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García-García S, Cepeda S, Arrese I, Sarabia R. A Fully Automated Pipeline Using Swin Transformers for Deep Learning-Based Blood Segmentation on Head Computed Tomography Scans After Aneurysmal Subarachnoid Hemorrhage. World Neurosurg 2024:S1878-8750(24)01357-3. [PMID: 39111661 DOI: 10.1016/j.wneu.2024.07.216] [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: 06/23/2024] [Accepted: 07/31/2024] [Indexed: 09/01/2024]
Abstract
BACKGROUND Accurate volumetric assessment of spontaneous aneurysmal subarachnoid hemorrhage (aSAH) is a labor-intensive task performed with current manual and semiautomatic methods that might be relevant for its clinical and prognostic implications. In the present research, we sought to develop and validate an artificial intelligence-driven, fully automated blood segmentation tool for subarachnoid hemorrhage (SAH) patients via noncontrast computed tomography (NCCT) scans employing a transformer-based Swin-UNETR architecture. METHODS We retrospectively analyzed NCCT scans from patients with confirmed aSAH utilizing the Swin-UNETR for segmentation. The performance of the proposed method was evaluated against manually segmented ground truth data using metrics such as Dice score, intersection over union, volumetric similarity index , symmetric average surface distance , sensitivity, and specificity. A validation cohort from an external institution was included to test the generalizability of the model. RESULTS The model demonstrated high accuracy with robust performance metrics across the internal and external validation cohorts. Notably, it achieved high Dice coefficient (0.873 ± 0.097), intersection over union (0.810 ± 0.092), volumetric similarity index (0.840 ± 0.131), sensitivity (0.821 ± 0.217), and specificity (0.996 ± 0.004) values and a low symmetric average surface distance (1.866 ± 2.910), suggesting proficiency in segmenting blood in SAH patients. The model's efficiency was reflected in its processing speed, indicating potential for real-time applications. CONCLUSIONS Our Swin UNETR-based model offers significant advances in the automated segmentation of blood in SAH patients on NCCT images. Despite the computational demands, the model operates effectively on standard hardware with a user-friendly interface, facilitating broader clinical adoption. Further validation across diverse datasets is warranted to confirm its clinical reliability.
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Affiliation(s)
- Sergio García-García
- Neurosurgery Department, Hospital Universitario Rio Hortega, Valladolid, Spain; Neurosurgery Department, Helsinki University Hospital, Helsinki, Finland.
| | - Santiago Cepeda
- Neurosurgery Department, Hospital Universitario Rio Hortega, Valladolid, Spain
| | - Ignacio Arrese
- Neurosurgery Department, Hospital Universitario Rio Hortega, Valladolid, Spain
| | - Rosario Sarabia
- Neurosurgery Department, Hospital Universitario Rio Hortega, Valladolid, Spain
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Abdollahifard S, Farrokhi A, Mowla A, Liebeskind DS. Performance Metrics, Algorithms, and Applications of Artificial Intelligence in Vascular and Interventional Neurology: A Review of Basic Elements. Neurol Clin 2024; 42:633-650. [PMID: 38937033 DOI: 10.1016/j.ncl.2024.03.001] [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] [Indexed: 06/29/2024]
Abstract
Artificial intelligence (AI) is currently being used as a routine tool for day-to-day activity. Medicine is not an exception to the growing usage of AI in various scientific fields. Vascular and interventional neurology deal with diseases that require early diagnosis and appropriate intervention, which are crucial to saving patients' lives. In these settings, AI can be an extra pair of hands for physicians or in conditions where there is a shortage of clinical experts. In this article, the authors have reviewed the common metrics used in interpreting the performance of models and common algorithms used in this field.
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Affiliation(s)
- Saeed Abdollahifard
- School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran; Research Center for Neuromodulation and Pain, Shiraz, Iran
| | | | - Ashkan Mowla
- Division of Stroke and Endovascular Neurosurgery, Department of Neurological Surgery, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA, USA
| | - David S Liebeskind
- UCLA Department of Neurology, Neurovascular Imaging Research Core, UCLA Comprehensive Stroke Center, University of California Los Angeles(UCLA), CA, USA.
