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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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Vitt JR, Mainali S. Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients. Semin Neurol 2024; 44:342-356. [PMID: 38569520 DOI: 10.1055/s-0044-1785504] [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: 04/05/2024]
Abstract
The utilization of Artificial Intelligence (AI) and Machine Learning (ML) is paving the way for significant strides in patient diagnosis, treatment, and prognostication in neurocritical care. These technologies offer the potential to unravel complex patterns within vast datasets ranging from vast clinical data and EEG (electroencephalogram) readings to advanced cerebral imaging facilitating a more nuanced understanding of patient conditions. Despite their promise, the implementation of AI and ML faces substantial hurdles. Historical biases within training data, the challenge of interpreting multifaceted data streams, and the "black box" nature of ML algorithms present barriers to widespread clinical adoption. Moreover, ethical considerations around data privacy and the need for transparent, explainable models remain paramount to ensure trust and efficacy in clinical decision-making.This article reflects on the emergence of AI and ML as integral tools in neurocritical care, discussing their roles from the perspective of both their scientific promise and the associated challenges. We underscore the importance of extensive validation in diverse clinical settings to ensure the generalizability of ML models, particularly considering their potential to inform critical medical decisions such as withdrawal of life-sustaining therapies. Advancement in computational capabilities is essential for implementing ML in clinical settings, allowing for real-time analysis and decision support at the point of care. As AI and ML are poised to become commonplace in clinical practice, it is incumbent upon health care professionals to understand and oversee these technologies, ensuring they adhere to the highest safety standards and contribute to the realization of personalized medicine. This engagement will be pivotal in integrating AI and ML into patient care, optimizing outcomes in neurocritical care through informed and data-driven decision-making.
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Affiliation(s)
- Jeffrey R Vitt
- Department of Neurological Surgery, UC Davis Medical Center, Sacramento, California
| | - Shraddha Mainali
- Department of Neurology, Virginia Commonwealth University, Richmond, Virginia
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Kim JY, Choi HJ, Kim SH, Ju H. Improved differentiation of cavernous malformation and acute intraparenchymal hemorrhage on CT using an AI algorithm. Sci Rep 2024; 14:11818. [PMID: 38782974 PMCID: PMC11116413 DOI: 10.1038/s41598-024-61960-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024] Open
Abstract
This study aimed to evaluate the utility of an artificial intelligence (AI) algorithm in differentiating between cerebral cavernous malformation (CCM) and acute intraparenchymal hemorrhage (AIH) on brain computed tomography (CT). A retrospective, multireader, randomized study was conducted to validate the performance of an AI algorithm in differentiating AIH from CCM on brain CT. CT images of CM and AIH (< 3 cm) were identified from the database. Six blinded reviewers, including two neuroradiologists, two radiology residents, and two emergency department physicians, evaluated CT images from 288 patients (CCM, n = 173; AIH, n = 115) with and without AI assistance, comparing diagnostic performance. Brain CT interpretation with AI assistance resulted in significantly higher diagnostic accuracy than without (86.92% vs. 79.86%, p < 0.001). Radiology residents and emergency department physicians showed significantly improved accuracy of CT interpretation with AI assistance than without (84.21% vs. 75.35%, 80.73% vs. 72.57%; respectively, p < 0.05). Neuroradiologists showed a trend of higher accuracy with AI assistance in the interpretation but lacked statistical significance (95.83% vs. 91.67%, p = 0.56). The use of an AI algorithm can enhance the differentiation of AIH from CCM in brain CT interpretation, particularly for nonexperts in neuroradiology.
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Affiliation(s)
- Jung Youn Kim
- Department of Radiology, CHA Bundang Medical Center, CHA University, 59 Yatap-Ro, Bundang, Seongnam, Gyeonggi-Do, 13496, Republic of Korea
| | - Hye Jeong Choi
- Department of Radiology, CHA Bundang Medical Center, CHA University, 59 Yatap-Ro, Bundang, Seongnam, Gyeonggi-Do, 13496, Republic of Korea.
| | - Sang Heum Kim
- Department of Radiology, CHA Bundang Medical Center, CHA University, 59 Yatap-Ro, Bundang, Seongnam, Gyeonggi-Do, 13496, Republic of Korea
| | - Hwangseon Ju
- Department of Radiology, CHA Bundang Medical Center, CHA University, 59 Yatap-Ro, Bundang, Seongnam, Gyeonggi-Do, 13496, Republic of Korea
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He B, Xu Z, Zhou D, Zhang L. Deep multiscale convolutional feature learning for intracranial hemorrhage classification and weakly supervised localization. Heliyon 2024; 10:e30270. [PMID: 38720700 PMCID: PMC11076974 DOI: 10.1016/j.heliyon.2024.e30270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 04/16/2024] [Accepted: 04/23/2024] [Indexed: 05/12/2024] Open
Abstract
Objective This study evaluated the performance of attentional fusion model-based multiscale features in classifying intracerebral hemorrhage and the localization of bleeding focus based on weakly supervised target localization. Methods A publicly available dataset provided by the American College of Neuroradiology (ASNR) was used, consisting of 750,000 computed tomography (CT) scans of the brain, manually marked by radiologists for intracranial hemorrhage and five hemorrhage subtypes. A multiscale feature classification and weakly supervised localization framework based on an attentional fusion mechanism were applied, which could be annotated at the slice level and provided intracranial hemorrhage classification and hemorrhage focus localization. Results The designed framework achieved excellent performance for classification and localization. The area under the curve (AUC) for predicting bleeding was 0.973. High AUC values were observed for the five hemorrhage subtypes (epidural AUC = 0.891, subdural AUC = 0.991, subarachnoid AUC = 0.983, intraventricular AUC = 0.995, intraparenchymal AUC = 0.990). This model outperformed the average entry-level radiology trainee compared to previously reported data. Conclusion The designed method quickly and accurately detected intracerebral hemorrhage, classifying hemorrhage subtypes and locating bleeding points with image-level annotation alone. The results indicate that this framework can significantly reduce diagnostic time while improving the detection of intracerebral hemorrhage in emergencies. It can thus be integrated into the diagnostic radiology workflow in the future.
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Affiliation(s)
- Bishi He
- School of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Zhe Xu
- School of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Dong Zhou
- School of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Lei Zhang
- School of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
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Morita D, Kawarazaki A, Soufi M, Otake Y, Sato Y, Numajiri T. Automatic detection of midfacial fractures in facial bone CT images using deep learning-based object detection models. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2024:101914. [PMID: 38750725 DOI: 10.1016/j.jormas.2024.101914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 04/24/2024] [Accepted: 05/12/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND Midfacial fractures are among the most frequent facial fractures. Surgery is recommended within 2 weeks of injury, but this time frame is often extended because the fracture is missed on diagnostic imaging in the busy emergency medicine setting. Using deep learning technology, which has progressed markedly in various fields, we attempted to develop a system for the automatic detection of midfacial fractures. The purpose of this study was to use this system to diagnose fractures accurately and rapidly, with the intention of benefiting both patients and emergency room physicians. METHODS One hundred computed tomography images that included midfacial fractures (e.g., maxillary, zygomatic, nasal, and orbital fractures) were prepared. In each axial image, the fracture area was surrounded by a rectangular region to create the annotation data. Eighty images were randomly classified as the training dataset (3736 slices) and 20 as the validation dataset (883 slices). Training and validation were performed using Single Shot MultiBox Detector (SSD) and version 8 of You Only Look Once (YOLOv8), which are object detection algorithms. RESULTS The performance indicators for SSD and YOLOv8 were respectively: precision, 0.872 and 0.871; recall, 0.823 and 0.775; F1 score, 0.846 and 0.82; average precision, 0.899 and 0.769. CONCLUSIONS The use of deep learning techniques allowed the automatic detection of midfacial fractures with good accuracy and high speed. The system developed in this study is promising for automated detection of midfacial fractures and may provide a quick and accurate solution for emergency medical care and other settings.
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Affiliation(s)
- Daiki Morita
- Department of Plastic and Reconstructive Surgery, Kyoto Prefectural University of Medicine, Kyoto, Japan; Department of Plastic and Reconstructive Surgery, Tokai University School of Medicine, Kanagawa, Japan.
| | - Ayako Kawarazaki
- Department of Plastic and Reconstructive Surgery, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Mazen Soufi
- Division of Information Science, Nara Institute of Science and Technology, Nara, Japan
| | - Yoshito Otake
- Division of Information Science, Nara Institute of Science and Technology, Nara, Japan
| | - Yoshinobu Sato
- Division of Information Science, Nara Institute of Science and Technology, Nara, Japan
| | - Toshiaki Numajiri
- Department of Plastic and Reconstructive Surgery, Kyoto Prefectural University of Medicine, Kyoto, Japan
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6
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Cheng CT, Ooyang CH, Kang SC, Liao CH. Applications of Deep Learning in Trauma Radiology: A Narrative Review. Biomed J 2024:100743. [PMID: 38679199 DOI: 10.1016/j.bj.2024.100743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 03/26/2024] [Accepted: 04/24/2024] [Indexed: 05/01/2024] Open
Abstract
Diagnostic imaging is essential in modern trauma care for initial evaluation and identifying injuries requiring intervention. Deep learning (DL) has become mainstream in medical image analysis and has shown promising efficacy for classification, segmentation, and lesion detection. This narrative review provides the fundamental concepts for developing DL algorithms in trauma imaging and presents an overview of current progress in each modality. DL has been applied to detect free fluid on Focused Assessment with Sonography for Trauma (FAST), traumatic findings on chest and pelvic X-rays, and computed tomography (CT) scans, identify intracranial hemorrhage on head CT, detect vertebral fractures, and identify injuries to organs like the spleen, liver, and lungs on abdominal and chest CT. Future directions involve expanding dataset size and diversity through federated learning, enhancing model explainability and transparency to build clinician trust, and integrating multimodal data to provide more meaningful insights into traumatic injuries. Though some commercial artificial intelligence products are Food and Drug Administration-approved for clinical use in the trauma field, adoption remains limited, highlighting the need for multi-disciplinary teams to engineer practical, real-world solutions. Overall, DL shows immense potential to improve the efficiency and accuracy of trauma imaging, but thoughtful development and validation are critical to ensure these technologies positively impact patient care.
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Affiliation(s)
- Chi-Tung Cheng
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan Taiwan
| | - Chun-Hsiang Ooyang
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan Taiwan
| | - Shih-Ching Kang
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan Taiwan.
| | - Chien-Hung Liao
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan Taiwan
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Zhang Y, Joshi J, Hadi M. AI in Acute Cerebrovascular Disorders: What can the Radiologist Contribute? Semin Roentgenol 2024; 59:137-147. [PMID: 38880512 DOI: 10.1053/j.ro.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 01/13/2024] [Accepted: 01/27/2024] [Indexed: 06/18/2024]
Affiliation(s)
- Yi Zhang
- Department of Radiology, University of Louisville, 530 South Jackson Street, CCB-C07, Louisville, KY
| | - Jonathan Joshi
- Department of Radiology, University of Louisville, 530 South Jackson Street, CCB-C07, Louisville, KY
| | - Mohiuddin Hadi
- Department of Radiology, University of Louisville, 530 South Jackson Street, CCB-C07, Louisville, KY.
