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Chen YR, Chen CC, Kuo CF, Lin CH. An efficient deep neural network for automatic classification of acute intracranial hemorrhages in brain CT scans. Comput Biol Med 2024; 176:108587. [PMID: 38735238 DOI: 10.1016/j.compbiomed.2024.108587] [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: 11/20/2023] [Revised: 05/08/2024] [Accepted: 05/08/2024] [Indexed: 05/14/2024]
Abstract
BACKGROUND Recent advancements in deep learning models have demonstrated their potential in the field of medical imaging, achieving remarkable performance surpassing human capabilities in tasks such as classification and segmentation. However, these modern state-of-the-art network architectures often demand substantial computational resources, which limits their practical application in resource-constrained settings. This study aims to propose an efficient diagnostic deep learning model specifically designed for the classification of intracranial hemorrhage in brain CT scans. METHOD Our proposed model utilizes a combination of depthwise separable convolutions and a multi-receptive field mechanism to achieve a trade-off between performance and computational efficiency. The model was trained on RSNA datasets and validated on CQ500 dataset and PhysioNet dataset. RESULT Through a comprehensive comparison with state-of-the-art models, our model achieves an average AUROC score of 0.952 on RSNA datasets and exhibits robust generalization capabilities, comparable to SE-ResNeXt, across other open datasets. Furthermore, the parameter count of our model is just 3 % of that of MobileNet V3. CONCLUSION This study presents a diagnostic deep-learning model that is optimized for classifying intracranial hemorrhages in brain CT scans. The efficient characteristics make our proposed model highly promising for broader applications in medical settings.
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Affiliation(s)
- Yu-Ruei Chen
- School of Medicine, Chang Gung University, Taoyuan, Taiwan; Medical Education Department, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Chih-Chieh Chen
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Chang-Fu Kuo
- Medical Education Department, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Ching-Heng Lin
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan.
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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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Klempka A, Schröder A, Neumayer P, Groden C, Clausen S, Hetjens S. Cranial Computer Tomography with Photon Counting and Energy-Integrated Detectors: Objective Comparison in the Same Patients. Diagnostics (Basel) 2024; 14:1019. [PMID: 38786317 PMCID: PMC11119038 DOI: 10.3390/diagnostics14101019] [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: 04/03/2024] [Revised: 05/10/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024] Open
Abstract
This study provides an objective comparison of cranial computed tomography (CT) imaging quality and radiation dose between photon counting detectors (PCCTs) and energy-integrated detectors (EIDs). We retrospectively analyzed 158 CT scans from 76 patients, employing both detector types on the same individuals to ensure a consistent comparison. Our analysis focused on the Computed Tomography Dose Index and the Dose-Length Product together with the contrast-to-noise ratio and the signal-to-noise ratio for brain gray and white matter. We utilized standardized imaging protocols and consistent patient positioning to minimize variables. PCCT showed a potential for higher image quality and lower radiation doses, as highlighted by this study, thus achieving diagnostic clarity with reduced radiation exposure, underlining its significance in patient care, particularly for patients requiring multiple scans. The results demonstrated that while both systems were effective, PCCT offered enhanced imaging and patient safety in neuroradiological evaluations.
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Affiliation(s)
- Anna Klempka
- Department of Neuroradiology, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, 68167 Mannheim, Germany
| | - Alexander Schröder
- Department of Neuroradiology, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, 68167 Mannheim, Germany
| | - Philipp Neumayer
- Department of Neuroradiology, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, 68167 Mannheim, Germany
| | - Christoph Groden
- Department of Neuroradiology, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, 68167 Mannheim, Germany
| | - Sven Clausen
- Department of Radiation Oncology, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, 68167 Mannheim, Germany
| | - Svetlana Hetjens
- Department of Medical Statistics and Biomathematics, Medical Faculty Mannheim, University of Heidelberg, 68167 Mannheim, Germany
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Maciag EJ, Martín-Noguerol T, Ortiz-Pérez S, Torres C, Luna A. Understanding Visual Disorders through Correlation of Clinical and Radiologic Findings. Radiographics 2024; 44:e230081. [PMID: 38271255 DOI: 10.1148/rg.230081] [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: 01/27/2024]
Abstract
Patients presenting with visual disturbances often require a neuroimaging approach. The spectrum of visual disturbances includes three main categories: vision impairment, ocular motility dysfunction, and abnormal pupillary response. Decreased vision is usually due to an eye abnormality. However, it can also be related to other disorders affecting the visual pathway, from the retina to the occipital lobe. Ocular motility dysfunction may follow disorders of the cranial nerves responsible for eye movements (ie, oculomotor, trochlear, and abducens nerves); may be due to any abnormality that directly affects the extraocular muscles, such as tumor or inflammation; or may result from any orbital disease that can alter the anatomy or function of these muscles, leading to diplopia and strabismus. Given that pupillary response depends on the normal function of the sympathetic and parasympathetic pathways, an abnormality affecting these neuronal systems manifests, respectively, as pupillary miosis or mydriasis, with other related symptoms. In some cases, neuroimaging studies must complement the clinical ophthalmologic examination to better assess the anatomic and pathologic conditions that could explain the symptoms. US has a major role in the assessment of diseases of the eye and anterior orbit. CT is usually the first-line imaging modality because of its attainability, especially in trauma settings. MRI offers further information for inflammatory and tumoral cases. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.
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Affiliation(s)
- Ewa J Maciag
- From the Department of Radiology, MRI Unit, SERCOSA, HT médica, Clínica Las Nieves, Carmelo Torres 2, 23007 Jaén, Spain (E.J.M., T.M.N., A.L.); Department of Ophthalmology, Hospital Virgen de las Nieves, Granada, Spain (S.O.P.); Department of Ophthalmology, Facultad de Medicina, Universidad de Granada, Spain (S.O.P.); Granada Vision and Eye Research Team, Instituto de Investigación Biosanitaria IBS, Granada, Spain (S.O.P.); Department of Radiology, Radiation Oncology, and Medical Physics, University of Ottawa, Ottawa, Ontario, Canada (C.T.); Department of Medical Imaging, The Ottawa Hospital, Ottawa, Ontario, Canada (C.T.); and Ottawa Hospital Research Institute OHRI and Ottawa Brain and Mind Research Institute, Ottawa, Ontario, Canada (C.T.)
| | - Teodoro Martín-Noguerol
- From the Department of Radiology, MRI Unit, SERCOSA, HT médica, Clínica Las Nieves, Carmelo Torres 2, 23007 Jaén, Spain (E.J.M., T.M.N., A.L.); Department of Ophthalmology, Hospital Virgen de las Nieves, Granada, Spain (S.O.P.); Department of Ophthalmology, Facultad de Medicina, Universidad de Granada, Spain (S.O.P.); Granada Vision and Eye Research Team, Instituto de Investigación Biosanitaria IBS, Granada, Spain (S.O.P.); Department of Radiology, Radiation Oncology, and Medical Physics, University of Ottawa, Ottawa, Ontario, Canada (C.T.); Department of Medical Imaging, The Ottawa Hospital, Ottawa, Ontario, Canada (C.T.); and Ottawa Hospital Research Institute OHRI and Ottawa Brain and Mind Research Institute, Ottawa, Ontario, Canada (C.T.)
| | - Santiago Ortiz-Pérez
- From the Department of Radiology, MRI Unit, SERCOSA, HT médica, Clínica Las Nieves, Carmelo Torres 2, 23007 Jaén, Spain (E.J.M., T.M.N., A.L.); Department of Ophthalmology, Hospital Virgen de las Nieves, Granada, Spain (S.O.P.); Department of Ophthalmology, Facultad de Medicina, Universidad de Granada, Spain (S.O.P.); Granada Vision and Eye Research Team, Instituto de Investigación Biosanitaria IBS, Granada, Spain (S.O.P.); Department of Radiology, Radiation Oncology, and Medical Physics, University of Ottawa, Ottawa, Ontario, Canada (C.T.); Department of Medical Imaging, The Ottawa Hospital, Ottawa, Ontario, Canada (C.T.); and Ottawa Hospital Research Institute OHRI and Ottawa Brain and Mind Research Institute, Ottawa, Ontario, Canada (C.T.)
