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Ru X, Zhao S, Chen W, Wu J, Yu R, Wang D, Dong M, Wu Q, Peng D, Song Y. A weakly supervised deep learning model integrating noncontrasted computed tomography images and clinical factors facilitates haemorrhagic transformation prediction after intravenous thrombolysis in acute ischaemic stroke patients. Biomed Eng Online 2023; 22:129. [PMID: 38115029 PMCID: PMC10731772 DOI: 10.1186/s12938-023-01193-w] [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: 01/28/2023] [Accepted: 12/09/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Haemorrhage transformation (HT) is a serious complication of intravenous thrombolysis (IVT) in acute ischaemic stroke (AIS). Accurate and timely prediction of the risk of HT before IVT may change the treatment decision and improve clinical prognosis. We aimed to develop a deep learning method for predicting HT after IVT for AIS using noncontrast computed tomography (NCCT) images. METHODS We retrospectively collected data from 828 AIS patients undergoing recombinant tissue plasminogen activator (rt-PA) treatment within a 4.5-h time window (n = 665) or of undergoing urokinase treatment within a 6-h time window (n = 163) and divided them into the HT group (n = 69) and non-HT group (n = 759). HT was defined based on the criteria of the European Cooperative Acute Stroke Study-II trial. To address the problems of indiscernible features and imbalanced data, a weakly supervised deep learning (WSDL) model for HT prediction was constructed based on multiple instance learning and active learning using admission NCCT images and clinical information in addition to conventional deep learning models. Threefold cross-validation and transfer learning were performed to confirm the robustness of the network. Of note, the predictive value of the commonly used scales in clinics associated with NCCT images (i.e., the HAT and SEDAN score) was also analysed and compared to measure the feasibility of our proposed DL algorithms. RESULTS Compared to the conventional DL and ML models, the WSDL model had the highest AUC of 0.799 (95% CI 0.712-0.883). Significant differences were observed between the WSDL model and five ML models (P < 0.05). The prediction performance of the WSDL model outperforms the HAT and SEDAN scores at the optimal operating point (threshold = 1.5). Further subgroup analysis showed that the WSDL model performed better for symptomatic intracranial haemorrhage (AUC = 0.833, F1 score = 0.909). CONCLUSIONS Our WSDL model based on NCCT images had relatively good performance for predicting HT in AIS and may be suitable for assisting in clinical treatment decision-making.
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Affiliation(s)
- Xiaoshuang Ru
- Department of Radiology, Central Hospital of Dalian University of Technology, No. 826 Xinan Rd, Shahekou District, Dalian, 116033, Liaoning Province, China
| | - Shilong Zhao
- Department of Radiology, Affliated ZhongShan Hospital of Dalian University, No. 6 Jiefang Rd, Zhongshan District, Dalian, 116001, Liaoning Province, China
| | - Weidao Chen
- InferVision Medical Technology Company Ltd, 25F, Building E, Yuanyang International Center, Chaoyang District, Beijing, 100025, China
| | - Jiangfen Wu
- InferVision Medical Technology Company Ltd, 25F, Building E, Yuanyang International Center, Chaoyang District, Beijing, 100025, China
| | - Ruize Yu
- InferVision Medical Technology Company Ltd, 25F, Building E, Yuanyang International Center, Chaoyang District, Beijing, 100025, China
| | - Dawei Wang
- InferVision Medical Technology Company Ltd, 25F, Building E, Yuanyang International Center, Chaoyang District, Beijing, 100025, China
| | - Mengxing Dong
- InferVision Medical Technology Company Ltd, 25F, Building E, Yuanyang International Center, Chaoyang District, Beijing, 100025, China
| | - Qiong Wu
- Department of Neurology, Central Hospital of Dalian University of Technology, No. 826 Xinan Rd, Shahekou District, Dalian, 116033, Liaoning Province, China
| | - Daoyong Peng
- Department of Neurology, Central Hospital of Dalian University of Technology, No. 826 Xinan Rd, Shahekou District, Dalian, 116033, Liaoning Province, China
| | - Yang Song
- Department of Radiology, Central Hospital of Dalian University of Technology, No. 826 Xinan Rd, Shahekou District, Dalian, 116033, Liaoning Province, China.
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Dasari Y, Duffin J, Sayin ES, Levine HT, Poublanc J, Para AE, Mikulis DJ, Fisher JA, Sobczyk O, Khamesee MB. Convolutional Neural Networks to Assess Steno-Occlusive Disease Using Cerebrovascular Reactivity. Healthcare (Basel) 2023; 11:2231. [PMID: 37628429 PMCID: PMC10454585 DOI: 10.3390/healthcare11162231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 07/31/2023] [Accepted: 08/05/2023] [Indexed: 08/27/2023] Open
Abstract
Cerebrovascular Reactivity (CVR) is a provocative test used with Blood oxygenation level-dependent (BOLD) Magnetic Resonance Imaging (MRI) studies, where a vasoactive stimulus is applied and the corresponding changes in the cerebral blood flow (CBF) are measured. The most common clinical application is the assessment of cerebral perfusion insufficiency in patients with steno-occlusive disease (SOD). Globally, millions of people suffer from cerebrovascular diseases, and SOD is the most common cause of ischemic stroke. Therefore, CVR analyses can play a vital role in early diagnosis and guiding clinical treatment. This study develops a convolutional neural network (CNN)-based clinical decision support system to facilitate the screening of SOD patients by discriminating between healthy and unhealthy CVR maps. The networks were trained on a confidential CVR dataset with two classes: 68 healthy control subjects, and 163 SOD patients. This original dataset was distributed in a ratio of 80%-10%-10% for training, validation, and testing, respectively, and image augmentations were applied to the training and validation sets. Additionally, some popular pre-trained networks were imported and customized for the objective classification task to conduct transfer learning experiments. Results indicate that a customized CNN with a double-stacked convolution layer architecture produces the best results, consistent with expert clinical readings.