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8
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Tran AT, Zeevi T, Haider SP, Abou Karam G, Berson ER, Tharmaseelan H, Qureshi AI, Sanelli PC, Werring DJ, Malhotra A, Petersen NH, de Havenon A, Falcone GJ, Sheth KN, Payabvash S. Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan. NPJ Digit Med 2024; 7:26. [PMID: 38321131 PMCID: PMC10847454 DOI: 10.1038/s41746-024-01007-w] [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: 06/21/2023] [Accepted: 01/10/2024] [Indexed: 02/08/2024] Open
Abstract
Hematoma expansion (HE) is a modifiable risk factor and a potential treatment target in patients with intracerebral hemorrhage (ICH). We aimed to train and validate deep-learning models for high-confidence prediction of supratentorial ICH expansion, based on admission non-contrast head Computed Tomography (CT). Applying Monte Carlo dropout and entropy of deep-learning model predictions, we estimated the model uncertainty and identified patients at high risk of HE with high confidence. Using the receiver operating characteristics area under the curve (AUC), we compared the deep-learning model prediction performance with multivariable models based on visual markers of HE determined by expert reviewers. We randomly split a multicentric dataset of patients (4-to-1) into training/cross-validation (n = 634) versus test (n = 159) cohorts. We trained and tested separate models for prediction of ≥6 mL and ≥3 mL ICH expansion. The deep-learning models achieved an AUC = 0.81 for high-confidence prediction of HE≥6 mL and AUC = 0.80 for prediction of HE≥3 mL, which were higher than visual maker models AUC = 0.69 for HE≥6 mL (p = 0.036) and AUC = 0.68 for HE≥3 mL (p = 0.043). Our results show that fully automated deep-learning models can identify patients at risk of supratentorial ICH expansion based on admission non-contrast head CT, with high confidence, and more accurately than benchmark visual markers.
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Grants
- U24 NS107136 NINDS NIH HHS
- UL1 TR001863 NCATS NIH HHS
- K76 AG059992 NIA NIH HHS
- P30 AG021342 NIA NIH HHS
- R03 NS112859 NINDS NIH HHS
- U24 NS107215 NINDS NIH HHS
- U01 NS106513 NINDS NIH HHS
- 2020097 Doris Duke Charitable Foundation
- K23 NS118056 NINDS NIH HHS
- R01 NR018335 NINR NIH HHS
- Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- Doris Duke Charitable Foundation (DDCF)
- Doris Duke Charitable Foundation (2020097), American Society of Neuroradiology, and National Institutes of Health (K23NS118056).
- National Institutes of Health (K76AG059992, R03NS112859, and P30AG021342), the American Heart Association (18IDDG34280056), the Yale Pepper Scholar Award, and the Neurocritical Care Society Research Fellowship
- National Institutes of Health (U24NS107136, U24NS107215, R01NR018335, and U01NS106513) and the American Heart Association (18TPA34170180 and 17CSA33550004) and a Hyperfine Research Inc research grant.
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Affiliation(s)
- Anh T Tran
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Tal Zeevi
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Stefan P Haider
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Munich, Germany
| | - Gaby Abou Karam
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Elisa R Berson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Hishan Tharmaseelan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Adnan I Qureshi
- Stroke Institute and Department of Neurology, University of Missouri, Columbia, MO, USA
| | - Pina C Sanelli
- Department of Radiology, Northwell Health, Manhasset, NY, USA
| | - David J Werring
- Stroke Research Centre, University College London, Queen Square Institute of Neurology, London, UK
| | - Ajay Malhotra
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Nils H Petersen
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Adam de Havenon
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Guido J Falcone
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Kevin N Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
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Zhang F, Turhon M, Huang J, Li M, Liu J, Zhang Y, Zhang Y. Global trend in research of intracranial aneurysm management with artificial intelligence technology: a bibliometric analysis. Quant Imaging Med Surg 2024; 14:1022-1038. [PMID: 38223110 PMCID: PMC10784100 DOI: 10.21037/qims-23-793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 10/08/2023] [Indexed: 01/16/2024]
Abstract
Background The use of artificial intelligence (AI) technology has been growing in the management of intracranial aneurysms (IAs). This study aims to conduct a bibliometric analysis of researches on intracranial aneurysm management with artificial intelligence technology (IAMWAIT) to gain insights into global research trends and potential future directions. Methods A comprehensive search of articles and reviews related to IAMWAIT, published from January 1, 1900 to July 20, 2023, was conducted using the Web of Science Core Collection (WoWCC).Visualizations of the bibliometric analysis were generated utilizing WPS Office, Scimago Graphica, VOSviewer, CiteSpace, and R. Results A total of 277 papers were included in the study. China emerged as the most prolific country in terms of publications, institutions, cooperating countries, and prolific authors. The United States garnered the highest number of total citations, institutions with the highest citations/H index, cooperating countries (n=9), and 3 of the top 10 cited papers. Both the total number of papers and the citation count exhibited a positive and significant correlation with the gross domestic product (GDP) of countries. The journal with the highest publication frequency was Frontiers in Neurology, while Stroke recorded the highest number of citations, H-index, and impact factor (IF). Areas of primary interest in IAMWAIT, leveraging AI technology, included rupture risk assessment/prediction, computer-assisted diagnosis, outcome prediction, hemodynamics, and laboratory research of IAs. Conclusions IAMWAIT is an active area of research that has undergone rapid development in recent years. Future endeavors should focus on broader application of AI algorithms in various sub-fields of IAMWAIT to better suit the real world.
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Affiliation(s)
- Fujunhui Zhang
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Mirzat Turhon
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiliang Huang
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Mengxing Li
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jian Liu
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yisen Zhang
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ying Zhang
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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Vacek A, Mair G, White P, Bath PM, Muir KW, Al-Shahi Salman R, Martin C, Dye D, Chappell FM, von Kummer R, Macleod M, Sprigg N, Wardlaw JM. Evaluating artificial intelligence software for delineating hemorrhage extent on CT brain imaging in stroke: AI delineation of ICH on CT. J Stroke Cerebrovasc Dis 2024; 33:107512. [PMID: 38007987 DOI: 10.1016/j.jstrokecerebrovasdis.2023.107512] [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: 06/07/2023] [Revised: 10/25/2023] [Accepted: 11/21/2023] [Indexed: 11/28/2023] Open
Abstract
BACKGROUND The extent and distribution of intracranial hemorrhage (ICH) directly affects clinical management. Artificial intelligence (AI) software can detect and may delineate ICH extent on brain CT. We evaluated e-ASPECTS software (Brainomix Ltd.) performance for ICH delineation. METHODS We qualitatively assessed software delineation of ICH on CT using patients from six stroke trials. We assessed hemorrhage delineation in five compartments: lobar, deep, posterior fossa, intraventricular, extra-axial. We categorized delineation as excellent, good, moderate, or poor. We assessed quality of software delineation with number of affected compartments in univariate analysis (Kruskall-Wallis test) and ICH location using logistic regression (dependent variable: dichotomous delineation categories 'excellent-good' versus 'moderate-poor'), and report odds ratios (OR) and 95 % confidence intervals (95 %CI). RESULTS From 651 patients with ICH (median age 75 years, 53 % male), we included 628 with assessable CTs. Software delineation of ICH extent was 'excellent' in 189/628 (30 %), 'good' in 255/628 (41 %), 'moderate' in 127/628 (20 %), and 'poor' in 57/628 cases (9 %). The quality of software delineation of ICH was better when fewer compartments were affected (Z = 3.61-6.27; p = 0.0063). Software delineation of ICH extent was more likely to be 'excellent-good' quality when lobar alone (OR = 1.56, 95 %CI = 0.97-2.53) but 'moderate-poor' with any intraventricular (OR = 0.56, 95 %CI = 0.39-0.81, p = 0.002) or any extra-axial (OR = 0.41, 95 %CI = 0.27-0.62, p<0.001) extension. CONCLUSIONS Delineation of ICH extent on stroke CT scans by AI software was excellent or good in 71 % of cases but was more likely to over- or under-estimate extent when ICH was either more extensive, intraventricular, or extra-axial.
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Affiliation(s)
- Adam Vacek
- Centre for Clinical Brain Sciences & UK Dementia Research Institute Centre, University of Edinburgh, UK
| | - Grant Mair
- Centre for Clinical Brain Sciences & UK Dementia Research Institute Centre, University of Edinburgh, UK.
| | - Philip White
- Translational and Clinical Research Institute, Newcastle University, UK
| | - Philip M Bath
- Stroke Trials Unit, Mental Health & Clinical Neuroscience, University of Nottingham, UK
| | - Keith W Muir
- School of Psychology & Neuroscience, University of Glasgow, UK
| | - Rustam Al-Shahi Salman
- Centre for Clinical Brain Sciences & UK Dementia Research Institute Centre, University of Edinburgh, UK
| | - Chloe Martin
- Centre for Clinical Brain Sciences & UK Dementia Research Institute Centre, University of Edinburgh, UK
| | - David Dye
- Centre for Clinical Brain Sciences & UK Dementia Research Institute Centre, University of Edinburgh, UK
| | - Francesca M Chappell
- Centre for Clinical Brain Sciences & UK Dementia Research Institute Centre, University of Edinburgh, UK
| | - Rüdiger von Kummer
- Department of Neuroradiology, University Hospital, Technische Universität Dresden, Germany
| | - Malcolm Macleod
- Centre for Clinical Brain Sciences & UK Dementia Research Institute Centre, University of Edinburgh, UK
| | - Nikola Sprigg
- Stroke Trials Unit, Mental Health & Clinical Neuroscience, University of Nottingham, UK
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences & UK Dementia Research Institute Centre, University of Edinburgh, UK
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11
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Maghami M, Sattari SA, Tahmasbi M, Panahi P, Mozafari J, Shirbandi K. Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study. Biomed Eng Online 2023; 22:114. [PMID: 38049809 PMCID: PMC10694901 DOI: 10.1186/s12938-023-01172-1] [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: 07/20/2023] [Accepted: 11/17/2023] [Indexed: 12/06/2023] Open
Abstract
BACKGROUND This systematic review and meta-analysis were conducted to objectively evaluate the evidence of machine learning (ML) in the patient diagnosis of Intracranial Hemorrhage (ICH) on computed tomography (CT) scans. METHODS Until May 2023, systematic searches were conducted in ISI Web of Science, PubMed, Scopus, Cochrane Library, IEEE Xplore Digital Library, CINAHL, Science Direct, PROSPERO, and EMBASE for studies that evaluated the diagnostic precision of ML model-assisted ICH detection. Patients with and without ICH as the target condition who were receiving CT-Scan were eligible for the research, which used ML algorithms based on radiologists' reports as the gold reference standard. For meta-analysis, pooled sensitivities, specificities, and a summary receiver operating characteristics curve (SROC) were used. RESULTS At last, after screening the title, abstract, and full paper, twenty-six retrospective and three prospective, and two retrospective/prospective studies were included. The overall (Diagnostic Test Accuracy) DTA of retrospective studies with a pooled sensitivity was 0.917 (95% CI 0.88-0.943, I2 = 99%). The pooled specificity was 0.945 (95% CI 0.918-0.964, I2 = 100%). The pooled diagnostic odds ratio (DOR) was 219.47 (95% CI 104.78-459.66, I2 = 100%). These results were significant for the specificity of the different network architecture models (p-value = 0.0289). However, the results for sensitivity (p-value = 0.6417) and DOR (p-value = 0.2187) were not significant. The ResNet algorithm has higher pooled specificity than other algorithms with 0.935 (95% CI 0.854-0.973, I2 = 93%). CONCLUSION This meta-analysis on DTA of ML algorithms for detecting ICH by assessing non-contrast CT-Scans shows the ML has an acceptable performance in diagnosing ICH. Using ResNet in ICH detection remains promising prediction was improved via training in an Architecture Learning Network (ALN).