| | - Carlos Torres
- From the Department of Radiology, MRI Unit, SERCOSA, HT médica, Clínica Las Nieves, Carmelo Torres 2, 23007 Jaén, Spain (E.J.M., T.M.N., A.L.); Department of Ophthalmology, Hospital Virgen de las Nieves, Granada, Spain (S.O.P.); Department of Ophthalmology, Facultad de Medicina, Universidad de Granada, Spain (S.O.P.); Granada Vision and Eye Research Team, Instituto de Investigación Biosanitaria IBS, Granada, Spain (S.O.P.); Department of Radiology, Radiation Oncology, and Medical Physics, University of Ottawa, Ottawa, Ontario, Canada (C.T.); Department of Medical Imaging, The Ottawa Hospital, Ottawa, Ontario, Canada (C.T.); and Ottawa Hospital Research Institute OHRI and Ottawa Brain and Mind Research Institute, Ottawa, Ontario, Canada (C.T.)
| | - Antonio Luna
- From the Department of Radiology, MRI Unit, SERCOSA, HT médica, Clínica Las Nieves, Carmelo Torres 2, 23007 Jaén, Spain (E.J.M., T.M.N., A.L.); Department of Ophthalmology, Hospital Virgen de las Nieves, Granada, Spain (S.O.P.); Department of Ophthalmology, Facultad de Medicina, Universidad de Granada, Spain (S.O.P.); Granada Vision and Eye Research Team, Instituto de Investigación Biosanitaria IBS, Granada, Spain (S.O.P.); Department of Radiology, Radiation Oncology, and Medical Physics, University of Ottawa, Ottawa, Ontario, Canada (C.T.); Department of Medical Imaging, The Ottawa Hospital, Ottawa, Ontario, Canada (C.T.); and Ottawa Hospital Research Institute OHRI and Ottawa Brain and Mind Research Institute, Ottawa, Ontario, Canada (C.T.)
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Ray TR, Kellogg RT, Fargen KM, Hui F, Vargas J. The perils and promises of generative artificial intelligence in neurointerventional surgery. J Neurointerv Surg 2023; 16:4-7. [PMID: 37438101 DOI: 10.1136/jnis-2023-020353] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/26/2023] [Indexed: 07/14/2023]
Abstract
Generative artificial intelligence (AI) holds great promise in neurointerventional surgery by providing clinicians with powerful tools for improving surgical precision, accuracy of diagnoses, and treatment planning. However, potential perils include biases or inaccuracies in the data used to train the algorithms, over-reliance on generative AI without human oversight, patient privacy concerns, and ethical implications of using AI in medical decision-making. Careful regulation and oversight are needed to ensure that the promises of generative AI in neurointerventional surgery are realized while minimizing its potential perils.[ChatGPT authored summary using the prompt "In one paragraph summarize the promises and perils of generative AI in neurointerventional surgery".].
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Affiliation(s)
- Tyler R Ray
- Department of Mechanical Engineering, University of Hawaii at Mānoa College of Engineering, Honolulu, Hawaii, USA
| | - Ryan T Kellogg
- Department of Neurosurgery, University of Virginia, Charlottesville, Virginia, USA
| | - Kyle M Fargen
- Department of Neurological Surgery and Radiology, Wake Forest University, Winston-Salem, North Carolina, USA
| | - Ferdinand Hui
- Neurointerventional Surgery, Queen's Medical Center Neuroscience Institute, Honolulu, Hawaii, USA
| | - Jan Vargas
- Division of Neurosurgery, Prisma Health Upstate, Greenville, South Carolina, USA
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Salman S, Gu Q, Sharma R, Wei Y, Dherin B, Reddy S, Tawk R, Freeman WD. Artificial intelligence and machine learning in aneurysmal subarachnoid hemorrhage: Future promises, perils, and practicalities. J Neurol Sci 2023; 454:120832. [PMID: 37865003 DOI: 10.1016/j.jns.2023.120832] [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: 06/14/2023] [Revised: 10/04/2023] [Accepted: 10/08/2023] [Indexed: 10/23/2023]
Abstract
INTRODUCTION Aneurysmal subarachnoid hemorrhage (SAH) is a subtype of hemorrhagic stroke with thirty-day mortality as high as 40%. Given the expansion of Machine Learning (ML) and Artificial intelligence (AI) methods in health care, SAH patients desperately need an integrated AI system that detects, segments, and supports clinical decisions based on presentation and severity. OBJECTIVES This review aims to synthesize the current state of the art of AI and ML tools for the management of SAH patients alongside providing an up-to-date account of future horizons in patient care. METHODS We performed a systematic review through various databases such as Cochrane Central Register of Controlled Trials, MEDLINE, Scopus, Cochrane Database of Systematic Reviews, and Embase. RESULTS A total of 507 articles were identified. Following extensive revision, only 21 articles were relevant. Two studies reported improved mortality prediction using Glasgow Coma Scale and biomarkers such as Neutrophil to Lymphocyte Ratio and glucose. One study reported that ffANN is equal to the SAHIT and VASOGRADE scores. One study reported that metabolic biomarkers Ornithine, Symmetric Dimethylarginine, and Dimethylguanidine Valeric acid were associated with poor outcomes. Nine studies reported improved prediction of complications and reduction in latency until intervention using clinical scores and imaging. Four studies reported accurate prediction of aneurysmal rupture based on size, shape, and CNN. One study reported AI-assisted Robotic Transcranial Doppler as a substitute for clinicians. CONCLUSION AI/ML technologies possess tremendous potential in accelerating SAH systems-of-care. Keeping abreast of developments is vital in advancing timely interventions for critical diseases.
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Affiliation(s)
- Saif Salman
- Department of Neurological Surgery, Neurology and Critical Care, Mayo Clinic, Jacksonville, FL 32224, United States of America
| | - Qiangqiang Gu
- Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN 55902, United States of America
| | - Rohan Sharma
- Department of Neurological Surgery, Neurology and Critical Care, Mayo Clinic, Jacksonville, FL 32224, United States of America
| | - Yujia Wei
- Artificial Intelligence (AI) Laboratory, Department of Radiology, Mayo Clinic, Rochester, MN 55905, United States of America
| | - Benoit Dherin
- Google, Inc., Mountain View, CA 94043, United States of America
| | - Sanjana Reddy
- Google, Inc., Mountain View, CA 94043, United States of America
| | - Rabih Tawk
- Department of Neurological Surgery, Neurology and Critical Care, Mayo Clinic, Jacksonville, FL 32224, United States of America
| | - W David Freeman
- Department of Neurological Surgery, Neurology and Critical Care, Mayo Clinic, Jacksonville, FL 32224, United States of America.
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Hu P, Zhou H, Yan T, Miu H, Xiao F, Zhu X, Shu L, Yang S, Jin R, Dou W, Ren B, Zhu L, Liu W, Zhang Y, Zeng K, Ye M, Lv S, Wu M, Deng G, Hu R, Zhan R, Chen Q, Zhang D, Zhu X. Deep learning-assisted identification and quantification of aneurysmal subarachnoid hemorrhage in non-contrast CT scans: Development and external validation of Hybrid 2D/3D UNet. Neuroimage 2023; 279:120321. [PMID: 37574119 DOI: 10.1016/j.neuroimage.2023.120321] [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: 06/27/2023] [Revised: 08/01/2023] [Accepted: 08/10/2023] [Indexed: 08/15/2023] Open
Abstract
Accurate stroke assessment and consequent favorable clinical outcomes rely on the early identification and quantification of aneurysmal subarachnoid hemorrhage (aSAH) in non-contrast computed tomography (NCCT) images. However, hemorrhagic lesions can be complex and difficult to distinguish manually. To solve these problems, here we propose a novel Hybrid 2D/3D UNet deep-learning framework for automatic aSAH identification and quantification in NCCT images. We evaluated 1824 consecutive patients admitted with aSAH to four hospitals in China between June 2018 and May 2022. Accuracy and precision, Dice scores and intersection over union (IoU), and interclass correlation coefficients (ICC) were calculated to assess model performance, segmentation performance, and correlations between automatic and manual segmentation, respectively. A total of 1355 patients with aSAH were enrolled: 931, 101, 179, and 144 in four datasets, of whom 326 were scanned with Siemens, 640 with Philips, and 389 with GE Medical Systems scanners. Our proposed deep-learning method accurately identified (accuracies 0.993-0.999) and segmented (Dice scores 0.550-0.897) hemorrhage in both the internal and external datasets, even combinations of hemorrhage subtypes. We further developed a convenient AI-assisted platform based on our algorithm to assist clinical workflows, whose performance was comparable to manual measurements by experienced neurosurgeons (ICCs 0.815-0.957) but with greater efficiency and reduced cost. While this tool has not yet been prospectively tested in clinical practice, our innovative hybrid network algorithm and platform can accurately identify and quantify aSAH, paving the way for fast and cheap NCCT interpretation and a reliable AI-based approach to expedite clinical decision-making for aSAH patients.