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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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Hu P, Yan T, Xiao B, Shu H, Sheng Y, Wu Y, Shu L, Lv S, Ye M, Gong Y, Wu M, Zhu X. Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis. Int J Surg 2024; 110:3839-3847. [PMID: 38489547 PMCID: PMC11175741 DOI: 10.1097/js9.0000000000001266] [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: 12/19/2023] [Accepted: 02/21/2024] [Indexed: 03/17/2024]
Abstract
BACKGROUND Deep learning (DL)-assisted detection and segmentation of intracranial hemorrhage stroke in noncontrast computed tomography (NCCT) scans are well-established, but evidence on this topic is lacking. MATERIALS AND METHODS PubMed and Embase databases were searched from their inception to November 2023 to identify related studies. The primary outcomes included sensitivity, specificity, and the Dice Similarity Coefficient (DSC); while the secondary outcomes were positive predictive value (PPV), negative predictive value (NPV), precision, area under the receiver operating characteristic curve (AUROC), processing time, and volume of bleeding. Random-effect model and bivariate model were used to pooled independent effect size and diagnostic meta-analysis data, respectively. RESULTS A total of 36 original studies were included in this meta-analysis. Pooled results indicated that DL technologies have a comparable performance in intracranial hemorrhage detection and segmentation with high values of sensitivity (0.89, 95% CI: 0.88-0.90), specificity (0.91, 95% CI: 0.89-0.93), AUROC (0.94, 95% CI: 0.93-0.95), PPV (0.92, 95% CI: 0.91-0.93), NPV (0.94, 95% CI: 0.91-0.96), precision (0.83, 95% CI: 0.77-0.90), DSC (0.84, 95% CI: 0.82-0.87). There is no significant difference between manual labeling and DL technologies in hemorrhage quantification (MD 0.08, 95% CI: -5.45-5.60, P =0.98), but the latter takes less process time than manual labeling (WMD 2.26, 95% CI: 1.96-2.56, P =0.001). CONCLUSION This systematic review has identified a range of DL algorithms that the performance was comparable to experienced clinicians in hemorrhage lesions identification, segmentation, and quantification but with greater efficiency and reduced cost. It is highly emphasized that multicenter randomized controlled clinical trials will be needed to validate the performance of these tools in the future, paving the way for fast and efficient decision-making during clinical procedure in patients with acute hemorrhagic stroke.
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Affiliation(s)
- Ping Hu
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
- Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases
- Jiangxi Health Commission Key Laboratory of Neurological Medicine
- Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Tengfeng Yan
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
- Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases
- Jiangxi Health Commission Key Laboratory of Neurological Medicine
- Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Bing Xiao
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
| | - Hongxin Shu
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
- Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases
- Jiangxi Health Commission Key Laboratory of Neurological Medicine
- Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Yilei Sheng
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
- Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases
- Jiangxi Health Commission Key Laboratory of Neurological Medicine
- Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Yanze Wu
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
- Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases
- Jiangxi Health Commission Key Laboratory of Neurological Medicine
- Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Lei Shu
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
- Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases
- Jiangxi Health Commission Key Laboratory of Neurological Medicine
- Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Shigang Lv
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
| | - Minhua Ye
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
| | - Yanyan Gong
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
| | - Miaojing Wu
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
| | - Xingen Zhu
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
- Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases
- Jiangxi Health Commission Key Laboratory of Neurological Medicine
- Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
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Li J, Jiang P, An Q, Wang GG, Kong HF. Medical image identification methods: A review. Comput Biol Med 2024; 169:107777. [PMID: 38104516 DOI: 10.1016/j.compbiomed.2023.107777] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/30/2023] [Accepted: 11/28/2023] [Indexed: 12/19/2023]
Abstract
The identification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Medical image data mainly include electronic health record data and gene information data, etc. Although intelligent imaging provided a good scheme for medical image analysis over traditional methods that rely on the handcrafted features, it remains challenging due to the diversity of imaging modalities and clinical pathologies. Many medical image identification methods provide a good scheme for medical image analysis. The concepts pertinent of methods, such as the machine learning, deep learning, convolutional neural networks, transfer learning, and other image processing technologies for medical image are analyzed and summarized in this paper. We reviewed these recent studies to provide a comprehensive overview of applying these methods in various medical image analysis tasks, such as object detection, image classification, image registration, segmentation, and other tasks. Especially, we emphasized the latest progress and contributions of different methods in medical image analysis, which are summarized base on different application scenarios, including classification, segmentation, detection, and image registration. In addition, the applications of different methods are summarized in different application area, such as pulmonary, brain, digital pathology, brain, skin, lung, renal, breast, neuromyelitis, vertebrae, and musculoskeletal, etc. Critical discussion of open challenges and directions for future research are finally summarized. Especially, excellent algorithms in computer vision, natural language processing, and unmanned driving will be applied to medical image recognition in the future.