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Affiliation(s)
- Masoud Maghami
- Medical Doctor (MD), School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Shahab Aldin Sattari
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Marziyeh Tahmasbi
- Department of Medical Imaging and Radiation Sciences, School of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Pegah Panahi
- Medical Doctor (MD), School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Javad Mozafari
- Department of Emergency Medicine, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
- Department of Radiology, Resident (MD), EUREGIO-KLINIK Albert-Schweitzer-Straße GmbH, Nordhorn, Germany
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12
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Yedavalli V, Heit JJ, Dehkharghani S, Haerian H, Mcmenamy J, Honce J, Timpone VM, Harnain C, Kesselman A, Filly A, Beardsley A, Sakamoto B, Song C, Montuori J, Navot B, Mena FV, Giurgiutiu DV, Kitamura F, Lima FO, Silva H, Mont’Alverne FJ, Albers G. Performance of RAPID noncontrast CT stroke platform in large vessel occlusion and intracranial hemorrhage detection. Front Neurol 2023; 14:1324088. [PMID: 38156093 PMCID: PMC10753184 DOI: 10.3389/fneur.2023.1324088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 11/08/2023] [Indexed: 12/30/2023] Open
Abstract
Background Noncontrast CT (NCCT) is used to evaluate for intracerebral hemorrhage (ICH) and ischemia in acute ischemic stroke (AIS). Large vessel occlusions (LVOs) are a major cause of AIS, but challenging to detect on NCCT. Aims The purpose of this study is to evaluate an AI software called RAPID NCCT Stroke (RAPID, iSchemaView, Menlo Park, CA) for ICH and LVO detection compared to expert readers. Methods In this IRB approved retrospective, multicenter study, stand-alone performance of the software was assessed based on the consensus of 3 neuroradiologists and sensitivity and specificity were determined. The platform's performance was then compared to interpretation by readers comprised of eight general radiologists (GR) and three neuroradiologists (NR) in detecting ICH and hyperdense vessel sign (HVS) indicating LVO. Results A total of 244 cases were included. Of the 244, 115 were LVOs and 26 were ICHs. One hundred three cases did not have LVO nor ICH. Stand-alone performance of the software demonstrated sensitivities and specificities of 96.2 and 99.5% for ICH and 63.5 and 95.1% for LVO detection. Compared to all 11 readers and eight GR readers only respectively, the software demonstrated superiority, achieving significantly higher sensitivities (63.5% versus 43.6%, p < 0.0001 and 63.5% versus 40.9%, p = 0.001). Conclusion The RAPID NCCT Stroke platform demonstrates superior performance to radiologists for detecting LVO from a NCCT. Use of this software platform could lead to earlier LVO detection and expedited transfer of these patients to a thrombectomy capable center.
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Affiliation(s)
- Vivek Yedavalli
- The Johns Hopkins Hospital, Johns Hopkins Medicine, Baltimore, MD, United States
| | | | - Seena Dehkharghani
- Department of Radiology, New York University, New York, NY, United States
| | | | - John Mcmenamy
- Department of Radiology, New York University, New York, NY, United States
| | - Justin Honce
- Department of Radiology, University of Colorado, Denver, CO, United States
| | | | | | - Andrew Kesselman
- Department of Radiology, Stanford University, Standford, CA, United States
| | | | - Adam Beardsley
- Department of Radiology, University of Virginia Hospital, Charlottesville, VA, United States
| | | | - Chris Song
- Weill Cornell Medicine, Cornell University, New York, NY, United States
| | | | - Benjamin Navot
- Columbia College, Columbia University, New York, NY, United States
| | | | | | - Felipe Kitamura
- Department of Radiology, Universidade Federal de São Paulo, Dasa, Brazil
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13
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Abdollahifard S, Farrokhi A, Mowla A. Response to 'Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis'. J Neurointerv Surg 2023; 15:1057-1058. [PMID: 37714539 DOI: 10.1136/jnis-2023-020804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 07/15/2023] [Indexed: 09/17/2023]
Affiliation(s)
- Saeed Abdollahifard
- School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
- Research Center for Neuromodulation and Pain, Shiraz, Iran
| | - Amirmohammad Farrokhi
- School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
- Research Center for Neuromodulation and Pain, Shiraz, Iran
| | - Ashkan Mowla
- Division of Stroke and Endovascular Neurosurgery, Department of Neurological Surgery, Keck School of Medicine University of Southern California (USC), Los Angeles, California, USA
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14
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Abdollahifard S, Farrokhi A, Mowla A. Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis. J Neurointerv Surg 2023; 15:995-1000. [PMID: 36418163 DOI: 10.1136/jnis-2022-019627] [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: 09/13/2022] [Accepted: 11/09/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND This study aimed to investigate the application of deep learning (DL) models for the detection of subdural hematoma (SDH). METHODS We conducted a comprehensive search using relevant keywords. Articles extracted were original studies in which sensitivity and/or specificity were reported. Two different approaches of frequentist and Bayesian inference were applied. For quality and risk of bias assessment we used Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). RESULTS We analyzed 22 articles that included 1,997,749 patients. In the first step, the frequentist method showed a pooled sensitivity of 88.8% (95% confidence interval (CI): 83.9% to 92.4%) and a specificity of 97.2% (95% CI 94.6% to 98.6%). In the second step, using Bayesian methods including 11 studies that reported sensitivity and specificity, a sensitivity rate of 86.8% (95% CI: 77.6% to 92.9%) at a specificity level of 86.9% (95% CI: 60.9% to 97.2%) was achieved. The risk of bias assessment was not remarkable using QUADAS-2. CONCLUSION DL models might be an appropriate tool for detecting SDHs with a reasonably high sensitivity and specificity.
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Affiliation(s)
- Saeed Abdollahifard
- Medical School, Shiraz University of Medical Sciences, Shiraz, Iran
- Center for Neuromodulation and Pain, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Amirmohammad Farrokhi
- Medical School, Shiraz University of Medical Sciences, Shiraz, Iran
- Center for Neuromodulation and Pain, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Ashkan Mowla
- Neurological Surgery, University of Southern California, Los Angeles, California, USA
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15
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Agarwal S, Wood DA, Modat M, Booth TC. Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis. J Neurointerv Surg 2023; 15:1056-1057. [PMID: 37258226 DOI: 10.1136/jnis-2023-020218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 04/17/2023] [Indexed: 06/02/2023]
Affiliation(s)
- Siddharth Agarwal
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - David A Wood
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Thomas C Booth
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, King's College Hospital NHS Foundation Trust, London, UK
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16
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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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17
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Kuwabara M, Ikawa F, Sakamoto S, Okazaki T, Ishii D, Hosogai M, Maeda Y, Chiku M, Kitamura N, Choppin A, Takamiya D, Shimahara Y, Nakayama T, Kurisu K, Horie N. Effectiveness of tuning an artificial intelligence algorithm for cerebral aneurysm diagnosis: a study of 10,000 consecutive cases. Sci Rep 2023; 13:16202. [PMID: 37758849 PMCID: PMC10533861 DOI: 10.1038/s41598-023-43418-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 09/23/2023] [Indexed: 09/29/2023] Open
Abstract
Diagnostic image analysis for unruptured cerebral aneurysms using artificial intelligence has a very high sensitivity. However, further improvement is needed because of a relatively high number of false positives. This study aimed to confirm the clinical utility of tuning an artificial intelligence algorithm for cerebral aneurysm diagnosis. We extracted 10,000 magnetic resonance imaging scans of participants who underwent brain screening using the "Brain Dock" system. The sensitivity and false positives/case for aneurysm detection were compared before and after tuning the algorithm. The initial diagnosis included only cases for which feedback to the algorithm was provided. In the primary analysis, the sensitivity of aneurysm diagnosis decreased from 96.5 to 90% and the false positives/case improved from 2.06 to 0.99 after tuning the algorithm (P < 0.001). In the secondary analysis, the sensitivity of aneurysm diagnosis decreased from 98.8 to 94.6% and the false positives/case improved from 1.99 to 1.03 after tuning the algorithm (P < 0.001). The false positives/case reduced without a significant decrease in sensitivity. Using large clinical datasets, we demonstrated that by tuning the algorithm, we could significantly reduce false positives with a minimal decline in sensitivity.
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Affiliation(s)
- Masashi Kuwabara
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Fusao Ikawa
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan.