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Affiliation(s)
- Ping Hu
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China; Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases, Nanchang, Jiangxi 330006, China; Jiangxi Health Commission Key Laboratory of Neurological Medicine, Nanchang, Jiangxi 330006, China; Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Haizhu Zhou
- School of Physics and Technology, Wuhan University, Wuhan, Hubei 430060, China
| | - Tengfeng Yan
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China; Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases, Nanchang, Jiangxi 330006, China; Jiangxi Health Commission Key Laboratory of Neurological Medicine, Nanchang, Jiangxi 330006, China; Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Hongping Miu
- Department of Neurosurgery, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China
| | - Feng Xiao
- Department of Neurosurgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310003, China
| | - Xinyi Zhu
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, China
| | - Lei Shu
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China; Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases, Nanchang, Jiangxi 330006, China; Jiangxi Health Commission Key Laboratory of Neurological Medicine, Nanchang, Jiangxi 330006, China; Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Shuang Yang
- School of Physics and Technology, Wuhan University, Wuhan, Hubei 430060, China
| | - Ruiyun Jin
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China
| | - Wenlei Dou
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China
| | - Baoyu Ren
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China
| | - Lizhen Zhu
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China
| | - Wanrong Liu
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China
| | - Yihan Zhang
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China
| | - Kaisheng Zeng
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China
| | - Minhua Ye
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China
| | - Shigang Lv
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China
| | - Miaojing Wu
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China
| | - Gang Deng
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, China
| | - Rong Hu
- Department of Neurosurgery, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China
| | - Renya Zhan
- Department of Neurosurgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310003, China
| | - Qianxue Chen
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, China
| | - Dong Zhang
- School of Physics and Technology, Wuhan University, Wuhan, Hubei 430060, China
| | - Xingen Zhu
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China; Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases, Nanchang, Jiangxi 330006, China; Jiangxi Health Commission Key Laboratory of Neurological Medicine, Nanchang, Jiangxi 330006, China; Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi 330006, China.
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Wang D, Jin R, Shieh CC, Ng AY, Pham H, Dugal T, Barnett M, Winoto L, Wang C, Barnett Y. Real world validation of an AI-based CT hemorrhage detection tool. Front Neurol 2023; 14:1177723. [PMID: 37602253 PMCID: PMC10435741 DOI: 10.3389/fneur.2023.1177723] [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: 03/01/2023] [Accepted: 07/12/2023] [Indexed: 08/22/2023] Open
Abstract
Introduction Intracranial hemorrhage (ICH) is a potentially life-threatening medical event that requires expedited diagnosis with computed tomography (CT). Automated medical imaging triaging tools can rapidly bring scans containing critical abnormalities, such as ICH, to the attention of radiologists and clinicians. Here, we retrospectively investigated the real-world performance of VeriScout™, an artificial intelligence-based CT hemorrhage detection and triage tool. Methods Ground truth for the presence or absence of ICH was iteratively determined by expert consensus in an unselected dataset of 527 consecutively acquired non-contrast head CT scans, which were sub-grouped according to the presence of artefact, post-operative features and referral source. The performance of VeriScout™ was compared with the ground truths for all groups. Results VeriScout™ detected hemorrhage with a sensitivity of 0.92 (CI 0.84-0.96) and a specificity of 0.96 (CI 0.94-0.98) in the global dataset, exceeding the sensitivity of general radiologists (0.88) with only a minor relative decrement in specificity (0.98). Crucially, the AI tool detected 13/14 cases of subarachnoid hemorrhage, a potentially fatal condition that is often missed in emergency department settings. There was no decrement in the performance of VeriScout™ in scans containing artefact or postoperative change. Using an integrated informatics platform, VeriScout™ was deployed into the existing radiology workflow. Detected hemorrhage cases were flagged in the hospital radiology information system (RIS) and relevant, annotated, preview images made available in the picture archiving and communications system (PACS) within 10 min. Conclusion AI-based radiology worklist prioritization for critical abnormalities, such as ICH, may enhance patient care without adding to radiologist or clinician burden.
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Affiliation(s)
- Dongang Wang
- Sydney Neuroimaging Analysis Centre, Camperdown, NSW, Australia
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - Ruilin Jin
- Department of Medical Imaging, St. Vincent’s Hospital, Sydney, NSW, Australia
| | | | - Adrian Y. Ng
- Emergency Department, St. Vincent’s Hospital, Sydney, NSW, Australia
| | - Hiep Pham
- Department of Medical Imaging, St. Vincent’s Hospital, Sydney, NSW, Australia
| | - Tej Dugal
- Sydney Neuroimaging Analysis Centre, Camperdown, NSW, Australia
| | - Michael Barnett
- Sydney Neuroimaging Analysis Centre, Camperdown, NSW, Australia
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - Luis Winoto
- Emergency Department, St. Vincent’s Hospital, Sydney, NSW, Australia
| | - Chenyu Wang
- Sydney Neuroimaging Analysis Centre, Camperdown, NSW, Australia
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - Yael Barnett
- Sydney Neuroimaging Analysis Centre, Camperdown, NSW, Australia
- Department of Medical Imaging, St. Vincent’s Hospital, Sydney, NSW, Australia
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9
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Chandrabhatla AS, Kuo EA, Sokolowski JD, Kellogg RT, Park M, Mastorakos P. Artificial Intelligence and Machine Learning in the Diagnosis and Management of Stroke: A Narrative Review of United States Food and Drug Administration-Approved Technologies. J Clin Med 2023; 12:jcm12113755. [PMID: 37297949 DOI: 10.3390/jcm12113755] [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: 04/30/2023] [Revised: 05/22/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
Stroke is an emergency in which delays in treatment can lead to significant loss of neurological function and be fatal. Technologies that increase the speed and accuracy of stroke diagnosis or assist in post-stroke rehabilitation can improve patient outcomes. No resource exists that comprehensively assesses artificial intelligence/machine learning (AI/ML)-enabled technologies indicated for the management of ischemic and hemorrhagic stroke. We queried a United States Food and Drug Administration (FDA) database, along with PubMed and private company websites, to identify the recent literature assessing the clinical performance of FDA-approved AI/ML-enabled technologies. The FDA has approved 22 AI/ML-enabled technologies that triage brain imaging for more immediate diagnosis or promote post-stroke neurological/functional recovery. Technologies that assist with diagnosis predominantly use convolutional neural networks to identify abnormal brain images (e.g., CT perfusion). These technologies perform comparably to neuroradiologists, improve clinical workflows (e.g., time from scan acquisition to reading), and improve patient outcomes (e.g., days spent in the neurological ICU). Two devices are indicated for post-stroke rehabilitation by leveraging neuromodulation techniques. Multiple FDA-approved technologies exist that can help clinicians better diagnose and manage stroke. This review summarizes the most up-to-date literature regarding the functionality, performance, and utility of these technologies so clinicians can make informed decisions when using them in practice.