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Affiliation(s)
- Juan Li
- School of Information Engineering, Wuhan Business University, Wuhan, 430056, China; School of Artificial Intelligence, Wuchang University of Technology, Wuhan, 430223, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Pan Jiang
- School of Information Engineering, Wuhan Business University, Wuhan, 430056, China
| | - Qing An
- School of Artificial Intelligence, Wuchang University of Technology, Wuhan, 430223, China
| | - Gai-Ge Wang
- School of Computer Science and Technology, Ocean University of China, Qingdao, 266100, China.
| | - Hua-Feng Kong
- School of Information Engineering, Wuhan Business University, Wuhan, 430056, China.
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Schonfeld E, Mordekai N, Berg A, Johnstone T, Shah A, Shah V, Haider G, Marianayagam NJ, Veeravagu A. Machine Learning in Neurosurgery: Toward Complex Inputs, Actionable Predictions, and Generalizable Translations. Cureus 2024; 16:e51963. [PMID: 38333513 PMCID: PMC10851045 DOI: 10.7759/cureus.51963] [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: 08/27/2023] [Accepted: 01/08/2024] [Indexed: 02/10/2024] Open
Abstract
Machine learning can predict neurosurgical diagnosis and outcomes, power imaging analysis, and perform robotic navigation and tumor labeling. State-of-the-art models can reconstruct and generate images, predict surgical events from video, and assist in intraoperative decision-making. In this review, we will detail the neurosurgical applications of machine learning, ranging from simple to advanced models, and their potential to transform patient care. As machine learning techniques, outputs, and methods become increasingly complex, their performance is often more impactful yet increasingly difficult to evaluate. We aim to introduce these advancements to the neurosurgical audience while suggesting major potential roadblocks to their safe and effective translation. Unlike the previous generation of machine learning in neurosurgery, the safe translation of recent advancements will be contingent on neurosurgeons' involvement in model development and validation.
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Affiliation(s)
- Ethan Schonfeld
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | | | - Alex Berg
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | - Thomas Johnstone
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | - Aaryan Shah
- School of Humanities and Sciences, Stanford University, Stanford, USA
| | - Vaibhavi Shah
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | - Ghani Haider
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | | | - Anand Veeravagu
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
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García-García S, Cepeda S, Müller D, Mosteiro A, Torné R, Agudo S, de la Torre N, Arrese I, Sarabia R. Mortality Prediction of Patients with Subarachnoid Hemorrhage Using a Deep Learning Model Based on an Initial Brain CT Scan. Brain Sci 2023; 14:10. [PMID: 38248225 PMCID: PMC10812955 DOI: 10.3390/brainsci14010010] [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: 11/05/2023] [Revised: 12/10/2023] [Accepted: 12/21/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Subarachnoid hemorrhage (SAH) entails high morbidity and mortality rates. Convolutional neural networks (CNN) are capable of generating highly accurate predictions from imaging data. Our objective was to predict mortality in SAH patients by processing initial CT scans using a CNN-based algorithm. METHODS We conducted a retrospective multicentric study of a consecutive cohort of patients with SAH. Demographic, clinical and radiological variables were analyzed. Preprocessed baseline CT scan images were used as the input for training using the AUCMEDI framework. Our model's architecture leveraged a DenseNet121 structure, employing transfer learning principles. The output variable was mortality in the first three months. RESULTS Images from 219 patients were processed; 175 for training and validation and 44 for the model's evaluation. Of the patients, 52% (115/219) were female and the median age was 58 (SD = 13.06) years. In total, 18.5% (39/219) had idiopathic SAH. The mortality rate was 28.5% (63/219). The model showed good accuracy at predicting mortality in SAH patients when exclusively using the images of the initial CT scan (accuracy = 74%, F1 = 75% and AUC = 82%). CONCLUSION Modern image processing techniques based on AI and CNN make it possible to predict mortality in SAH patients with high accuracy using CT scan images as the only input. These models might be optimized by including more data and patients, resulting in better training, development and performance on tasks that are beyond the skills of conventional clinical knowledge.