- Department of Neurosurgery, Shimane Prefectural Central Hospital, 4-1-1 Himebara, Izumo, Shimane, 693-8555, Japan.
| | - Shigeyuki Sakamoto
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Takahito Okazaki
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Daizo Ishii
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Masahiro Hosogai
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Yuyo Maeda
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Masaaki Chiku
- Department of Neurosurgery, Medical Check Studio, Tokyo Ginza Clinic, 1-2-4 Ginza, Chuo-ku, Tokyo, 104-0061, Japan
| | - Naoyuki Kitamura
- Department of Diagnostic Radiology, Kasumi Clinic, 1-2-27 Shinonomehommachi, Minami-ku, Hiroshima, Hiroshima, 734-0023, Japan
| | - Antoine Choppin
- LPIXEL Inc., 1-6-1 Otemachi, Chiyoda-ku, Tokyo, 100-0004, Japan
| | | | - Yuki Shimahara
- LPIXEL Inc., 1-6-1 Otemachi, Chiyoda-ku, Tokyo, 100-0004, Japan
| | - Takeo Nakayama
- Department of Health Informatics, School of Public Health, Graduate School of Medicine, Kyoto University, Yoshida-Konoe, Sakyo-ku, Kyoto, Kyoto, 606-8501, Japan
| | - Kaoru Kurisu
- Chugoku Rosai Hospital, 1-5-1 Hirotagaya, Kure, Hiroshima, 737-0193, Japan
| | - Nobutaka Horie
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan
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18
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Umapathy S, Murugappan M, Bharathi D, Thakur M. Automated Computer-Aided Detection and Classification of Intracranial Hemorrhage Using Ensemble Deep Learning Techniques. Diagnostics (Basel) 2023; 13:2987. [PMID: 37761354 PMCID: PMC10527774 DOI: 10.3390/diagnostics13182987] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 09/03/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023] Open
Abstract
Diagnosing Intracranial Hemorrhage (ICH) at an early stage is difficult since it affects the blood vessels in the brain, often resulting in death. We propose an ensemble of Convolutional Neural Networks (CNNs) combining Squeeze and Excitation-based Residual Networks with the next dimension (SE-ResNeXT) and Long Short-Term Memory (LSTM) Networks in order to address this issue. This research work primarily used data from the Radiological Society of North America (RSNA) brain CT hemorrhage challenge dataset and the CQ500 dataset. Preprocessing and data augmentation are performed using the windowing technique in the proposed work. The ICH is then classified using ensembled CNN techniques after being preprocessed, followed by feature extraction in an automatic manner. ICH is classified into the following five types: epidural, intraventricular, subarachnoid, intra-parenchymal, and subdural. A gradient-weighted Class Activation Mapping method (Grad-CAM) is used for identifying the region of interest in an ICH image. A number of performance measures are used to compare the experimental results with various state-of-the-art algorithms. By achieving 99.79% accuracy with an F-score of 0.97, the proposed model proved its efficacy in detecting ICH compared to other deep learning models. The proposed ensembled model can classify epidural, intraventricular, subarachnoid, intra-parenchymal, and subdural hemorrhages with an accuracy of 99.89%, 99.65%, 98%, 99.75%, and 99.88%. Simulation results indicate that the suggested approach can categorize a variety of intracranial bleeding types. By implementing the ensemble deep learning technique using the SE-ResNeXT and LSTM models, we achieved significant classification accuracy and AUC scores.
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Affiliation(s)
- Snekhalatha Umapathy
- Department of Biomedical Engineering, SRM Institute of Science and Technology, Chennai 603203, India
- College of Engineering, Architecture, and Fine Arts, Batangas State University, Batangas 4200, Philippines
| | - Murugappan 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, Arau 02600, Perlis, Malaysia
| | - Deepa Bharathi
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai 600089, India
| | - Mahima Thakur
- Department of Biomedical Engineering, SRM Institute of Science and Technology, Chennai 603203, India
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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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20
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Kaya İ, Gençtürk TH, Gülağız FK. A revolutionary acute subdural hematoma detection based on two-tiered artificial intelligence model. ULUS TRAVMA ACIL CER 2023; 29:858-871. [PMID: 37563894 PMCID: PMC10560802 DOI: 10.14744/tjtes.2023.76756] [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: 10/03/2022] [Revised: 03/22/2023] [Accepted: 04/23/2023] [Indexed: 08/12/2023]
Abstract
BACKGROUND The article was planned to make the first evaluation in terms of acute subdural hemorrhages, thinking that it can help in appropriate pathologies by tomography interpretation with the artificial intelligence (AI) method, at least in a way to quickly warn the responsible doctor. METHODS A two-level AI-based hybrid method was developed. The proposed model uses the mask-region convolutional neural network (Mask R-CNN) technique, which is a deep learning model, in the hemorrhagic region's mask generation stage, and a problem-specific, optimized support vector machines (SVM) technique which is a machine learning model in the binary classification stage. Furthermore, the bee colony algorithm was used for the optimization of SVM algorithms' parameters. RESULTS In the first stage, the mean average precision (mAP) value was obtained as 0.754 when the intercept over union (IOU) value was taken as 0.5 with the Mask R-CNN architecture used. At the same time, when a 5-fold cross-validation was applied, the mAP value was obtained 0.736. With the hyperparameter optimization for both Mask R-CNN and the SVM algorithm, the accuracy of the two-level classification process was obtained as 96.36%. Furthermore, final false-negative rate and false-positive rate values were obtained as 6.20%, and 2.57%, respectively. CONCLUSION With the proposed model, both the detection of hemorrhage and the presentation of the suspicious area to the physician were performed more successfully on two dimensional (2D) images with low cost and high accuracy compared to similar studies and today's interpretations with telemedicine techniques.
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Affiliation(s)
- İsmail Kaya
- Department of Neurosurgery, Niğde Ömer Halisdemir University, Faculty of Medicine, Niğde-Türkiye
| | - Tuğrul Hakan Gençtürk
- Department of Computer Engineering, Kocaeli University, Faculty of Engineering, Kocaeli-Türkiye
| | - Fidan Kaya Gülağız
- Department of Computer Engineering, Kocaeli University, Faculty of Engineering, Kocaeli-Türkiye
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21
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Leming MJ, Bron EE, Bruffaerts R, Ou Y, Iglesias JE, Gollub RL, Im H. Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting. NPJ Digit Med 2023; 6:129. [PMID: 37443276 DOI: 10.1038/s41746-023-00868-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
Advances in artificial intelligence have cultivated a strong interest in developing and validating the clinical utilities of computer-aided diagnostic models. Machine learning for diagnostic neuroimaging has often been applied to detect psychological and neurological disorders, typically on small-scale datasets or data collected in a research setting. With the collection and collation of an ever-growing number of public datasets that researchers can freely access, much work has been done in adapting machine learning models to classify these neuroimages by diseases such as Alzheimer's, ADHD, autism, bipolar disorder, and so on. These studies often come with the promise of being implemented clinically, but despite intense interest in this topic in the laboratory, limited progress has been made in clinical implementation. In this review, we analyze challenges specific to the clinical implementation of diagnostic AI models for neuroimaging data, looking at the differences between laboratory and clinical settings, the inherent limitations of diagnostic AI, and the different incentives and skill sets between research institutions, technology companies, and hospitals. These complexities need to be recognized in the translation of diagnostic AI for neuroimaging from the laboratory to the clinic.
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Affiliation(s)
- Matthew J Leming
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA.
- Massachusetts Alzheimer's Disease Research Center, Charlestown, MA, USA.
| | - Esther E Bron
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Rose Bruffaerts
- Computational Neurology, Experimental Neurobiology Unit (ENU), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
| | - Yangming Ou
- Boston Children's Hospital, 300 Longwood Ave, Boston, MA, USA
| | - Juan Eugenio Iglesias
- Center for Medical Image Computing, University College London, London, UK
- Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Randy L Gollub
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hyungsoon Im
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA.
- Massachusetts Alzheimer's Disease Research Center, Charlestown, MA, USA.
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
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22
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Sengupta J, Alzbutas R, Falkowski-Gilski P, Falkowska-Gilska B. Intracranial hemorrhage detection in 3D computed tomography images using a bi-directional long short-term memory network-based modified genetic algorithm. Front Neurosci 2023; 17:1200630. [PMID: 37469843 PMCID: PMC10352619 DOI: 10.3389/fnins.2023.1200630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 06/12/2023] [Indexed: 07/21/2023] Open
Abstract
Introduction Intracranial hemorrhage detection in 3D Computed Tomography (CT) brain images has gained more attention in the research community. The major issue to deal with the 3D CT brain images is scarce and hard to obtain the labelled data with better recognition results. Methods To overcome the aforementioned problem, a new model has been implemented in this research manuscript. After acquiring the images from the Radiological Society of North America (RSNA) 2019 database, the region of interest (RoI) was segmented by employing Otsu's thresholding method. Then, feature extraction was performed utilizing Tamura features: directionality, contrast, coarseness, and Gradient Local Ternary Pattern (GLTP) descriptors to extract vectors from the segmented RoI regions. The extracted vectors were dimensionally reduced by proposing a modified genetic algorithm, where the infinite feature selection technique was incorporated with the conventional genetic algorithm to further reduce the redundancy within the regularized vectors. The selected optimal vectors were finally fed to the Bi-directional Long Short Term Memory (Bi-LSTM) network to classify intracranial hemorrhage sub-types, such as subdural, intraparenchymal, subarachnoid, epidural, and intraventricular. Results The experimental investigation demonstrated that the Bi-LSTM based modified genetic algorithm obtained 99.40% sensitivity, 99.80% accuracy, and 99.48% specificity, which are higher compared to the existing machine learning models: Naïve Bayes, Random Forest, Support Vector Machine (SVM), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) network.
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23
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Ebong U, Büttner SM, Schmidt SA, Flack F, Korf P, Peters L, Grüner B, Stenger S, Stamminger T, Kestler H, Beer M, Kloth C. Quantitative Evaluation of COVID-19 Pneumonia CT Using AI Analysis-Feasibility and Differentiation from Other Common Pneumonia Forms. Diagnostics (Basel) 2023; 13:2129. [PMID: 37371024 DOI: 10.3390/diagnostics13122129] [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: 03/25/2023] [Revised: 05/14/2023] [Accepted: 05/23/2023] [Indexed: 06/29/2023] Open
Abstract
PURPOSE: To implement the technical feasibility of an AI-based software prototype optimized for the detection of COVID-19 pneumonia in CT datasets of the lung and the differentiation between other etiologies of pneumonia. METHODS: This single-center retrospective case-control-study consecutively yielded 144 patients (58 female, mean age 57.72 ± 18.25 y) with CT datasets of the lung. Subgroups including confirmed bacterial (n = 24, 16.6%), viral (n = 52, 36.1%), or fungal (n = 25, 16.6%) pneumonia and (n = 43, 30.7%) patients without detected pneumonia (comparison group) were evaluated using the AI-based Pneumonia Analysis prototype. Scoring (extent, etiology) was compared to reader assessment. RESULTS: The software achieved an optimal sensitivity of 80.8% with a specificity of 50% for the detection of COVID-19; however, the human radiologist achieved optimal sensitivity of 80.8% and a specificity of 97.2%. The mean postprocessing time was 7.61 ± 4.22 min. The use of a contrast agent did not influence the results of the software (p = 0.81). The mean evaluated COVID-19 probability is 0.80 ± 0.36 significantly higher in COVID-19 patients than in patients with fungal pneumonia (p < 0.05) and bacterial pneumonia (p < 0.001). The mean percentage of opacity (PO) and percentage of high opacity (PHO ≥ -200 HU) were significantly higher in COVID-19 patients than in healthy patients. However, the total mean HU in COVID-19 patients was -679.57 ± 112.72, which is significantly higher than in the healthy control group (p < 0.001). CONCLUSION: The detection and quantification of pneumonia beyond the primarily trained COVID-19 datasets is possible and shows comparable results for COVID-19 pneumonia to an experienced reader. The advantages are the fast, automated segmentation and quantification of the pneumonia foci.