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Affiliation(s)
- Anirudha S Chandrabhatla
- School of Medicine, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Elyse A Kuo
- School of Medicine, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Jennifer D Sokolowski
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Ryan T Kellogg
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Min Park
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Panagiotis Mastorakos
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
- Department of Neurological Surgery, Thomas Jefferson University Hospital, 111 S 11th Street, Philadelphia, PA 19107, USA
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10
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Rusche T, Wasserthal J, Breit HC, Fischer U, Guzman R, Fiehler J, Psychogios MN, Sporns PB. Machine Learning for Onset Prediction of Patients with Intracerebral Hemorrhage. J Clin Med 2023; 12:jcm12072631. [PMID: 37048712 PMCID: PMC10094957 DOI: 10.3390/jcm12072631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/13/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023] Open
Abstract
Objective: Intracerebral hemorrhage (ICH) has a high mortality and long-term morbidity and thus has a significant overall health–economic impact. Outcomes are especially poor if the exact onset is unknown, but reliable imaging-based methods for onset estimation have not been established. We hypothesized that onset prediction of patients with ICH using artificial intelligence (AI) may be more accurate than human readers. Material and Methods: A total of 7421 computed tomography (CT) datasets between January 2007–July 2021 from the University Hospital Basel with confirmed ICH were extracted and an ICH-segmentation algorithm as well as two classifiers (one with radiomics, one with convolutional neural networks) for onset estimation were trained. The classifiers were trained based on the gold standard of 644 datasets with a known onset of >1 and <48 h. The results of the classifiers were compared to the ratings of two radiologists. Results: Both the AI-based classifiers and the radiologists had poor discrimination of the known onsets, with a mean absolute error (MAE) of 9.77 h (95% CI (confidence interval) = 8.52–11.03) for the convolutional neural network (CNN), 9.96 h (8.68–11.32) for the radiomics model, 13.38 h (11.21–15.74) for rater 1 and 11.21 h (9.61–12.90) for rater 2, respectively. The results of the CNN and radiomics model were both not significantly different to the mean of the known onsets (p = 0.705 and p = 0.423). Conclusions: In our study, the discriminatory power of AI-based classifiers and human readers for onset estimation of patients with ICH was poor. This indicates that accurate AI-based onset estimation of patients with ICH based only on CT-data may be unlikely to change clinical decision making in the near future. Perhaps multimodal AI-based approaches could improve ICH onset prediction and should be considered in future studies.
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Affiliation(s)
- Thilo Rusche
- Department of Neuroradiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland
- Correspondence:
| | - Jakob Wasserthal
- Department of Neuroradiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland
| | - Hanns-Christian Breit
- Department of Neuroradiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland
| | - Urs Fischer
- Department of Neurology, University Hospital Basel, 4031 Basel, Switzerland
| | - Raphael Guzman
- Department of Neurosurgery, University Hospital Basel, 4031 Basel, Switzerland
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, 55131 Hamburg, Germany
| | - Marios-Nikos Psychogios
- Department of Neuroradiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland
| | - Peter B. Sporns
- Department of Neuroradiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, 55131 Hamburg, Germany
- Department of Radiology and Neuroradiology, Stadtspital Zürich, 8063 Zürich, Switzerland
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11
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Yoon BC, Pomerantz SR, Mercaldo ND, Goyal S, L’Italien EM, Lev MH, Buch KA, Buchbinder BR, Chen JW, Conklin J, Gupta R, Hunter GJ, Kamalian SC, Kelly HR, Rapalino O, Rincon SP, Romero JM, He J, Schaefer PW, Do S, González RG. Incorporating algorithmic uncertainty into a clinical machine deep learning algorithm for urgent head CTs. PLoS One 2023; 18:e0281900. [PMID: 36913348 PMCID: PMC10010506 DOI: 10.1371/journal.pone.0281900] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 02/03/2023] [Indexed: 03/14/2023] Open
Abstract
Machine learning (ML) algorithms to detect critical findings on head CTs may expedite patient management. Most ML algorithms for diagnostic imaging analysis utilize dichotomous classifications to determine whether a specific abnormality is present. However, imaging findings may be indeterminate, and algorithmic inferences may have substantial uncertainty. We incorporated awareness of uncertainty into an ML algorithm that detects intracranial hemorrhage or other urgent intracranial abnormalities and evaluated prospectively identified, 1000 consecutive noncontrast head CTs assigned to Emergency Department Neuroradiology for interpretation. The algorithm classified the scans into high (IC+) and low (IC-) probabilities for intracranial hemorrhage or other urgent abnormalities. All other cases were designated as No Prediction (NP) by the algorithm. The positive predictive value for IC+ cases (N = 103) was 0.91 (CI: 0.84-0.96), and the negative predictive value for IC- cases (N = 729) was 0.94 (0.91-0.96). Admission, neurosurgical intervention, and 30-day mortality rates for IC+ was 75% (63-84), 35% (24-47), and 10% (4-20), compared to 43% (40-47), 4% (3-6), and 3% (2-5) for IC-. There were 168 NP cases, of which 32% had intracranial hemorrhage or other urgent abnormalities, 31% had artifacts and postoperative changes, and 29% had no abnormalities. An ML algorithm incorporating uncertainty classified most head CTs into clinically relevant groups with high predictive values and may help accelerate the management of patients with intracranial hemorrhage or other urgent intracranial abnormalities.
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Affiliation(s)
- Byung C. Yoon
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Stuart R. Pomerantz
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
- Mass General Brigham Data Science Office, Boston, MA, United States of America
| | - Nathaniel D. Mercaldo
- Massachusetts General Hospital Institute for Technology Assessment, Boston, MA, United States of America
| | - Swati Goyal
- Mass General Brigham Data Science Office, Boston, MA, United States of America
- Department of Radiology/ Information Systems, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Eric M. L’Italien
- Mass General Brigham Data Science Office, Boston, MA, United States of America
- Department of Radiology/ Information Systems, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Michael H. Lev
- Emergency Radiology & Neuroradiology Divisions, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Karen A. Buch
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Bradley R. Buchbinder
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - John W. Chen
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
- Massachusetts General Hospital Center for Systems Biology (CSB), Boston, MA, United States of America
| | - John Conklin
- Emergency Radiology & Neuroradiology Divisions, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Rajiv Gupta
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
- Massachusetts General Hospital Consortia for Integration of Medicine and Innovative Technologies (CIMIT), Boston, MA, United States of America
- Massachusetts General Hospital CT Innovation and Advanced X-ray Imaging Science (AXIS) Center, Boston, MA, United States of America
| | - George J. Hunter
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Shahmir C. Kamalian
- Emergency Radiology & Neuroradiology Divisions, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Hillary R. Kelly
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
- Department of Radiology, Massachusetts Eye and Ear Institute, Harvard Medical School, Boston, MA, United States of America
| | - Otto Rapalino
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Sandra P. Rincon
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Javier M. Romero
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Julian He
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Pamela W. Schaefer
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
- Mass General Brigham Enterprise Radiology, Boston, MA, United States of America
| | - Synho Do
- Mass General Brigham Data Science Office, Boston, MA, United States of America
| | - Ramon Gilberto González
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
- Mass General Brigham Data Science Office, Boston, MA, United States of America
- Massachusetts General Hospital Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, United States of America
- * E-mail:
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12
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Cellina M, Cè M, Irmici G, Ascenti V, Caloro E, Bianchi L, Pellegrino G, D’Amico N, Papa S, Carrafiello G. Artificial Intelligence in Emergency Radiology: Where Are We Going? Diagnostics (Basel) 2022; 12:diagnostics12123223. [PMID: 36553230 PMCID: PMC9777804 DOI: 10.3390/diagnostics12123223] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/11/2022] [Accepted: 12/16/2022] [Indexed: 12/23/2022] Open
Abstract
Emergency Radiology is a unique branch of imaging, as rapidity in the diagnosis and management of different pathologies is essential to saving patients' lives. Artificial Intelligence (AI) has many potential applications in emergency radiology: firstly, image acquisition can be facilitated by reducing acquisition times through automatic positioning and minimizing artifacts with AI-based reconstruction systems to optimize image quality, even in critical patients; secondly, it enables an efficient workflow (AI algorithms integrated with RIS-PACS workflow), by analyzing the characteristics and images of patients, detecting high-priority examinations and patients with emergent critical findings. Different machine and deep learning algorithms have been trained for the automated detection of different types of emergency disorders (e.g., intracranial hemorrhage, bone fractures, pneumonia), to help radiologists to detect relevant findings. AI-based smart reporting, summarizing patients' clinical data, and analyzing the grading of the imaging abnormalities, can provide an objective indicator of the disease's severity, resulting in quick and optimized treatment planning. In this review, we provide an overview of the different AI tools available in emergency radiology, to keep radiologists up to date on the current technological evolution in this field.