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Affiliation(s)
- Sergio García-García
- Neurosurgery Department, Rio Hortega University Hospital, 47012 Valladolid, Spain; (S.C.); (S.A.); (N.d.l.T.); (I.A.); (R.S.)
| | - Santiago Cepeda
- Neurosurgery Department, Rio Hortega University Hospital, 47012 Valladolid, Spain; (S.C.); (S.A.); (N.d.l.T.); (I.A.); (R.S.)
| | - Dominik Müller
- IT-Infrastructure for Translational Medical Research, University of Augsburg, 86159 Augsburg, Germany;
| | - Alejandra Mosteiro
- Neurosurgery Department, Hospital Clinic de Barcelona, 08036 Barcelona, Spain; (A.M.); (R.T.)
| | - Ramón Torné
- Neurosurgery Department, Hospital Clinic de Barcelona, 08036 Barcelona, Spain; (A.M.); (R.T.)
| | - Silvia Agudo
- Neurosurgery Department, Rio Hortega University Hospital, 47012 Valladolid, Spain; (S.C.); (S.A.); (N.d.l.T.); (I.A.); (R.S.)
| | - Natalia de la Torre
- Neurosurgery Department, Rio Hortega University Hospital, 47012 Valladolid, Spain; (S.C.); (S.A.); (N.d.l.T.); (I.A.); (R.S.)
| | - Ignacio Arrese
- Neurosurgery Department, Rio Hortega University Hospital, 47012 Valladolid, Spain; (S.C.); (S.A.); (N.d.l.T.); (I.A.); (R.S.)
| | - Rosario Sarabia
- Neurosurgery Department, Rio Hortega University Hospital, 47012 Valladolid, Spain; (S.C.); (S.A.); (N.d.l.T.); (I.A.); (R.S.)
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Krawchuk LJ, Sharrock MF. Prognostic Neuroimaging Biomarkers in Acute Vascular Brain Injury and Traumatic Brain Injury. Semin Neurol 2023; 43:699-711. [PMID: 37802120 DOI: 10.1055/s-0043-1775790] [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: 10/08/2023]
Abstract
Prognostic imaging biomarkers after acute brain injury inform treatment decisions, track the progression of intracranial injury, and can be used in shared decision-making processes with families. Herein, key established biomarkers and prognostic scoring systems are surveyed in the literature, and their applications in clinical practice and clinical trials are discussed. Biomarkers in acute ischemic stroke include computed tomography (CT) hypodensity scoring, diffusion-weighted lesion volume, and core infarct size on perfusion imaging. Intracerebral hemorrhage biomarkers include hemorrhage volume, expansion, and location. Aneurysmal subarachnoid biomarkers include hemorrhage grading, presence of diffusion-restricting lesions, and acute hydrocephalus. Traumatic brain injury CT scoring systems, contusion expansion, and diffuse axonal injury grading are reviewed. Emerging biomarkers including white matter disease scoring, diffusion tensor imaging, and the automated calculation of scoring systems and volumetrics are discussed.
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Affiliation(s)
- Lindsey J Krawchuk
- Department of Neurology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Matthew F Sharrock
- Department of Neurology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
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Haight TJ, Eshaghi A. Deep Learning Algorithms for Brain Imaging: From Black Box to Clinical Toolbox? Neurology 2023; 100:549-550. [PMID: 36639238 DOI: 10.1212/wnl.0000000000206808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 11/29/2022] [Indexed: 01/15/2023] Open
Affiliation(s)
- Thaddeus J Haight
- From the Center for Neuroscience and Regenerative Medicine (T.J.H.), Uniformed Services University of the Health Sciences, Bethesda; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD; Department of Neuroinflammation (A.E.), Queen Square Multiple Sclerosis Centre, Queen Square Institute of Neurology, University College London; Centre for Medical Image Computing (A.E.), Department of Computer Science, University College London, United Kingdom.
| | - Arman Eshaghi
- From the Center for Neuroscience and Regenerative Medicine (T.J.H.), Uniformed Services University of the Health Sciences, Bethesda; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD; Department of Neuroinflammation (A.E.), Queen Square Multiple Sclerosis Centre, Queen Square Institute of Neurology, University College London; Centre for Medical Image Computing (A.E.), Department of Computer Science, University College London, United Kingdom
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