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Affiliation(s)
- Una Ebong
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Susanne Martina Büttner
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Stefan A Schmidt
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Franziska Flack
- Scientific Collaborations Siemens Healthcare GmbH Erlangen, 91052 Erlangen, Germany
| | - Patrick Korf
- Scientific Collaborations Siemens Healthcare GmbH Erlangen, 91052 Erlangen, Germany
| | - Lynn Peters
- Division of Infectious Diseases, University Hospital and Medical Centre of Ulm, 89081 Ulm, Germany
| | - Beate Grüner
- Division of Infectious Diseases, University Hospital and Medical Centre of Ulm, 89081 Ulm, Germany
| | - Steffen Stenger
- Institute of Medical Microbiology and Hygiene, Ulm University Medical Center, 89081 Ulm, Germany
| | - Thomas Stamminger
- Institute of Virology, Ulm University Medical Center, 89081 Ulm, Germany
| | - Hans Kestler
- Institute for Medical Systems Biology, Ulm University, 89081 Ulm, Germany
| | - Meinrad Beer
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Christopher Kloth
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany
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Angkurawaranon S, Sanorsieng N, Unsrisong K, Inkeaw P, Sripan P, Khumrin P, Angkurawaranon C, Vaniyapong T, Chitapanarux I. A comparison of performance between a deep learning model with residents for localization and classification of intracranial hemorrhage. Sci Rep 2023; 13:9975. [PMID: 37340038 DOI: 10.1038/s41598-023-37114-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 06/15/2023] [Indexed: 06/22/2023] Open
Abstract
Intracranial hemorrhage (ICH) from traumatic brain injury (TBI) requires prompt radiological investigation and recognition by physicians. Computed tomography (CT) scanning is the investigation of choice for TBI and has become increasingly utilized under the shortage of trained radiology personnel. It is anticipated that deep learning models will be a promising solution for the generation of timely and accurate radiology reports. Our study examines the diagnostic performance of a deep learning model and compares the performance of that with detection, localization and classification of traumatic ICHs involving radiology, emergency medicine, and neurosurgery residents. Our results demonstrate that the high level of accuracy achieved by the deep learning model, (0.89), outperforms the residents with regard to sensitivity (0.82) but still lacks behind in specificity (0.90). Overall, our study suggests that the deep learning model may serve as a potential screening tool aiding the interpretation of head CT scans among traumatic brain injury patients.
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Affiliation(s)
- Salita Angkurawaranon
- Department of Radiology, Maharaj Nakorn Chiang Mai Hospital, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand
- Global Health and Chronic Conditions Research Group, Chiang Mai, 50200, Thailand
| | - Nonn Sanorsieng
- Department of Radiology, Maharaj Nakorn Chiang Mai Hospital, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Kittisak Unsrisong
- Department of Radiology, Maharaj Nakorn Chiang Mai Hospital, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Papangkorn Inkeaw
- Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Patumrat Sripan
- Research Institute for Health Sciences, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Piyapong Khumrin
- Department of Family Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Chaisiri Angkurawaranon
- Global Health and Chronic Conditions Research Group, Chiang Mai, 50200, Thailand
- Department of Family Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Tanat Vaniyapong
- Neurosurgery Division, Department of Surgery, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Imjai Chitapanarux
- Department of Radiology, Maharaj Nakorn Chiang Mai Hospital, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.
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Gunturkun F, Bakir-Batu B, Siddiqui A, Lakin K, Hoehn ME, Vestal R, Davis RL, Shafi NI. Development of a Deep Learning Model for Retinal Hemorrhage Detection on Head Computed Tomography in Young Children. JAMA Netw Open 2023; 6:e2319420. [PMID: 37347482 PMCID: PMC10288337 DOI: 10.1001/jamanetworkopen.2023.19420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 05/05/2023] [Indexed: 06/23/2023] Open
Abstract
Importance Abusive head trauma (AHT) in children is often missed in medical encounters, and retinal hemorrhage (RH) is considered strong evidence for AHT. Although head computed tomography (CT) is obtained routinely, all but exceptionally large RHs are undetectable on CT images in children. Objective To examine whether deep learning-based image analysis can detect RH on pediatric head CT. Design, Setting, and Participants This diagnostic study included 301 patients diagnosed with AHT who underwent head CT and dilated fundoscopic examinations at a quaternary care children's hospital. The study assessed a deep learning model using axial slices from 218 segmented globes with RH and 384 globes without RH between May 1, 2007, and March 31, 2021. Two additional light gradient boosting machine (GBM) models were assessed: one that used demographic characteristics and common brain findings in AHT and another that combined the deep learning model's risk prediction plus the same demographic characteristics and brain findings. Main Outcomes and Measures Sensitivity (recall), specificity, precision, accuracy, F1 score, and area under the curve (AUC) for each model predicting the presence or absence of RH in globes were assessed. Globe regions that influenced the deep learning model predictions were visualized in saliency maps. The contributions of demographic and standard CT features were assessed by Shapley additive explanation. Results The final study population included 301 patients (187 [62.1%] male; median [range] age, 4.6 [0.1-35.8] months). A total of 120 patients (39.9%) had RH on fundoscopic examinations. The deep learning model performed as follows: sensitivity, 79.6%; specificity, 79.2%; positive predictive value (precision), 68.6%; negative predictive value, 87.1%; accuracy, 79.3%; F1 score, 73.7%; and AUC, 0.83 (95% CI, 0.75-0.91). The AUCs were 0.80 (95% CI, 0.69-0.91) for the general light GBM model and 0.86 (95% CI, 0.79-0.93) for the combined light GBM model. Sensitivities of all models were similar, whereas the specificities of the deep learning and combined light GBM models were higher than those of the light GBM model. Conclusions and Relevance The findings of this diagnostic study indicate that a deep learning-based image analysis of globes on pediatric head CTs can predict the presence of RH. After prospective external validation, a deep learning model incorporated into CT image analysis software could calibrate clinical suspicion for AHT and provide decision support for which patients urgently need fundoscopic examinations.
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Affiliation(s)
- Fatma Gunturkun
- Quantitative Sciences Unit, Department of Medicine, Stanford University, Palo Alto, California
| | - Berna Bakir-Batu
- Center for Biomedical Informatics, University of Tennessee Health Science Center, Memphis
| | - Adeel Siddiqui
- Department of Radiology, University of Tennessee Health Sciences Center, Memphis
| | - Karen Lakin
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Mary E. Hoehn
- Department of Ophthalmology, University of Tennessee Health Sciences Center, Memphis
| | - Robert Vestal
- Department of Ophthalmology, University of Tennessee Health Sciences Center, Memphis
| | - Robert L. Davis
- Center for Biomedical Informatics, University of Tennessee Health Science Center, Memphis
| | - Nadeem I. Shafi
- Department of Pediatrics, University of Tennessee Health Sciences Center, Memphis
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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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Neves G, Warman PI, Warman A, Warman R, Bueso T, Vadhan JD, Windisch T. External Validation of an Artificial Intelligence Device for Intracranial Hemorrhage Detection. World Neurosurg 2023; 173:e800-e807. [PMID: 36906085 DOI: 10.1016/j.wneu.2023.03.019] [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] [Received: 11/18/2022] [Revised: 03/03/2023] [Accepted: 03/04/2023] [Indexed: 03/12/2023]
Abstract
BACKGROUND Artificial intelligence applications have gained traction in the field of cerebrovascular disease by assisting in the triage, classification, and prognostication of both ischemic and hemorrhagic stroke. The Caire ICH system aims to be the first device to move into the realm of assisted diagnosis for intracranial hemorrhage (ICH) and its subtypes. METHODS A single-center retrospective dataset of 402 head noncontrast CT scans (NCCT) with an intracranial hemorrhage were retrospectively collected from January 2012 to July 2020; an additional 108 NCCT scans with no intracranial hemorrhage findings were also included. The presence of an ICH and its subtype were determined from the International Classification of Diseases-10 code associated with the scan and validated by an expert panel. We used the Caire ICH vR1 to analyze these scans, and we evaluated its performance in terms of accuracy, sensitivity, and specificity. RESULTS We found the Caire ICH system to have an accuracy of 98.05% (95% confidence interval [CI]: 96.44%-99.06%), a sensitivity of 97.52% (95% CI: 95.50%-98.81%), and a specificity of 100% (95% CI: 96.67%-100.00%) in the detection of ICH. Experts reviewed the 10 incorrectly classified scans. CONCLUSIONS The Caire ICH vR1 algorithm was highly accurate, sensitive, and specific in detecting the presence or absence of an ICH and its subtypes in NCCTs. This work suggests that the Caire ICH device has potential to minimize clinical errors in ICH diagnosis that could improve patient outcomes and current workflows as both a point-of-care tool for diagnostics and as a safety net for radiologists.
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Affiliation(s)
- Gabriel Neves
- Department of Neurology, Texas Tech University Medical Sciences Center, Lubbock, Texas, USA.
| | | | | | | | - Tulio Bueso
- Department of Neurology, Texas Tech University Medical Sciences Center, Lubbock, Texas, USA
| | - Jason D Vadhan
- Department of Emergency Medicine, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Thomas Windisch
- Department of Neurology, Texas Tech University Medical Sciences Center, Lubbock, Texas, USA; Covenant Health, Lubbock, Texas, USA
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Yeo M, Tahayori B, Kok HK, Maingard J, Kutaiba N, Russell J, Thijs V, Jhamb A, Chandra RV, Brooks M, Barras CD, Asadi H. Evaluation of techniques to improve a deep learning algorithm for the automatic detection of intracranial haemorrhage on CT head imaging. Eur Radiol Exp 2023; 7:17. [PMID: 37032417 PMCID: PMC10083149 DOI: 10.1186/s41747-023-00330-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 02/07/2023] [Indexed: 04/11/2023] Open
Abstract
BACKGROUND Deep learning (DL) algorithms are playing an increasing role in automatic medical image analysis. PURPOSE To evaluate the performance of a DL model for the automatic detection of intracranial haemorrhage and its subtypes on non-contrast CT (NCCT) head studies and to compare the effects of various preprocessing and model design implementations. METHODS The DL algorithm was trained and externally validated on open-source, multi-centre retrospective data containing radiologist-annotated NCCT head studies. The training dataset was sourced from four research institutions across Canada, the USA and Brazil. The test dataset was sourced from a research centre in India. A convolutional neural network (CNN) was used, with its performance compared against similar models with additional implementations: (1) a recurrent neural network (RNN) attached to the CNN, (2) preprocessed CT image-windowed inputs and (3) preprocessed CT image-concatenated inputs. The area under the receiver operating characteristic curve (AUC-ROC) and microaveraged precision (mAP) score were used to evaluate and compare model performances. RESULTS The training and test datasets contained 21,744 and 491 NCCT head studies, respectively, with 8,882 (40.8%) and 205 (41.8%) positive for intracranial haemorrhage. Implementation of preprocessing techniques and the CNN-RNN framework increased mAP from 0.77 to 0.93 and increased AUC-ROC [95% confidence intervals] from 0.854 [0.816-0.889] to 0.966 [0.951-0.980] (p-value = 3.91 × 10-12). CONCLUSIONS The deep learning model accurately detected intracranial haemorrhage and improved in performance following specific implementation techniques, demonstrating clinical potential as a decision support tool and an automated system to improve radiologist workflow efficiency. KEY POINTS • The deep learning model detected intracranial haemorrhages on computed tomography with high accuracy. • Image preprocessing, such as windowing, plays a large role in improving deep learning model performance. • Implementations which enable an analysis of interslice dependencies can improve deep learning model performance. • Visual saliency maps can facilitate explainable artificial intelligence systems. • Deep learning within a triage system may expedite earlier intracranial haemorrhage detection.