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Affiliation(s)
- Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121 Milan, Italy
- Correspondence:
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Giovanni Irmici
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Velio Ascenti
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Elena Caloro
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Lorenzo Bianchi
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Giuseppe Pellegrino
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Natascha D’Amico
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano, Via Saint Bon 20, 20147 Milan, Italy
| | - Sergio Papa
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano, Via Saint Bon 20, 20147 Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
- Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Via Sforza 35, 20122 Milan, Italy
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13
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Hillal A, Ullberg T, Ramgren B, Wassélius J. Computed tomography in acute intracerebral hemorrhage: neuroimaging predictors of hematoma expansion and outcome. Insights Imaging 2022; 13:180. [DOI: 10.1186/s13244-022-01309-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 09/24/2022] [Indexed: 11/24/2022] Open
Abstract
AbstractIntracerebral hemorrhage (ICH) accounts for 10–20% of all strokes worldwide and is associated with serious outcomes, including a 30-day mortality rate of up to 40%. Neuroimaging is pivotal in diagnosing ICH as early detection and determination of underlying cause, and risk for expansion/rebleeding is essential in providing the correct treatment. Non-contrast computed tomography (NCCT) is the most used modality for detection of ICH, identification of prognostic markers and measurements of hematoma volume, all of which are of major importance to predict outcome. The strongest predictors of 30-day mortality and functional outcome for ICH patients are baseline hematoma volume and hematoma expansion. Even so, exact hematoma measurement is rare in clinical routine practice, primarily due to a lack of tools available for fast, effective, and reliable volumetric tools. In this educational review, we discuss neuroimaging findings for ICH from NCCT images, and their prognostic value, as well as the use of semi-automatic and fully automated hematoma volumetric methods and assessment of hematoma expansion in prognostic studies.
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14
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Nizarudeen S, Shunmugavel GR. Multi-Layer ResNet-DenseNet architecture in consort with the XgBoost classifier for intracranial hemorrhage (ICH) subtype detection and classification. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-221177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Intracerebral haemorrhage (ICH) is defined as bleeding occurs in the brain and causes vascular abnormality, tumor, venous Infarction, therapeutic anticoagulation, trauma property, and cerebral aneurysm. It is a dangerous disease and increases high mortality rate within the age of 15 to 24. It may be cured by finding what type of ICH is affected in the brain within short period with more accuracy. The previous method did not provide adequate accuracy and increase the computational time. Therefore, in this manuscript Detection and Categorization of Acute Intracranial Hemorrhage (ICH) subtypes using a Multi-Layer DenseNet-ResNet Architecture with Improved Random Forest Classifier (IRF) is proposed to detect the subtypes of ICH with high accuracy, less computational time with maximal speed. Here, the brain CT images are collected from Physionet repository publicly dataset. Then the images are pre-processed to eliminate the noises. After that, the image features are extracted by using multi layer Densely Connected Convolutional Network (DenseNet) combined with Residual Network (ResNet) architecture with multiple Convolutional layers. The sub types of ICH (Epidural Hemorrhage (EDH), Subarachnoid Hemorrhage (SAH), Intracerebral Hemorrhage (ICH), Subdural Hemorrhage (SDH), Intraventricular Hemorrhage (IVH), normal is classified by using Improved Random Forest (IRF) Classifier with high accuracy. The simulation is activated in MATLAB platform. The proposed Multilayer-DenseNet-ResNet-IRF approach attains higher accuracy 23.44%, 31.93%, 42.83%, 41.9% compared with existing approaches, like Detection with classification of intracranial haemorrhage on CT images utilizing new deep-learning algorithm (ICH-DC-CNN), Detection with classification of intracranial haemorrhage on CT images utilizing new deep-learning algorithm (ICH-DC-CNN-ResNet-50), Shallow 3D CNN for detecting acute brain hemorrhage from medical imaging sensors (ICH-DC-S-3D-CNN), Convolutional neural network: a review of models, methods and applications to object detection (ICH-DC-CNN-AlexNet) respectively.
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Affiliation(s)
- Shanu Nizarudeen
- Department of Electronics and Communication Engineering, College of Engineering Karunagapally, Thodiyoor, Kollam, Karunagappalli, Kerala, India
| | - Ganesh R. Shunmugavel
- Department of Electronics and Communication Engineering, NICHE, Kumaracoil, Tamil Nadu, India
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15
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Peng Q, Chen X, Zhang C, Li W, Liu J, Shi T, Wu Y, Feng H, Nian Y, Hu R. Deep learning-based computed tomography image segmentation and volume measurement of intracerebral hemorrhage. Front Neurosci 2022; 16:965680. [PMID: 36263364 PMCID: PMC9575984 DOI: 10.3389/fnins.2022.965680] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 09/06/2022] [Indexed: 11/25/2022] Open
Abstract
The study aims to enhance the accuracy and practicability of CT image segmentation and volume measurement of ICH by using deep learning technology. A dataset including the brain CT images and clinical data of 1,027 patients with spontaneous ICHs treated from January 2010 to December 2020 were retrospectively analyzed, and a deep segmentation network (AttFocusNet) integrating the focus structure and the attention gate (AG) mechanism is proposed to enable automatic, accurate CT image segmentation and volume measurement of ICHs. In internal validation set, experimental results showed that AttFocusNet achieved a Dice coefficient of 0.908, an intersection-over-union (IoU) of 0.874, a sensitivity of 0.913, a positive predictive value (PPV) of 0.957, and a 95% Hausdorff distance (HD95) (mm) of 5.960. The intraclass correlation coefficient (ICC) of the ICH volume measurement between AttFocusNet and the ground truth was 0.997. The average time of per case achieved by AttFocusNet, Coniglobus formula and manual segmentation is 5.6, 47.7, and 170.1 s. In the two external validation sets, AttFocusNet achieved a Dice coefficient of 0.889 and 0.911, respectively, an IoU of 0.800 and 0.836, respectively, a sensitivity of 0.817 and 0.849, respectively, a PPV of 0.976 and 0.981, respectively, and a HD95 of 5.331 and 4.220, respectively. The ICC of the ICH volume measurement between AttFocusNet and the ground truth were 0.939 and 0.956, respectively. The proposed segmentation network AttFocusNet significantly outperforms the Coniglobus formula in terms of ICH segmentation and volume measurement by acquiring measurement results closer to the true ICH volume and significantly reducing the clinical workload.
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Affiliation(s)
- Qi Peng
- Department of Digital Medicine, School of Biomedical Engineering and Imaging Medicine, Army Medical University, Third Military Medical University, Chongqing, China
| | - Xingcai Chen
- Department of Digital Medicine, School of Biomedical Engineering and Imaging Medicine, Army Medical University, Third Military Medical University, Chongqing, China
| | - Chao Zhang
- Department of Neurosurgery, First Affiliated Hospital,Southwest Hospital, Army Medical University, Third Military Medical University, Chongqing, China
| | - Wenyan Li
- Department of Neurosurgery, First Affiliated Hospital,Southwest Hospital, Army Medical University, Third Military Medical University, Chongqing, China
| | - Jingjing Liu
- Department of Digital Medicine, School of Biomedical Engineering and Imaging Medicine, Army Medical University, Third Military Medical University, Chongqing, China
| | - Tingxin Shi
- School of Basic Medicine, Army Medical University, Third Military Medical University, Chongqing, China
| | - Yi Wu
- Department of Digital Medicine, School of Biomedical Engineering and Imaging Medicine, Army Medical University, Third Military Medical University, Chongqing, China
| | - Hua Feng
- Department of Neurosurgery, First Affiliated Hospital,Southwest Hospital, Army Medical University, Third Military Medical University, Chongqing, China
| | - Yongjian Nian
- Department of Digital Medicine, School of Biomedical Engineering and Imaging Medicine, Army Medical University, Third Military Medical University, Chongqing, China
- *Correspondence: Yongjian Nian,
| | - Rong Hu
- Department of Neurosurgery, First Affiliated Hospital,Southwest Hospital, Army Medical University, Third Military Medical University, Chongqing, China
- Rong Hu,
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16
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Assessment of CT for the categorization of hemorrhagic stroke (HS) and cerebral amyloid angiopathy hemorrhage (CAAH): A review. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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17
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Intracerebral hemorrhage detection on computed tomography images using a residual neural network. Phys Med 2022; 99:113-119. [PMID: 35671679 DOI: 10.1016/j.ejmp.2022.05.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 04/23/2022] [Accepted: 05/26/2022] [Indexed: 01/31/2023] Open
Abstract
Intracerebral hemorrhage (ICH) is a high mortality rate, critical medical injury, produced by the rupture of a blood vessel of the vascular system inside the skull. ICH can lead to paralysis and even death. Therefore, it is considered a clinically dangerous disease that needs to be treated quickly. Thanks to the advancement in machine learning and the computing power of today's microprocessors, deep learning has become an unbelievably valuable tool for detecting diseases, in particular from medical images. In this work, we are interested in differentiating computer tomography (CT) images of healthy brains and ICH using a ResNet-18, a deep residual convolutional neural network. In addition, the gradient-weighted class activation mapping (Grad-CAM) technique was employed to visually explore and understand the network's decisions. The generalizability of the detector was assessed through a 100-iteration Monte Carlo cross-validation (80% of the data for training and 20% for test). In a database with 200 CT images of brains (100 with ICH and 100 without ICH), the detector yielded, on average, 95.93%accuracy, 96.20% specificity, 95.65% sensitivity, 96.40% precision, and 95.91% F1-core, with an average computing time of 165.90 s to train the network (on 160 images) and 1.17 s to test it with 40 CT images. These results are comparable with the state of the art with a simpler and lower computational load approach. Our detector could assist physicians in their medical decision, in resource optimization and in reducing the time and error in the diagnosis of ICH.
