateral Ventricular and Cranial Vault Volume Measurements in 13,851 Patients Using Deep Learning Algorithms. World Neurosurg 2021; 148:e363-e373. [PMID: 33421645 DOI: 10.1016/j.wneu.2020.12.148] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 12/27/2020] [Accepted: 12/28/2020] [Indexed: 11/24/2022]
Abstract
BACKGROUND No large dataset-derived standard has been established for normal or pathologic human cerebral ventricular and cranial vault volumes. Automated volumetric measurements could be used to assist in diagnosis and follow-up of hydrocephalus or craniofacial syndromes. In this work, we use deep learning algorithms to measure ventricular and cranial vault volumes in a large dataset of head computed tomography (CT) scans. METHODS A cross-sectional dataset comprising 13,851 CT scans was used to deploy U-Net deep learning networks to segment and quantify lateral cerebral ventricular and cranial vault volumes in relation to age and sex. The models were validated against manual segmentations. Corresponding radiologic reports were annotated using a rule-based natural language processing framework to identify normal scans, cerebral atrophy, or hydrocephalus. RESULTS U-Net models had high fidelity to manual segmentations for lateral ventricular and cranial vault volume measurements (Dice index, 0.878 and 0.983, respectively). The natural language processing identified 6239 (44.7%) normal radiologic reports, 1827 (13.1%) with cerebral atrophy, and 1185 (8.5%) with hydrocephalus. Age-based and sex-based reference tables with medians, 25th and 75th percentiles for scans classified as normal, atrophy, and hydrocephalus were constructed. The median lateral ventricular volume in normal scans was significantly smaller compared with hydrocephalus (15.7 vs. 82.0 mL; P < 0.001). CONCLUSIONS This is the first study to measure lateral ventricular and cranial vault volumes in a large dataset, made possible with artificial intelligence. We provide a robust method to establish normal values for these volumes and a tool to report these on CT scans when evaluating for hydrocephalus.
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Affiliation(s)
- Georgios A Maragkos
- Neurosurgery Service, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Aristotelis S Filippidis
- Neurosurgery Service, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Mohamed M Salem
- Neurosurgery Service, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Santiago Gomez-Paz
- Neurosurgery Service, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Justin M Moore
- Neurosurgery Service, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Efstathios Papavassiliou
- Neurosurgery Service, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - David Hackney
- Radiology Department, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Ajith J Thomas
- Neurosurgery Service, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
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14
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Zhou X, Ye Q, Jiang Y, Wang M, Niu Z, Menpes-Smith W, Fang EF, Liu Z, Xia J, Yang G. Systematic and Comprehensive Automated Ventricle Segmentation on Ventricle Images of the Elderly Patients: A Retrospective Study. Front Aging Neurosci 2020; 12:618538. [PMID: 33390930 PMCID: PMC7772233 DOI: 10.3389/fnagi.2020.618538] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 11/23/2020] [Indexed: 11/13/2022] Open
Abstract
Background and Objective: Ventricle volume is closely related to hydrocephalus, brain atrophy, Alzheimer's, Parkinson's syndrome, and other diseases. To accurately measure the volume of the ventricles for elderly patients, we use deep learning to establish a systematic and comprehensive automated ventricle segmentation framework. Methods: The study participation included 20 normal elderly people, 20 patients with cerebral atrophy, 64 patients with normal pressure hydrocephalus, and 51 patients with acquired hydrocephalus. Second, get their imaging data through the picture archiving and communication systems (PACS) system. Then use ITK software to manually label participants' ventricular structures. Finally, extract imaging features through machine learning. Results: This automated ventricle segmentation method can be applied not only to CT and MRI images but also to images with different scan slice thicknesses. More importantly, it produces excellent segmentation results (Dice > 0.9). Conclusion: This automated ventricle segmentation method has wide applicability and clinical practicability. It can help clinicians find early disease, diagnose disease, understand the patient's disease progression, and evaluate the patient's treatment effect.