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Affiliation(s)
- Yashesh Dasari
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
| | - James Duffin
- Department of Physiology, University of Toronto, Toronto, ON M5S 1A8, Canada
- Department of Anesthesia and Pain Management, University Health Network, Toronto, ON M5G 2C4, Canada
| | - Ece Su Sayin
- Department of Physiology, University of Toronto, Toronto, ON M5S 1A8, Canada
- Department of Anesthesia and Pain Management, University Health Network, Toronto, ON M5G 2C4, Canada
| | - Harrison T. Levine
- Department of Physiology, University of Toronto, Toronto, ON M5S 1A8, Canada
- Department of Anesthesia and Pain Management, University Health Network, Toronto, ON M5G 2C4, Canada
| | - Julien Poublanc
- Joint Department of Medical Imaging and the Functional Neuroimaging Laboratory, University Health Network, Toronto, ON M5G 2C4, Canada
| | - Andrea E. Para
- Joint Department of Medical Imaging and the Functional Neuroimaging Laboratory, University Health Network, Toronto, ON M5G 2C4, Canada
| | - David J. Mikulis
- Joint Department of Medical Imaging and the Functional Neuroimaging Laboratory, University Health Network, Toronto, ON M5G 2C4, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Joseph A. Fisher
- Department of Physiology, University of Toronto, Toronto, ON M5S 1A8, Canada
- Department of Anesthesia and Pain Management, University Health Network, Toronto, ON M5G 2C4, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Olivia Sobczyk
- Department of Anesthesia and Pain Management, University Health Network, Toronto, ON M5G 2C4, Canada
- Joint Department of Medical Imaging and the Functional Neuroimaging Laboratory, University Health Network, Toronto, ON M5G 2C4, Canada
| | - Mir Behrad Khamesee
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
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Khan MM, Chowdhury MEH, Arefin ASMS, Podder KK, Hossain MSA, Alqahtani A, Murugappan M, Khandakar A, Mushtak A, Nahiduzzaman M. A Deep Learning-Based Automatic Segmentation and 3D Visualization Technique for Intracranial Hemorrhage Detection Using Computed Tomography Images. Diagnostics (Basel) 2023; 13:2537. [PMID: 37568900 PMCID: PMC10417300 DOI: 10.3390/diagnostics13152537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/20/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
Intracranial hemorrhage (ICH) occurs when blood leaks inside the skull as a result of trauma to the skull or due to medical conditions. ICH usually requires immediate medical and surgical attention because the disease has a high mortality rate, long-term disability potential, and other potentially life-threatening complications. There are a wide range of severity levels, sizes, and morphologies of ICHs, making accurate identification challenging. Hemorrhages that are small are more likely to be missed, particularly in healthcare systems that experience high turnover when it comes to computed tomography (CT) investigations. Although many neuroimaging modalities have been developed, CT remains the standard for diagnosing trauma and hemorrhage (including non-traumatic ones). A CT scan-based diagnosis can provide time-critical, urgent ICH surgery that could save lives because CT scan-based diagnoses can be obtained rapidly. The purpose of this study is to develop a machine-learning algorithm that can detect intracranial hemorrhage based on plain CT images taken from 75 patients. CT images were preprocessed using brain windowing, skull-stripping, and image inversion techniques. Hemorrhage segmentation was performed using multiple pre-trained models on preprocessed CT images. A U-Net model with DenseNet201 pre-trained encoder outperformed other U-Net, U-Net++, and FPN (Feature Pyramid Network) models with the highest Dice similarity coefficient (DSC) and intersection over union (IoU) scores, which were previously used in many other medical applications. We presented a three-dimensional brain model highlighting hemorrhages from ground truth and predicted masks. The volume of hemorrhage was measured volumetrically to determine the size of the hematoma. This study is essential in examining ICH for diagnostic purposes in clinical practice by comparing the predicted 3D model with the ground truth.
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Affiliation(s)
- Muntakim Mahmud Khan
- Department of Biomedical Physics and Technology, University of Dhaka, Dhaka 1000, Bangladesh
| | | | - A. S. M. Shamsul Arefin
- Department of Biomedical Physics and Technology, University of Dhaka, Dhaka 1000, Bangladesh
| | - Kanchon Kanti Podder
- Department of Biomedical Physics and Technology, University of Dhaka, Dhaka 1000, Bangladesh
| | - Md. Sakib Abrar Hossain
- Department of Biomedical Physics and Technology, University of Dhaka, Dhaka 1000, Bangladesh
| | - Abdulrahman Alqahtani
- Department of Medical Equipment Technology, College of Applied, Medical Science, Majmaah University, Majmaah City 11952, Saudi Arabia
- Department of Biomedical Technology, College of Applied Medical Sciences in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
| | - M. Murugappan
- Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, Doha 13133, Kuwait
- Department of Electronics and Communication Engineering, School of Engineering, Vels Institute of Sciences, Technology, and Advanced Studies, Chennai 600117, India
- Center of Excellence for Unmanned Aerial Systems (CoEUAS), Universiti Malaysia Perlis, Perlis 02600, Malaysia
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Adam Mushtak
- Clinical Imaging Department, Hamad Medical Corporation, Doha 3050, Qatar
| | - Md. Nahiduzzaman
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
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