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18
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Performance of Automated RAPID Intracranial Hemorrhage Detection in Real-World Practice: A Single-Institution Experience. J Comput Assist Tomogr 2022; 46:770-774. [PMID: 35617649 DOI: 10.1097/rct.0000000000001335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND AND PURPOSE Intracranial hemorrhage (ICH) is a common finding in patients presenting to the emergency department with acute neurological symptoms. Noncontrast head computed tomography (NCCT) is the primary modality for assessment and detection of ICH in the acute setting. RAPID ICH software aims to automatically detect ICH on NCCT and was previously shown to have high accuracy when applied to a curated test data set. Here, we measured the test performance characteristics of RAPID ICH software in detecting ICH on NCCT performed in patients undergoing emergency stroke evaluation at a tertiary academic comprehensive stroke center. MATERIALS AND METHODS This retrospective study assessed consecutive patients over a 6-month period who presented with acute neurological symptoms suspicious for stroke and underwent NCCT with RAPID ICH postprocessing. RAPID ICH detection was compared with the interpretation of a reference standard comprising a board-certified or board-eligible neuroradiologist, or in cases of discrepancy, adjudicated by a consensus panel of 3 neuroradiologists. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of RAPID ICH for ICH detection were determined. RESULTS Three hundred seven NCCT scans were included in the study. RAPID ICH correctly identified 34 of 37 cases with ICH and 228 of 270 without ICH. RAPID ICH had a sensitivity of 91.9% (78.1%-98.3%), specificity of 84.4% (79.6%-88.6%), NPV of 98.7% (96.3%-99.6%), PPV of 44.7% (37.6%-52.1%), and overall accuracy of 85.3% (80.9%-89.1%). CONCLUSIONS In a real-world scenario, RAPID ICH software demonstrated high NPV but low PPV for the presence of ICH when evaluating possible stroke patients.
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Bravo J, Wali AR, Hirshman BR, Gopesh T, Steinberg JA, Yan B, Pannell JS, Norbash A, Friend J, Khalessi AA, Santiago-Dieppa D. Robotics and Artificial Intelligence in Endovascular Neurosurgery. Cureus 2022; 14:e23662. [PMID: 35371874 PMCID: PMC8971092 DOI: 10.7759/cureus.23662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/30/2022] [Indexed: 11/05/2022] Open
Abstract
The use of artificial intelligence (AI) and robotics in endovascular neurosurgery promises to transform neurovascular care. We present a review of the recently published neurosurgical literature on artificial intelligence and robotics in endovascular neurosurgery to provide insights into the current advances and applications of this technology. The PubMed database was searched for "neurosurgery" OR "endovascular" OR "interventional" AND "robotics" OR "artificial intelligence" between January 2016 and August 2021. A total of 1296 articles were identified, and after applying the inclusion and exclusion criteria, 38 manuscripts were selected for review and analysis. These manuscripts were divided into four categories: 1) robotics and AI for the diagnosis of cerebrovascular pathology, 2) robotics and AI for the treatment of cerebrovascular pathology, 3) robotics and AI for training in neuroendovascular procedures, and 4) robotics and AI for clinical outcome optimization. The 38 articles presented include 23 articles on AI-based diagnosis of cerebrovascular disease, 10 articles on AI-based treatment of cerebrovascular disease, two articles on AI-based training techniques for neuroendovascular procedures, and three articles reporting AI prediction models of clinical outcomes in vascular disorders of the brain. Innovation with robotics and AI focus on diagnostic efficiency, optimizing treatment and interventional procedures, improving physician procedural performance, and predicting clinical outcomes with the use of artificial intelligence and robotics. Experimental studies with robotic systems have demonstrated safety and efficacy in treating cerebrovascular disorders, and novel microcatheterization techniques may permit access to deeper brain regions. Other studies show that pre-procedural simulations increase overall physician performance. Artificial intelligence also shows superiority over existing statistical tools in predicting clinical outcomes. The recent advances and current usage of robotics and AI in the endovascular neurosurgery field suggest that the collaboration between physicians and machines has a bright future for the improvement of patient care. The aim of this work is to equip the medical readership, in particular the neurosurgical specialty, with tools to better understand and apply findings from research on artificial intelligence and robotics in endovascular neurosurgery.
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Zhao K, Zhao Q, Zhou P, Liu B, Zhang Q, Yang M. Can Artificial Intelligence Be Applied to Diagnose Intracerebral Hemorrhage under the Background of the Fourth Industrial Revolution? A Novel Systemic Review and Meta-Analysis. Int J Clin Pract 2022; 2022:9430097. [PMID: 35685590 PMCID: PMC9159188 DOI: 10.1155/2022/9430097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 01/24/2022] [Indexed: 11/17/2022] Open
Abstract
Aim We intended to provide the clinical evidence that artificial intelligence (AI) could be used to assist doctors in the diagnosis of intracerebral hemorrhage (ICH). Methods Studies published in 2021 were identified after the literature search of PubMed, Embase, and Cochrane. Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used to perform the quality assessment of studies. Data extraction of diagnosis effect included accuracy (ACC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV), area under curve (AUC), and Dice scores (Dices). The pooled effect with its 95% confidence interval (95%CI) was calculated by the random effects model. I-Square (I 2) was used to test heterogeneity. To check the stability of the overall results, sensitivity analysis was conducted by recalculating the pooled effect of the remaining studies after omitting the study with the highest quality or the random effects model was switched to the fixed effects model. Funnel plot was used to evaluate publication bias. To reduce heterogeneity, recalculating the pooled effect of the remaining studies after omitting the study with the lowest quality or perform subgroup analysis. Results Twenty-five diagnostic tests of ICH via AI and doctors with overall high quality were included. Pooled ACC, SEN, SPE, PPV, NPV, AUC, and Dices were 0.88 (0.83∼0.93), 0.85 (0.81∼0.89), 0.90 (0.88∼0.92), 0.80 (0.75∼0.85), 0.93 (0.91∼0.95), 0.84 (0.80∼0.89), and 0.90 (0.85∼0.95), respectively. There was no publication bias. All of results were stable as revealed by sensitivity analysis and were accordant as outcomes via subgroups analysis. Conclusion Under the background of the fourth industrial revolution, AI might be an effective and efficient tool to assist doctors in the clinical diagnosis of ICH.