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Affiliation(s)
- Melissa Yeo
- Melbourne Medical School, The University of Melbourne, Melbourne, VIC, Australia.
| | - Bahman Tahayori
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
- IBM Research Australia, Melbourne, VIC, Australia
| | - Hong Kuan Kok
- Interventional Radiology Service, Department of Radiology, Northern Health, Epping, VIC, Australia
- School of Medicine, Faculty of Health, Deakin University, Burwood, VIC, Australia
| | - Julian Maingard
- School of Medicine, Faculty of Health, Deakin University, Burwood, VIC, Australia
- Interventional Neuroradiology Unit, Monash Health, Clayton, VIC, Australia
- Faculty of Medicine Nursing and Health Sciences, Monash University, Clayton, VIC, Australia
- Department of Radiology, St Vincent's Hospital, Melbourne, VIC, Australia
| | - Numan Kutaiba
- Department of Radiology, Austin Hospital, Melbourne, VIC, Australia
| | - Jeremy Russell
- Department of Neurosurgery, Austin Hospital, Melbourne, VIC, Australia
| | - Vincent Thijs
- Stroke Theme, Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
- Department of Neurology, Austin Health, Melbourne, VIC, Australia
| | - Ashu Jhamb
- Department of Radiology, St Vincent's Hospital, Melbourne, VIC, Australia
| | - Ronil V Chandra
- Interventional Neuroradiology Unit, Monash Health, Clayton, VIC, Australia
- Faculty of Medicine Nursing and Health Sciences, Monash University, Clayton, VIC, Australia
| | - Mark Brooks
- Melbourne Medical School, The University of Melbourne, Melbourne, VIC, Australia
- School of Medicine, Faculty of Health, Deakin University, Burwood, VIC, Australia
- Stroke Theme, Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
- Interventional Neuroradiology Service, Department of Radiology, Austin Hospital, Melbourne, VIC, Australia
| | - Christen D Barras
- South Australian Institute of Health and Medical Research, Adelaide, South Australia, Australia
- School of Medicine, The University of Adelaide, Adelaide, South Australia, Australia
| | - Hamed Asadi
- Melbourne Medical School, The University of Melbourne, Melbourne, VIC, Australia
- School of Medicine, Faculty of Health, Deakin University, Burwood, VIC, Australia
- Interventional Neuroradiology Unit, Monash Health, Clayton, VIC, Australia
- Department of Radiology, St Vincent's Hospital, Melbourne, VIC, Australia
- Stroke Theme, Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
- Interventional Neuroradiology Service, Department of Radiology, Austin Hospital, Melbourne, VIC, Australia
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Yun TJ, Choi JW, Han M, Jung WS, Choi SH, Yoo RE, Hwang IP. Deep learning based automatic detection algorithm for acute intracranial haemorrhage: a pivotal randomized clinical trial. NPJ Digit Med 2023; 6:61. [PMID: 37029272 PMCID: PMC10082037 DOI: 10.1038/s41746-023-00798-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 03/10/2023] [Indexed: 04/09/2023] Open
Abstract
Acute intracranial haemorrhage (AIH) is a potentially life-threatening emergency that requires prompt and accurate assessment and management. This study aims to develop and validate an artificial intelligence (AI) algorithm for diagnosing AIH using brain-computed tomography (CT) images. A retrospective, multi-reader, pivotal, crossover, randomised study was performed to validate the performance of an AI algorithm was trained using 104,666 slices from 3010 patients. Brain CT images (12,663 slices from 296 patients) were evaluated by nine reviewers belonging to one of the three subgroups (non-radiologist physicians, n = 3; board-certified radiologists, n = 3; and neuroradiologists, n = 3) with and without the aid of our AI algorithm. Sensitivity, specificity, and accuracy were compared between AI-unassisted and AI-assisted interpretations using the chi-square test. Brain CT interpretation with AI assistance results in significantly higher diagnostic accuracy than that without AI assistance (0.9703 vs. 0.9471, p < 0.0001, patient-wise). Among the three subgroups of reviewers, non-radiologist physicians demonstrate the greatest improvement in diagnostic accuracy for brain CT interpretation with AI assistance compared to that without AI assistance. For board-certified radiologists, the diagnostic accuracy for brain CT interpretation is significantly higher with AI assistance than without AI assistance. For neuroradiologists, although brain CT interpretation with AI assistance results in a trend for higher diagnostic accuracy compared to that without AI assistance, the difference does not reach statistical significance. For the detection of AIH, brain CT interpretation with AI assistance results in better diagnostic performance than that without AI assistance, with the most significant improvement observed for non-radiologist physicians.
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Affiliation(s)
- Tae Jin Yun
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jin Wook Choi
- Department of Radiology, Ajou University School of Medicine, Suwon, Republic of Korea.
| | - Miran Han
- Department of Radiology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Woo Sang Jung
- Department of Radiology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Seung Hong Choi
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Roh-Eul Yoo
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - In Pyeong Hwang
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
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Pérez Del Barrio A, Esteve Domínguez AS, Menéndez Fernández-Miranda P, Sanz Bellón P, Rodríguez González D, Lloret Iglesias L, Marqués Fraguela E, González Mandly AA, Vega JA. A deep learning model for prognosis prediction after intracranial hemorrhage. J Neuroimaging 2023; 33:218-226. [PMID: 36585957 DOI: 10.1111/jon.13078] [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: 10/03/2022] [Revised: 12/13/2022] [Accepted: 12/20/2022] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND AND PURPOSE Intracranial hemorrhage (ICH) is a common life-threatening condition that must be rapidly diagnosed and treated. However, there is still a lack of consensus regarding treatment, driven to some extent by prognostic uncertainty. While several prediction models for ICH detection have already been published, here we present a deep learning predictive model for ICH prognosis. METHODS We included patients with ICH (n = 262), and we trained a custom model for the classification of patients into poor prognosis and good prognosis, using a hybrid input consisting of brain CT images and other clinical variables. We compared it with two other models, one trained with images only (I-model) and the other with tabular data only (D-model). RESULTS Our hybrid model achieved an area under the receiver operating characteristic curve (AUC) of .924 (95% confidence interval [CI]: .831-.986), and an accuracy of .861 (95% CI: .760-.960). The I- and D-models achieved an AUC of .763 (95% CI: .622-.902) and .746 (95% CI: .598-.876), respectively. CONCLUSIONS The proposed hybrid model was able to accurately classify patients into good and poor prognosis. To the best of our knowledge, this is the first ICH prognosis prediction deep learning model. We concluded that deep learning can be applied for prognosis prediction in ICH that could have a great impact on clinical decision-making. Further, hybrid inputs could be a promising technique for deep learning in medical imaging.
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Affiliation(s)
- Amaia Pérez Del Barrio
- Servicio de Radiodiagnóstico, Hospital Universitario "Marqués de Valdecilla", Santander, Spain
| | - Anna Salut Esteve Domínguez
- Advanced Computation and e-Science, Instituto de Física de Cantabria (IFCA), Consejo Superior de Investigaciones Científicas (CSIC), Santander, Spain
| | | | - Pablo Sanz Bellón
- Servicio de Radiodiagnóstico, Hospital Universitario "Marqués de Valdecilla", Santander, Spain
| | - David Rodríguez González
- Advanced Computation and e-Science, Instituto de Física de Cantabria (IFCA), Consejo Superior de Investigaciones Científicas (CSIC), Santander, Spain
| | - Lara Lloret Iglesias
- Advanced Computation and e-Science, Instituto de Física de Cantabria (IFCA), Consejo Superior de Investigaciones Científicas (CSIC), Santander, Spain
| | | | | | - José A Vega
- Departamento de Morfología y Biología Celular, Universidad de Oviedo, Oviedo, Spain.,Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Santiago de Chile, Chile
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Maxillofacial fracture detection and classification in computed tomography images using convolutional neural network-based models. Sci Rep 2023; 13:3434. [PMID: 36859660 PMCID: PMC9978019 DOI: 10.1038/s41598-023-30640-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 02/27/2023] [Indexed: 03/03/2023] Open
Abstract
The purpose of this study was to evaluate the performance of convolutional neural network-based models for the detection and classification of maxillofacial fractures in computed tomography (CT) maxillofacial bone window images. A total of 3407 CT images, 2407 of which contained maxillofacial fractures, were retrospectively obtained from the regional trauma center from 2016 to 2020. Multiclass image classification models were created by using DenseNet-169 and ResNet-152. Multiclass object detection models were created by using faster R-CNN and YOLOv5. DenseNet-169 and ResNet-152 were trained to classify maxillofacial fractures into frontal, midface, mandibular and no fracture classes. Faster R-CNN and YOLOv5 were trained to automate the placement of bounding boxes to specifically detect fracture lines in each fracture class. The performance of each model was evaluated on an independent test dataset. The overall accuracy of the best multiclass classification model, DenseNet-169, was 0.70. The mean average precision of the best multiclass detection model, faster R-CNN, was 0.78. In conclusion, DenseNet-169 and faster R-CNN have potential for the detection and classification of maxillofacial fractures in CT images.
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Evaluation of grouped capsule network for intracranial hemorrhage segmentation in CT scans. Sci Rep 2023; 13:3440. [PMID: 36859709 PMCID: PMC9977894 DOI: 10.1038/s41598-023-30581-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 02/27/2023] [Indexed: 03/03/2023] Open
Abstract
Intracranial hemorrhage is a cerebral vascular disease with high mortality. Automotive diagnosing and segmentation of intracranial hemorrhage in Computed Tomography (CT) could assist the neurosurgeon in making treatment plans, which improves the survival rate. In this paper, we design a grouped capsule network named GroupCapsNet to segment the hemorrhage region from a Non-contract CT scan. In grouped capsule network, we constrain the prediction capsules for output capsules produced from different groups of input capsules with various types in each layer. This method can reduce the number of intermediate prediction capsules and accelerate the capsule network. In addition, we modify the squashing function to further accelerate the forward procedure without sacrificing its performance. We evaluate our proposed method with a collected dataset containing 210 intracranial hemorrhage CT scan slices. In experiments, our proposed method achieves competitive results in intracranial hemorrhage area segmentation compared to the existing methods.