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Affiliation(s)
- Xi Zhou
- Department of Radiology, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Qinghao Ye
- Hangzhou Ocean's Smart Boya Co., Ltd., Hangzhou, China.,Mind Rank Ltd., Hongkong, China
| | - Yinghui Jiang
- Hangzhou Ocean's Smart Boya Co., Ltd., Hangzhou, China.,Mind Rank Ltd., Hongkong, China
| | - Minhao Wang
- Hangzhou Ocean's Smart Boya Co., Ltd., Hangzhou, China.,Mind Rank Ltd., Hongkong, China
| | - Zhangming Niu
- Aladdin Healthcare Technologies Ltd., London, United Kingdom
| | | | - Evandro Fei Fang
- Department of Clinical Molecular Biology, University of Oslo, Oslo, Norway
| | - Zhi Liu
- School of Information Science and Engineering, Shandong University, Qingdao, China
| | - Jun Xia
- Department of Radiology, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Guang Yang
- Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom.,National Heart and Lung Institute, Imperial College London, London, United Kingdom
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15
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Goo HW. Hydrocephalus: Ventricular Volume Quantification Using Three-Dimensional Brain CT Data and Semiautomatic Three-Dimensional Threshold-Based Segmentation Approach. Korean J Radiol 2020; 22:435-441. [PMID: 33169552 PMCID: PMC7909866 DOI: 10.3348/kjr.2020.0671] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 06/15/2020] [Accepted: 06/22/2020] [Indexed: 01/13/2023] Open
Abstract
OBJECTIVE To evaluate the usefulness of the ventricular volume percentage quantified using three-dimensional (3D) brain computed tomography (CT) data for interpreting serial changes in hydrocephalus. MATERIALS AND METHODS Intracranial and ventricular volumes were quantified using the semiautomatic 3D threshold-based segmentation approach for 113 brain CT examinations (age at brain CT examination ≤ 18 years) in 38 patients with hydrocephalus. Changes in ventricular volume percentage were calculated using 75 serial brain CT pairs (time interval 173.6 ± 234.9 days) and compared with the conventional assessment of changes in hydrocephalus (increased, unchanged, or decreased). A cut-off value for the diagnosis of no change in hydrocephalus was calculated using receiver operating characteristic curve analysis. The reproducibility of the volumetric measurements was assessed using the intraclass correlation coefficient on a subset of 20 brain CT examinations. RESULTS Mean intracranial volume, ventricular volume, and ventricular volume percentage were 1284.6 ± 297.1 cm³, 249.0 ± 150.8 cm³, and 19.9 ± 12.8%, respectively. The volumetric measurements were highly reproducible (intraclass correlation coefficient = 1.0). Serial changes (0.8 ± 0.6%) in ventricular volume percentage in the unchanged group (n = 28) were significantly smaller than those in the increased and decreased groups (6.8 ± 4.3% and 5.6 ± 4.2%, respectively; p = 0.001 and p < 0.001, respectively; n = 11 and n = 36, respectively). The ventricular volume percentage was an excellent parameter for evaluating the degree of hydrocephalus (area under the receiver operating characteristic curve = 0.975; 95% confidence interval, 0.948-1.000; p < 0.001). With a cut-off value of 2.4%, the diagnosis of unchanged hydrocephalus could be made with 83.0% sensitivity and 100.0% specificity. CONCLUSION The ventricular volume percentage quantified using 3D brain CT data is useful for interpreting serial changes in hydrocephalus.
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Affiliation(s)
- Hyun Woo Goo
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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16
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Lui YW, Chang PD, Zaharchuk G, Barboriak DP, Flanders AE, Wintermark M, Hess CP, Filippi CG. Artificial Intelligence in Neuroradiology: Current Status and Future Directions. AJNR Am J Neuroradiol 2020; 41:E52-E59. [PMID: 32732276 PMCID: PMC7658873 DOI: 10.3174/ajnr.a6681] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Fueled by new techniques, computational tools, and broader availability of imaging data, artificial intelligence has the potential to transform the practice of neuroradiology. The recent exponential increase in publications related to artificial intelligence and the central focus on artificial intelligence at recent professional and scientific radiology meetings underscores the importance. There is growing momentum behind leveraging artificial intelligence techniques to improve workflow and diagnosis and treatment and to enhance the value of quantitative imaging techniques. This article explores the reasons why neuroradiologists should care about the investments in new artificial intelligence applications, highlights current activities and the roles neuroradiologists are playing, and renders a few predictions regarding the near future of artificial intelligence in neuroradiology.