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Affiliation(s)
- Kai Zhao
- Graduate School, Qinghai University, Xining 810016, Qinghai, China
| | - Qing Zhao
- Human Resource, Women's and Children's Hospital of Qinghai Province, Xining 810007, Qinghai, China
| | - Ping Zhou
- Department of Neurosurgery, Qinghai Provincial People's Hospital, Xining 810007, Qinghai, China
| | - Bin Liu
- Department of Neurosurgery, Qinghai Provincial People's Hospital, Xining 810007, Qinghai, China
| | - Qiang Zhang
- Department of Neurosurgery, Qinghai Provincial People's Hospital, Xining 810007, Qinghai, China
| | - Mingfei Yang
- Department of Neurosurgery, Qinghai Provincial People's Hospital, Xining 810007, Qinghai, China
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21
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Swetz D, Seymour SE, Rava RA, Shiraz Bhurwani MM, Monteiro A, Baig AA, Waqas M, Snyder KV, Levy EI, Davies JM, Siddiqui AH, Ionita CN. Initial investigation of predicting hematoma expansion for intracerebral hemorrhage using imaging biomarkers and machine learning. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12036:120360B. [PMID: 36081709 PMCID: PMC9451134 DOI: 10.1117/12.2610672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
PURPOSE Intracerebral Hemorrhage (ICH) is one of the most devastating types of strokes with mortality and morbidity rates ranging from about 51%-65% one year after diagnosis. Early hematoma expansion (HE) is a known cause of worsening neurological status of ICH patients. The goal of this study was to investigate whether non-contrast computed tomography imaging biomarkers (NCCT-IB) acquired at initial presentation can predict ICH growth in the acute stage. MATERIALS AND METHODS We retrospectively collected NCCT data from 200 patients with acute (<6 hours) ICH. Four NCCT-IBs (blending region, dark hole, island, and edema) were identified for each hematoma, respectively. HE status was recorded based on the clinical observation reported in the patient chart. Supervised machine learning models were developed, trained, and tested for 15 different input combinations of the NCCT-IBs to predict HE. Model performance was assessed using area under the receiver operating characteristic curve and probability for accurate diagnosis (PAD) was calculated. A 20-fold Monte-Carlo cross validation was implemented to ensure model reliability on a limited sample size of data, by running a myriad of random training/testing splits. RESULTS The developed algorithm was able to predict expansion utilizing all four inputs with an accuracy of 70.17%. Further testing of all biomarker combinations yielded P AD ranging from 0.57, to 0.70. CONCLUSION Specific attributes of ICHs may influence the likelihood of HE and can be evaluated via a machine learning algorithm. However, certain parameters may differ in importance to reach accurate conclusions about potential expansion.
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Affiliation(s)
- Dennis Swetz
- Department of Biomedical Engineering, University at Buffalo, Buffalo NY 14228
- Canon Stroke and Vascular Research Center, Buffalo, NY 14203
| | - Samantha E Seymour
- Department of Biomedical Engineering, University at Buffalo, Buffalo NY 14228
- Canon Stroke and Vascular Research Center, Buffalo, NY 14203
| | - Ryan A Rava
- Department of Biomedical Engineering, University at Buffalo, Buffalo NY 14228
- Canon Stroke and Vascular Research Center, Buffalo, NY 14203
| | - Mohammad Mahdi Shiraz Bhurwani
- Department of Biomedical Engineering, University at Buffalo, Buffalo NY 14228
- Canon Stroke and Vascular Research Center, Buffalo, NY 14203
| | - Andre Monteiro
- Canon Stroke and Vascular Research Center, Buffalo, NY 14203
- University at Buffalo Neurosurgery, University at Buffalo Jacobs School of Medicine, Buffalo NY 14228
| | - Ammad A Baig
- Canon Stroke and Vascular Research Center, Buffalo, NY 14203
- University at Buffalo Neurosurgery, University at Buffalo Jacobs School of Medicine, Buffalo NY 14228
| | - Muhammad Waqas
- Canon Stroke and Vascular Research Center, Buffalo, NY 14203
- University at Buffalo Neurosurgery, University at Buffalo Jacobs School of Medicine, Buffalo NY 14228
| | - Kenneth V Snyder
- Canon Stroke and Vascular Research Center, Buffalo, NY 14203
- University at Buffalo Neurosurgery, University at Buffalo Jacobs School of Medicine, Buffalo NY 14228
| | - Elad I Levy
- Canon Stroke and Vascular Research Center, Buffalo, NY 14203
- University at Buffalo Neurosurgery, University at Buffalo Jacobs School of Medicine, Buffalo NY 14228
| | - Jason M Davies
- Canon Stroke and Vascular Research Center, Buffalo, NY 14203
- University at Buffalo Neurosurgery, University at Buffalo Jacobs School of Medicine, Buffalo NY 14228
- QAS.AI Incorporated, Buffalo NY 14203
- University Dept. of Biomedical Informatics, University at Buffalo, Buffalo, NY 14214
| | - Adnan H Siddiqui
- Canon Stroke and Vascular Research Center, Buffalo, NY 14203
- University at Buffalo Neurosurgery, University at Buffalo Jacobs School of Medicine, Buffalo NY 14228
| | - Ciprian N Ionita
- Department of Biomedical Engineering, University at Buffalo, Buffalo NY 14228
- Canon Stroke and Vascular Research Center, Buffalo, NY 14203
- University at Buffalo Neurosurgery, University at Buffalo Jacobs School of Medicine, Buffalo NY 14228
- QAS.AI Incorporated, Buffalo NY 14203
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22
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Seymour SE, Rava RA, Swetz DJ, Monteiro A, Baig A, Schultz K, Snyder KV, Waqas M, Davies JM, Levy EI, Siddiqui AH, Ionita CN. Predicting Hematoma Expansion after Spontaneous Intracranial Hemorrhage Through a Radiomics Based Model. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12033:120332X. [PMID: 35990197 PMCID: PMC9390077 DOI: 10.1117/12.2611847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
PURPOSE Intracranial hemorrhage (ICH) is characterized as bleeding into the brain tissue, intracranial space, and ventricles and is the second most disabling form of stroke. Hematoma expansion (HE) following ICH has been correlated with significant neurological decline and death. For early detection of patients at risk, deep learning prediction models were developed to predict whether hematoma due to ICH will expand. This study aimed to explore the feasibility of HE prediction using a radiomic approach to help clinicians better stratify HE patients and tailor intensive therapies timely and effectively. MATERIALS AND METHODS Two hundred ICH patients with known hematoma evolution, were enrolled in this study. An open-source python package was utilized for the extraction of radiomic features from both non-contrast computed tomography (NCCT) and magnetic resonance imaging (MRI) scans through characterization algorithms. A total of 99 radiomic features were extracted and different features were selected for network inputs for the NCCT and MR models. Seven supervised classifiers: Support Vector Machine, Naïve Bayes, Decision Tree, Random Forest, Logistic Regression, K-Nearest Neighbor and Multilayer Perceptron were used to build the models. A training:testing split of 80:20 and 20 iterations of Monte Carlo cross validation were performed to prevent overfitting and assess the variability of the networks, respectively. The models were fed training datasets from which they learned to classify the data based on pre-determined radiomic categories. RESULTS The highest sensitivity among the NCCT classifier models was seen with the support vector machine (SVM) and logistic regression (LR) of 72 ± 0.3% and 73 ± 0.5%, respectively. The MRI classifier models had the highest sensitivity of 68 ± 0.5% and 72 ± 0.5% for the SVM and LR models, respectively. CONCLUSIONS This study indicates that the NCCT radiomics model is a better predictor of HE and that SVM and LR classifiers are better predictors of HE due to their more cautious approach indicated by a higher sensitivity metric.