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Wang C, Yu J, Zhong J, Han S, Qi Y, Fang B, Li X. Prior knowledge-based precise diagnosis of blend sign from head computed tomography. Front Neurosci 2023; 17:1112355. [PMID: 36845414 PMCID: PMC9950259 DOI: 10.3389/fnins.2023.1112355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 01/17/2023] [Indexed: 02/12/2023] Open
Abstract
Introduction Automated diagnosis of intracranial hemorrhage on head computed tomography (CT) plays a decisive role in clinical management. This paper presents a prior knowledge-based precise diagnosis of blend sign network from head CT scans. Method We employ the object detection task as an auxiliary task in addition to the classification task, which could incorporate the hemorrhage location as prior knowledge into the detection framework. The auxiliary task could help the model pay more attention to the regions with hemorrhage, which is beneficial for distinguishing the blend sign. Furthermore, we propose a self-knowledge distillation strategy to deal with inaccuracy annotations. Results In the experiment, we retrospectively collected 1749 anonymous non-contrast head CT scans from the First Affiliated Hospital of China Medical University. The dataset contains three categories: no intracranial hemorrhage (non-ICH), normal intracranial hemorrhage (normal ICH), and blend sign. The experimental results demonstrate that our method performs better than other methods. Discussion Our method has the potential to assist less-experienced head CT interpreters, reduce radiologists' workload, and improve efficiency in natural clinical settings.
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Affiliation(s)
- Chen Wang
- College of Computer Science, Chongqing University, Chongqing, China
| | - Jiefu Yu
- Department of Neurosurgery, The First Hospital of China Medical University, Shenyang, China
| | - Jiang Zhong
- College of Computer Science, Chongqing University, Chongqing, China,*Correspondence: Jiang Zhong ✉
| | - Shuai Han
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, China,Shuai Han ✉
| | - Yafei Qi
- College of Computer Science and Engineering, Central South University, Changsha, China
| | - Bin Fang
- College of Computer Science, Chongqing University, Chongqing, China
| | - Xue Li
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia
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Intracranial Hemorrhage Detection Using Parallel Deep Convolutional Models and Boosting Mechanism. Diagnostics (Basel) 2023; 13:diagnostics13040652. [PMID: 36832137 PMCID: PMC9955715 DOI: 10.3390/diagnostics13040652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/31/2023] [Accepted: 02/02/2023] [Indexed: 02/12/2023] Open
Abstract
Intracranial hemorrhage (ICH) can lead to death or disability, which requires immediate action from radiologists. Due to the heavy workload, less experienced staff, and the complexity of subtle hemorrhages, a more intelligent and automated system is necessary to detect ICH. In literature, many artificial-intelligence-based methods are proposed. However, they are less accurate for ICH detection and subtype classification. Therefore, in this paper, we present a new methodology to improve the detection and subtype classification of ICH based on two parallel paths and a boosting technique. The first path employs the architecture of ResNet101-V2 to extract potential features from windowed slices, whereas Inception-V4 captures significant spatial information in the second path. Afterwards, the detection and subtype classification of ICH is performed by the light gradient boosting machine (LGBM) using the outputs of ResNet101-V2 and Inception-V4. Thus, the combined solution, known as ResNet101-V2, Inception-V4, and LGBM (Res-Inc-LGBM), is trained and tested over the brain computed tomography (CT) scans of CQ500 and Radiological Society of North America (RSNA) datasets. The experimental results state that the proposed solution efficiently obtains 97.7% accuracy, 96.5% sensitivity, and 97.4% F1 score using the RSNA dataset. Moreover, the proposed Res-Inc-LGBM outperforms the standard benchmarks for the detection and subtype classification of ICH regarding the accuracy, sensitivity, and F1 score. The results prove the significance of the proposed solution for its real-time application.
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Kothala LP, Jonnala P, Guntur SR. Localization of mixed intracranial hemorrhages by using a ghost convolution-based YOLO network. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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36
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Detection of Critical Spinal Epidural Lesions on CT Using Machine Learning. Spine (Phila Pa 1976) 2023; 48:1-7. [PMID: 35905328 DOI: 10.1097/brs.0000000000004438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 07/12/2022] [Indexed: 02/01/2023]
Abstract
BACKGROUND Critical spinal epidural pathologies can cause paralysis or death if untreated. Although magnetic resonance imaging is the preferred modality for visualizing these pathologies, computed tomography (CT) occurs far more commonly than magnetic resonance imaging in the clinical setting. OBJECTIVE A machine learning model was developed to screen for critical epidural lesions on CT images at a large-scale teleradiology practice. This model has utility for both worklist prioritization of emergent studies and identifying missed findings. MATERIALS AND METHODS There were 153 studies with epidural lesions available for training. These lesions were segmented and used to train a machine learning model. A test data set was also created using previously missed epidural lesions. The trained model was then integrated into a teleradiology workflow for 90 days. Studies were sent to secondary manual review if the model detected an epidural lesion but none was mentioned in the clinical report. RESULTS The model correctly identified 50.0% of epidural lesions in the test data set with 99.0% specificity. For prospective data, the model correctly prioritized 66.7% of the 18 epidural lesions diagnosed on the initial read with 98.9% specificity. There were 2.0 studies flagged for potential missed findings per day, and 17 missed epidural lesions were found during a 90-day time period. These results suggest almost half of critical spinal epidural lesions visible on CT imaging are being missed on initial diagnosis. CONCLUSION A machine learning model for identifying spinal epidural hematomas and abscesses on CT can be implemented in a clinical workflow.
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Intracerebral Hemorrhage Segmentation on Noncontrast Computed Tomography Using a Masked Loss Function U-Net Approach. J Comput Assist Tomogr 2023; 47:93-101. [PMID: 36219722 DOI: 10.1097/rct.0000000000001380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
OBJECTIVE Intracerebral hemorrhage (ICH) volume is a strong predictor of outcome in patients presenting with acute hemorrhagic stroke. It is necessary to segment the hematoma for ICH volume estimation and for computerized extraction of features, such as spot sign, texture parameters, or extravasated iodine content at dual-energy computed tomography. Manual and semiautomatic segmentation methods to delineate the hematoma are tedious, user dependent, and require trained personnel. This article presents a convolutional neural network to automatically delineate ICH from noncontrast computed tomography scans of the head. METHODS A model combining a U-Net architecture with a masked loss function was trained on standard noncontrast computed tomography images that were down sampled to 256 × 256 size. Data augmentation was applied to prevent overfitting, and the loss score was calculated using the soft Dice loss function. The Dice coefficient and the Hausdorff distance were computed to quantitatively evaluate the segmentation performance of the model, together with the sensitivity and specificity to determine the ICH detection accuracy. RESULTS The results demonstrate a median Dice coefficient of 75.9% and Hausdorff distance of 2.65 pixels in segmentation performance, with a detection sensitivity of 77.0% and specificity of 96.2%. CONCLUSIONS The proposed masked loss U-Net is accurate in the automatic segmentation of ICH. Future research should focus on increasing the detection sensitivity of the model and comparing its performance with other model architectures.
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Smorchkova AK, Khoruzhaya AN, Kremneva EI, Petryaikin AV. [Machine learning technologies in CT-based diagnostics and classification of intracranial hemorrhages]. ZHURNAL VOPROSY NEIROKHIRURGII IMENI N. N. BURDENKO 2023; 87:85-91. [PMID: 37011333 DOI: 10.17116/neiro20238702185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
Abstract
This review discusses pooled experience of creation, implementation and effectiveness of machine learning technologies in CT-based diagnosis of intracranial hemorrhages. The authors analyzed 21 original articles between 2015 and 2022 using the following keywords: «intracranial hemorrhage», «machine learning», «deep learning», «artificial intelligence». The review contains general data on basic concepts of machine learning and also considers in more detail such aspects as technical characteristics of data sets used for creation of AI algorithms for certain type of clinical task, their possible impact on effectiveness and clinical experience.
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Affiliation(s)
- A K Smorchkova
- Moscow Research Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow, Russia
| | - A N Khoruzhaya
- Moscow Research Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow, Russia
| | - E I Kremneva
- Moscow Research Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow, Russia
- Neurology Research Center, Moscow, Russia
| | - A V Petryaikin
- Moscow Research Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow, Russia
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Bhatele KR, Jha A, Tiwari D, Bhatele M, Sharma S, Mithora MR, Singhal S. COVID-19 Detection: A Systematic Review of Machine and Deep Learning-Based Approaches Utilizing Chest X-Rays and CT Scans. Cognit Comput 2022:1-38. [PMID: 36593991 PMCID: PMC9797382 DOI: 10.1007/s12559-022-10076-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 11/15/2022] [Indexed: 12/30/2022]
Abstract
This review study presents the state-of-the-art machine and deep learning-based COVID-19 detection approaches utilizing the chest X-rays or computed tomography (CT) scans. This study aims to systematically scrutinize as well as to discourse challenges and limitations of the existing state-of-the-art research published in this domain from March 2020 to August 2021. This study also presents a comparative analysis of the performance of four majorly used deep transfer learning (DTL) models like VGG16, VGG19, ResNet50, and DenseNet over the COVID-19 local CT scans dataset and global chest X-ray dataset. A brief illustration of the majorly used chest X-ray and CT scan datasets of COVID-19 patients utilized in state-of-the-art COVID-19 detection approaches are also presented for future research. The research databases like IEEE Xplore, PubMed, and Web of Science are searched exhaustively for carrying out this survey. For the comparison analysis, four deep transfer learning models like VGG16, VGG19, ResNet50, and DenseNet are initially fine-tuned and trained using the augmented local CT scans and global chest X-ray dataset in order to observe their performance. This review study summarizes major findings like AI technique employed, type of classification performed, used datasets, results in terms of accuracy, specificity, sensitivity, F1 score, etc., along with the limitations, and future work for COVID-19 detection in tabular manner for conciseness. The performance analysis of the four majorly used deep transfer learning models affirms that Visual Geometry Group 19 (VGG19) model delivered the best performance over both COVID-19 local CT scans dataset and global chest X-ray dataset.
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Affiliation(s)
| | - Anand Jha
- RJIT BSF Academy, Tekanpur, Gwalior India
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40
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A Dense-Layered Deep Neural Model-Based Classification of Brain Hemorrhages Using Head Computer Tomography Images. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10090-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Hibi A, Jaberipour M, Cusimano MD, Bilbily A, Krishnan RG, Aviv RI, Tyrrell PN. Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet? Medicine (Baltimore) 2022; 101:e31848. [PMID: 36451512 PMCID: PMC9704869 DOI: 10.1097/md.0000000000031848] [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] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND The purpose of this study was to conduct a systematic review for understanding the availability and limitations of artificial intelligence (AI) approaches that could automatically identify and quantify computed tomography (CT) findings in traumatic brain injury (TBI). METHODS Systematic review, in accordance with PRISMA 2020 and SPIRIT-AI extension guidelines, with a search of 4 databases (Medline, Embase, IEEE Xplore, and Web of Science) was performed to find AI studies that automated the clinical tasks for identifying and quantifying CT findings of TBI-related abnormalities. RESULTS A total of 531 unique publications were reviewed, which resulted in 66 articles that met our inclusion criteria. The following components for identification and quantification regarding TBI were covered and automated by existing AI studies: identification of TBI-related abnormalities; classification of intracranial hemorrhage types; slice-, pixel-, and voxel-level localization of hemorrhage; measurement of midline shift; and measurement of hematoma volume. Automated identification of obliterated basal cisterns was not investigated in the existing AI studies. Most of the AI algorithms were based on deep neural networks that were trained on 2- or 3-dimensional CT imaging datasets. CONCLUSION We identified several important TBI-related CT findings that can be automatically identified and quantified with AI. A combination of these techniques may provide useful tools to enhance reproducibility of TBI identification and quantification by supporting radiologists and clinicians in their TBI assessments and reducing subjective human factors.