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Affiliation(s)
- Y W Lui
- From the Department of Radiology (Y.W.L.), New York University Langone Medical Center, New York, New York
| | - P D Chang
- Department of Radiology (P.D.C.), University of California Irvine Health Medical Center, Orange, California
| | - G Zaharchuk
- Department of Neuroradiology (G.Z., M.W.), Stanford University, Stanford, California
| | - D P Barboriak
- Department of Radiology (D.P.B.), Duke University Medical Center, Durham, North Carolina
| | - A E Flanders
- Department of Radiology (A.E.F.), Thomas Jefferson University Hospital, Philadelphia, Pennsylvania
| | - M Wintermark
- Department of Neuroradiology (G.Z., M.W.), Stanford University, Stanford, California
| | - C P Hess
- Department of Radiology and Biomedical Imaging (C.P.H.), University of California, San Francisco, San Francisco, California
| | - C G Filippi
- Department of Radiology (C.G.F.), Northwell Health, New York, New York.
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17
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Yamin G, Cheecharoen P, Goel G, Sung A, Li CQ, Chang YHA, McDonald CR, Farid N. Automated CT registration tool improves sensitivity to change in ventricular volume in patients with shunts and drains. Br J Radiol 2020; 93:20190398. [PMID: 31825670 DOI: 10.1259/bjr.20190398] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE CT is the mainstay imaging modality for assessing change in ventricular volume in patients with ventricular shunts or external ventricular drains (EVDs). We evaluated the performance of a novel fully automated CT registration and subtraction method to improve reader accuracy and confidence compared with standard CT. METHODS In a retrospective evaluation of 49 ventricular shunt or EVD patients who underwent sequential head CT scans with an automated CT registration tool (CT CoPilot), three readers were assessed on their ability to discern change in ventricular volume between scans using standard axial CT images versus reformats and subtraction images generated by the registration tool. The inter-rater reliability among the readers was calculated using an intraclass correlation coefficient (ICC). Bland-Altman tests were performed to determine reader performance compared to semi-quantitative assessment using the bifrontal horn and third ventricular width. McNemar's test was used to determine whether the use of the registration tool increased the reader's level of confidence. RESULTS Inter-rater reliability was higher when using the output of the registration tool (single measure ICC of 0.909 with versus 0.755 without the tool). Agreement between the readers' assessment of ventricular volume change and the semi-quantitative assessment improved with the registration tool (limits of agreement 4.1 vs 4.3). Furthermore, the tool improved reader confidence in determining increased or decreased ventricular volume (p < 0.001). CONCLUSION Automated CT registration and subtraction improves the reader's ability to detect change in ventricular volume between sequential scans in patients with ventricular shunts or EVDs. ADVANCES IN KNOWLEDGE Our automated CT registration and subtraction method may serve as a promising generalizable tool for accurate assessment of change in ventricular volume, which can significantly affect clinical management.
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Affiliation(s)
- Ghiam Yamin
- Department of Radiology, University of California San Diego School of Medicine, La Jolla, CA
| | - Piyaphon Cheecharoen
- Department of Radiology, University of California San Diego School of Medicine, La Jolla, CA
| | - Gunjan Goel
- Department of Neurosurgery, University of California San Diego School of Medicine, La Jolla, CA
| | - Andrew Sung
- Department of Radiology, University of California San Diego School of Medicine, La Jolla, CA
| | - Charles Q Li
- Department of Radiology, University of California San Diego School of Medicine, La Jolla, CA
| | - Yu-Hsuan A Chang
- Department of Psychiatry, University of California San Diego School of Medicine, La Jolla, CA
| | - Carrie R McDonald
- Department of Psychiatry, University of California San Diego School of Medicine, La Jolla, CA
| | - Nikdokht Farid
- Department of Radiology, University of California San Diego School of Medicine, La Jolla, CA
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