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Affiliation(s)
- Samantha E Seymour
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY 14260, US
- Canon Stroke and Vascular Research Center, Buffalo, NY, 14203, US
| | - Ryan A Rava
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY 14260, US
- Canon Stroke and Vascular Research Center, Buffalo, NY, 14203, US
| | - Dennis J Swetz
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY 14260, US
- Canon Stroke and Vascular Research Center, Buffalo, NY, 14203, US
| | - Andre Monteiro
- Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, 14203, US
- Department of Neurosurgery, University at Buffalo, Buffalo, NY 14203, US
| | - Ammad Baig
- Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, 14203, US
- Department of Neurosurgery, University at Buffalo, Buffalo, NY 14203, US
| | - Kurt Schultz
- Canon Medical Research USA, Vernon Hills, IL 60061, US
| | - Kenneth V Snyder
- Canon Stroke and Vascular Research Center, Buffalo, NY, 14203, US
- Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, 14203, US
- Department of Neurosurgery, University at Buffalo, Buffalo, NY 14203, US
| | - Muhammad Waqas
- Canon Stroke and Vascular Research Center, Buffalo, NY, 14203, US
- Department of Neurosurgery, University at Buffalo, Buffalo, NY 14203, US
| | - Jason M Davies
- Canon Stroke and Vascular Research Center, Buffalo, NY, 14203, US
- Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, 14203, US
- Department of Neurosurgery, University at Buffalo, Buffalo, NY 14203, US
- Department of Bioinformatics, University at Buffalo, Buffalo, NY 14214, US
| | - Elad I Levy
- Canon Stroke and Vascular Research Center, Buffalo, NY, 14203, US
- Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, 14203, US
- Department of Neurosurgery, University at Buffalo, Buffalo, NY 14203, US
| | - Adnan H Siddiqui
- Canon Stroke and Vascular Research Center, Buffalo, NY, 14203, US
- Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, 14203, US
- Department of Neurosurgery, University at Buffalo, Buffalo, NY 14203, US
| | - Ciprian N Ionita
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY 14260, US
- Canon Stroke and Vascular Research Center, Buffalo, NY, 14203, US
- Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, 14203, US
- Department of Neurosurgery, University at Buffalo, Buffalo, NY 14203, US
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23
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Kundisch A, Hönning A, Mutze S, Kreissl L, Spohn F, Lemcke J, Sitz M, Sparenberg P, Goelz L. Deep learning algorithm in detecting intracranial hemorrhages on emergency computed tomographies. PLoS One 2021; 16:e0260560. [PMID: 34843559 PMCID: PMC8629230 DOI: 10.1371/journal.pone.0260560] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 10/26/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Highly accurate detection of intracranial hemorrhages (ICH) on head computed tomography (HCT) scans can prove challenging at high-volume centers. This study aimed to determine the number of additional ICHs detected by an artificial intelligence (AI) algorithm and to evaluate reasons for erroneous results at a level I trauma center with teleradiology services. METHODS In a retrospective multi-center cohort study, consecutive emergency non-contrast HCT scans were analyzed by a commercially available ICH detection software (AIDOC, Tel Aviv, Israel). Discrepancies between AI analysis and initial radiology report (RR) were reviewed by a blinded neuroradiologist to determine the number of additional ICHs detected and evaluate reasons leading to errors. RESULTS 4946 HCT (05/2020-09/2020) from 18 hospitals were included in the analysis. 205 reports (4.1%) were classified as hemorrhages by both radiology report and AI. Out of a total of 162 (3.3%) discrepant reports, 62 were confirmed as hemorrhages by the reference neuroradiologist. 33 ICHs were identified exclusively via RRs. The AI algorithm detected an additional 29 instances of ICH, missed 12.4% of ICH and overcalled 1.9%; RRs missed 10.9% of ICHs and overcalled 0.2%. Many of the ICHs missed by the AI algorithm were located in the subarachnoid space (42.4%) and under the calvaria (48.5%). 85% of ICHs missed by RRs occurred outside of regular working-hours. Calcifications (39.3%), beam-hardening artifacts (18%), tumors (15.7%), and blood vessels (7.9%) were the most common reasons for AI overcalls. ICH size, image quality, and primary examiner experience were not found to be significantly associated with likelihood of incorrect AI results. CONCLUSION Complementing human expertise with AI resulted in a 12.2% increase in ICH detection. The AI algorithm overcalled 1.9% HCT. TRIAL REGISTRATION German Clinical Trials Register (DRKS-ID: DRKS00023593).
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Affiliation(s)
- Almut Kundisch
- Center for Emergency Training, BG Klinikum Unfallkrankenhaus Berlin, Berlin, Germany
| | - Alexander Hönning
- Center for Clinical Research, BG Klinikum Unfallkrankenhaus Berlin, Berlin, Germany
| | - Sven Mutze
- Department of Radiology and Neuroradiology, BG Klinikum Unfallkrankenhaus Berlin, Berlin, Germany.,Institute for Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Lutz Kreissl
- Department of Radiology and Neuroradiology, BG Klinikum Unfallkrankenhaus Berlin, Berlin, Germany
| | - Frederik Spohn
- Department of Radiology and Neuroradiology, BG Klinikum Unfallkrankenhaus Berlin, Berlin, Germany
| | - Johannes Lemcke
- Department of Neurosurgery, BG Klinikum Unfallkrankenhaus Berlin, Berlin, Germany
| | - Maximilian Sitz
- Department of Neurosurgery, BG Klinikum Unfallkrankenhaus Berlin, Berlin, Germany
| | - Paul Sparenberg
- Department of Neurology, BG Klinikum Unfallkrankenhaus Berlin, Berlin, Germany
| | - Leonie Goelz
- Department of Radiology and Neuroradiology, BG Klinikum Unfallkrankenhaus Berlin, Berlin, Germany.,Institute for Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
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24
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Rava RA, Seymour SE, Snyder KV, Waqas M, Davies JM, Levy EI, Siddiqui AH, Ionita CN. Automated Collateral Flow Assessment in Patients with Acute Ischemic Stroke Using Computed Tomography with Artificial Intelligence Algorithms. World Neurosurg 2021; 155:e748-e760. [PMID: 34506979 DOI: 10.1016/j.wneu.2021.08.136] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 08/28/2021] [Indexed: 11/16/2022]
Abstract
BACKGROUND Collateral circulation is associated with improved functional outcome in patients with large vessel occlusion acute ischemic stroke (AIS) who undergo reperfusion therapy. Assessment of collateral flow can be time consuming, subjective, and difficult because of complex neurovasculature. This study assessed the ability of multiple artificial intelligence algorithms in determining collateral flow of patients with AIS. METHODS Two hundred patients with AIS between March 2019 and January 2020 were included in this retrospective study. Peak arterial computed tomography perfusion volumes were used to assess collateral scores. Neural networks were developed for dichotomized (≥50% or <50%) and multiclass (0% filling, 0%-50% filling, 50%-100% filling, or 100% filling) collateral scoring. Maximum intensity projections from axial and anteroposterior (AP) views were synthesized for each bone subtracted three-dimensional volume and used as network inputs separately and together, along with three-dimensional data. Training:testing:validation splits of 60:30:10 and 20 iterations of Monte Carlo cross-validation were used. Network performance was assessed using 95% confidence intervals of accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS The axial and AP input combination provided the most accurate results for dichotomized classification: accuracy, 0.85 ± 0.01; sensitivity, 0.88 ± 0.02; specificity, 0.82 ± 0.03; PPV, 0.86 ± 0.02; and NPV, 0.83 ± 0.03. Similarly, the axial and AP input combination provided the best results for multiclass classification: accuracy, 0.80 ± 0.01; sensitivity, 0.64 ± 0.01; specificity, 0.85 ± 0.01; PPV, 0.65 ± 0.02; and NPV, 0.85 ± 0.01. CONCLUSIONS This study reports one of the first artificial intelligence-based algorithms capable of accurately and efficiently assessing collateral flow of patients with AIS. This automated method for determining collateral filling could streamline clinical workflow, reduce bias, and aid in clinical decision making for determining reperfusion-eligible patients.
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Affiliation(s)
- Ryan A Rava
- Department of Biomedical Engineering, University at Buffalo, Buffalo, New York, USA; Canon Stroke and Vascular Research Center, Buffalo, New York, USA.
| | - Samantha E Seymour
- Department of Biomedical Engineering, University at Buffalo, Buffalo, New York, USA; Canon Stroke and Vascular Research Center, Buffalo, New York, USA
| | - Kenneth V Snyder
- Department of Neurosurgery, University at Buffalo, Buffalo, New York, USA; Canon Stroke and Vascular Research Center, Buffalo, New York, USA; Jacobs School of Medicine and Biomedical Sciences, Buffalo, New York, USA
| | - Muhammad Waqas
- Department of Neurosurgery, University at Buffalo, Buffalo, New York, USA; Canon Stroke and Vascular Research Center, Buffalo, New York, USA
| | - Jason M Davies
- Department of Neurosurgery, University at Buffalo, Buffalo, New York, USA; Department of Bioinformatics, University at Buffalo, Buffalo, New York, USA; Canon Stroke and Vascular Research Center, Buffalo, New York, USA; Jacobs School of Medicine and Biomedical Sciences, Buffalo, New York, USA
| | - Elad I Levy
- Department of Neurosurgery, University at Buffalo, Buffalo, New York, USA; Canon Stroke and Vascular Research Center, Buffalo, New York, USA; Jacobs School of Medicine and Biomedical Sciences, Buffalo, New York, USA
| | - Adnan H Siddiqui
- Department of Neurosurgery, University at Buffalo, Buffalo, New York, USA; Canon Stroke and Vascular Research Center, Buffalo, New York, USA; Jacobs School of Medicine and Biomedical Sciences, Buffalo, New York, USA
| | - Ciprian N Ionita
- Department of Biomedical Engineering, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, University at Buffalo, Buffalo, New York, USA; Canon Stroke and Vascular Research Center, Buffalo, New York, USA; Jacobs School of Medicine and Biomedical Sciences, Buffalo, New York, USA
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