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Affiliation(s)
- Atsuhiro Hibi
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Majid Jaberipour
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - Michael D Cusimano
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Division of Neurosurgery, St Michael's Hospital, University of Toronto, Toronto, Canada
| | - Alexander Bilbily
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
- Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Rahul G Krishnan
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Richard I Aviv
- Department of Radiology, Radiation Oncology and Medical Physics, University of Ottawa, Ottawa, Ontario, Canada
| | - Pascal N Tyrrell
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
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Ho N, Kim YC. Estimation of Cardiac Short Axis Slice Levels with a Cascaded Deep Convolutional and Recurrent Neural Network Model. Tomography 2022; 8:2749-2760. [PMID: 36412688 PMCID: PMC9680453 DOI: 10.3390/tomography8060229] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/04/2022] [Accepted: 11/07/2022] [Indexed: 11/16/2022] Open
Abstract
Automatic identification of short axis slice levels in cardiac magnetic resonance imaging (MRI) is important in efficient and precise diagnosis of cardiac disease based on the geometry of the left ventricle. We developed a combined model of convolutional neural network (CNN) and recurrent neural network (RNN) that takes a series of short axis slices as input and predicts a series of slice levels as output. Each slice image was labeled as one of the following five classes: out-of-apical, apical, mid, basal, and out-of-basal levels. A variety of multi-class classification models were evaluated. When compared with the CNN-alone models, the cascaded CNN-RNN models resulted in higher mean F1-score and accuracy. In our implementation and testing of four different baseline networks with different combinations of RNN modules, MobileNet as the feature extractor cascaded with a two-layer long short-term memory (LSTM) network produced the highest scores in four of the seven evaluation metrics, i.e., five F1-scores, area under the curve (AUC), and accuracy. Our study indicates that the cascaded CNN-RNN models are superior to the CNN-alone models for the classification of short axis slice levels in cardiac cine MR images.
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Affiliation(s)
- Namgyu Ho
- Kim Jaechul Graduate School of Artificial Intelligence, KAIST, Seoul 02455, Republic of Korea
- Department of Computer Science and Engineering, Sogang University, Seoul 04107, Republic of Korea
| | - Yoon-Chul Kim
- Division of Digital Healthcare, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju 26493, Republic of Korea
- Correspondence:
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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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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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Altenbernd JC, Fischer S, Scharbrodt W, Schimrigk S, Eyding J, Nordmeyer H, Wohlert C, Dörner N, Li Y, Wrede K, Pierscianek D, Köhrmann M, Frank B, Forsting M, Deuschl C. CT and DSA for evaluation of spontaneous intracerebral lobar bleedings. Front Neurol 2022; 13:956888. [PMID: 36262835 PMCID: PMC9574012 DOI: 10.3389/fneur.2022.956888] [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] [Received: 05/30/2022] [Accepted: 08/12/2022] [Indexed: 11/18/2022] Open
Abstract
Purpose This study retrospectively examined the extent to which computed tomography angiography (CTA) and digital subtraction angiography (DSA) can help identify the cause of lobar intracerebral bleeding. Materials and methods In the period from 2002 to 2020, data from patients who were >18 years at a university and an academic teaching hospital with lobar intracerebral bleeding were evaluated retrospectively. The CTA DSA data were reviewed separately by two neuroradiologists, and differences in opinion were resolved by consensus after discussion. A positive finding was defined as an underlying vascular etiology of lobar bleeding. Results The data of 412 patients were retrospectively investigated. DSA detected a macrovascular cause of bleeding in 125/412 patients (33%). In total, sixty patients had AVMs (15%), 30 patients with aneurysms (7%), 12 patients with vasculitis (3%), and 23 patients with dural fistulas (6%). The sensitivity, specificity, positive and negative predictive values, and accuracy of CTA compared with DSA were 93, 97, 100, and 97%. There were false-negative CTA readings for two AVMs and one dural fistula. Conclusion The DSA is still the gold standard diagnostic modality for detecting macrovascular causes of ICH; however, most patients with lobar ICH can be investigated first with CTA, and the cause of bleeding can be found. Our results showed higher sensitivity and specificity than those of other CTA studies.
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Affiliation(s)
- Jens-Christian Altenbernd
- Department of Radiology, Gemeinschaftskrankenhaus, Herdecke, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- *Correspondence: Jens-Christian Altenbernd
| | | | | | | | - Jens Eyding
- Department of Neurology, Gemeinschaftskrankenhaus, Herdecke, Germany
| | | | - Christine Wohlert
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Nils Dörner
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Yan Li
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Karsten Wrede
- Department of Neurosurgery, University Hospital Essen, Essen, Germany
| | | | - Martin Köhrmann
- Department of Neurology, University Hospital Essen, Essen, Germany
| | - Benedikt Frank
- Department of Neurology, University Hospital Essen, Essen, Germany
| | - Michael Forsting
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Cornelius Deuschl
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
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Punitha S, Al-Turjman F, Stephan T. A novel e-healthcare diagnosing system for COVID-19 via whale optimization algorithm. J EXP THEOR ARTIF IN 2022. [DOI: 10.1080/0952813x.2022.2125079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- S. Punitha
- Department of Computer Science and Engineering, Graphics Era Deemed to be University, Dehradun, India
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Department of AI and Robotics Institute, Near East University, Nicosia, Turkey
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Kyrenia, Turkey
| | - Thompson Stephan
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, M. S. Ramaiah University of Applied Sciences, Bangalore, India
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Huang C, Wang J, Wang SH, Zhang YD. Applicable artificial intelligence for brain disease: A survey. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Matsoukas S, Scaggiante J, Schuldt BR, Smith CJ, Chennareddy S, Kalagara R, Majidi S, Bederson JB, Fifi JT, Mocco J, Kellner CP. Accuracy of artificial intelligence for the detection of intracranial hemorrhage and chronic cerebral microbleeds: a systematic review and pooled analysis. LA RADIOLOGIA MEDICA 2022; 127:1106-1123. [PMID: 35962888 DOI: 10.1007/s11547-022-01530-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 07/12/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND Artificial intelligence (AI)-driven software has been developed and become commercially available within the past few years for the detection of intracranial hemorrhage (ICH) and chronic cerebral microbleeds (CMBs). However, there is currently no systematic review that summarizes all of these tools or provides pooled estimates of their performance. METHODS In this PROSPERO-registered, PRISMA compliant systematic review, we sought to compile and review all MEDLINE and EMBASE published studies that have developed and/or tested AI algorithms for ICH detection on non-contrast CT scans (NCCTs) or MRI scans and CMBs detection on MRI scans. RESULTS In total, 40 studies described AI algorithms for ICH detection in NCCTs/MRIs and 19 for CMBs detection in MRIs. The overall sensitivity, specificity, and accuracy were 92.06%, 93.54%, and 93.46%, respectively, for ICH detection and 91.6%, 93.9%, and 92.7% for CMBs detection. Some of the challenges encountered in the development of these algorithms include the laborious work of creating large, labeled and balanced datasets, the volumetric nature of the imaging examinations, the fine tuning of the algorithms, and the reduction in false positives. CONCLUSIONS Numerous AI-driven software tools have been developed over the last decade. On average, they are characterized by high performance and expert-level accuracy for the diagnosis of ICH and CMBs. As a result, implementing these tools in clinical practice may improve workflow and act as a failsafe for the detection of such lesions. REGISTRATION-URL: https://www.crd.york.ac.uk/prospero/ Unique Identifier: CRD42021246848.
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Affiliation(s)
- Stavros Matsoukas
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA.
| | - Jacopo Scaggiante
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Braxton R Schuldt
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Colton J Smith
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Susmita Chennareddy
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Roshini Kalagara
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Shahram Majidi
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Joshua B Bederson
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Johanna T Fifi
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - J Mocco
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Christopher P Kellner
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
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Chen X, Lei Y, Su J, Yang H, Ni W, Yu J, Gu Y, Mao Y. A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges. Curr Neuropharmacol 2022; 20:1359-1382. [PMID: 34749621 PMCID: PMC9881077 DOI: 10.2174/1570159x19666211108141446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/07/2021] [Accepted: 10/10/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND A variety of emerging medical imaging technologies based on artificial intelligence have been widely applied in many diseases, but they are still limitedly used in the cerebrovascular field even though the diseases can lead to catastrophic consequences. OBJECTIVE This work aims to discuss the current challenges and future directions of artificial intelligence technology in cerebrovascular diseases through reviewing the existing literature related to applications in terms of computer-aided detection, prediction and treatment of cerebrovascular diseases. METHODS Based on artificial intelligence applications in four representative cerebrovascular diseases including intracranial aneurysm, arteriovenous malformation, arteriosclerosis and moyamoya disease, this paper systematically reviews studies published between 2006 and 2021 in five databases: National Center for Biotechnology Information, Elsevier Science Direct, IEEE Xplore Digital Library, Web of Science and Springer Link. And three refinement steps were further conducted after identifying relevant literature from these databases. RESULTS For the popular research topic, most of the included publications involved computer-aided detection and prediction of aneurysms, while studies about arteriovenous malformation, arteriosclerosis and moyamoya disease showed an upward trend in recent years. Both conventional machine learning and deep learning algorithms were utilized in these publications, but machine learning techniques accounted for a larger proportion. CONCLUSION Algorithms related to artificial intelligence, especially deep learning, are promising tools for medical imaging analysis and will enhance the performance of computer-aided detection, prediction and treatment of cerebrovascular diseases.
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Affiliation(s)
- Xi Chen
- School of Information Science and Technology, Fudan University, Shanghai, China; ,These authors contributed equally to this work
| | - Yu Lei
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China,These authors contributed equally to this work
| | - Jiabin Su
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Heng Yang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Wei Ni
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Jinhua Yu
- School of Information Science and Technology, Fudan University, Shanghai, China; ,Address correspondence to these authors at the School of Information Science and Technology, Fudan University, Shanghai 200433, China; Tel: +86 021 65643202; Fax: +86 021 65643202; E-mail: Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai 200040, China; Tel: +86 021 52889999; Fax: +86 021 62489191; E-mail:
| | - Yuxiang Gu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China,Address correspondence to these authors at the School of Information Science and Technology, Fudan University, Shanghai 200433, China; Tel: +86 021 65643202; Fax: +86 021 65643202; E-mail: Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai 200040, China; Tel: +86 021 52889999; Fax: +86 021 62489191; E-mail:
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
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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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