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Daga K, Agarwal S, Moti Z, Lee MBK, Din M, Wood D, Modat M, Booth TC. Machine Learning Algorithms to Predict the Risk of Rupture of Intracranial Aneurysms: a Systematic Review. Clin Neuroradiol 2025; 35:3-16. [PMID: 39546007 PMCID: PMC11832721 DOI: 10.1007/s00062-024-01474-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2024] [Accepted: 10/17/2024] [Indexed: 11/17/2024]
Abstract
PURPOSE Subarachnoid haemorrhage is a potentially fatal consequence of intracranial aneurysm rupture, however, it is difficult to predict if aneurysms will rupture. Prophylactic treatment of an intracranial aneurysm also involves risk, hence identifying rupture-prone aneurysms is of substantial clinical importance. This systematic review aims to evaluate the performance of machine learning algorithms for predicting intracranial aneurysm rupture risk. METHODS MEDLINE, Embase, Cochrane Library and Web of Science were searched until December 2023. Studies incorporating any machine learning algorithm to predict the risk of rupture of an intracranial aneurysm were included. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). PROSPERO registration: CRD42023452509. RESULTS Out of 10,307 records screened, 20 studies met the eligibility criteria for this review incorporating a total of 20,286 aneurysm cases. The machine learning models gave a 0.66-0.90 range for performance accuracy. The models were compared to current clinical standards in six studies and gave mixed results. Most studies posed high or unclear risks of bias and concerns for applicability, limiting the inferences that can be drawn from them. There was insufficient homogenous data for a meta-analysis. CONCLUSIONS Machine learning can be applied to predict the risk of rupture for intracranial aneurysms. However, the evidence does not comprehensively demonstrate superiority to existing practice, limiting its role as a clinical adjunct. Further prospective multicentre studies of recent machine learning tools are needed to prove clinical validation before they are implemented in the clinic.
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Affiliation(s)
- Karan Daga
- School of Biomedical Engineering & Imaging Sciences, King's College London, BMEIS, King's College London. 1 Lambeth Palace Road, UK SE1 7EU, London, UK
- Guy's and St. Thomas' NHS Foundation Trust, Westminster Bridge Road, UK SE1 7EH, London, UK
| | - Siddharth Agarwal
- School of Biomedical Engineering & Imaging Sciences, King's College London, BMEIS, King's College London. 1 Lambeth Palace Road, UK SE1 7EU, London, UK
| | - Zaeem Moti
- Guy's and St. Thomas' NHS Foundation Trust, Westminster Bridge Road, UK SE1 7EH, London, UK
| | - Matthew B K Lee
- University College London Hospital NHS Foundation Trust, 235 Euston Rd, UK NW1 2BU, London, UK
| | - Munaib Din
- Guy's and St. Thomas' NHS Foundation Trust, Westminster Bridge Road, UK SE1 7EH, London, UK
| | - David Wood
- School of Biomedical Engineering & Imaging Sciences, King's College London, BMEIS, King's College London. 1 Lambeth Palace Road, UK SE1 7EU, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, BMEIS, King's College London. 1 Lambeth Palace Road, UK SE1 7EU, London, UK
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, BMEIS, King's College London. 1 Lambeth Palace Road, UK SE1 7EU, London, UK.
- Department of Neuroradiology, King's College Hospital, Denmark Hill, UK SE5 9RS, London, UK.
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Owens M, Tenhoeve SA, Rawson C, Azab M, Karsy M. Systematic Review of Radiomics and Artificial Intelligence in Intracranial Aneurysm Management. J Neuroimaging 2025; 35:e70037. [PMID: 40095247 PMCID: PMC11912304 DOI: 10.1111/jon.70037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2024] [Revised: 03/05/2025] [Accepted: 03/06/2025] [Indexed: 03/19/2025] Open
Abstract
Intracranial aneurysms, with an annual incidence of 2%-3%, reflect a rare disease associated with significant mortality and morbidity risks when ruptured. Early detection, risk stratification of high-risk subgroups, and prediction of patient outcomes are important to treatment. Radiomics is an emerging field using the quantification of medical imaging to identify parameters beyond traditional radiology interpretation that may offer diagnostic or prognostic significance. The general radiomic workflow involves image normalization and segmentation, feature extraction, feature selection or dimensional reduction, training of a predictive model, and validation of the said model. Artificial intelligence (AI) techniques have shown increasing interest in applications toward vascular pathologies, with some commercially successful software including AiDoc, RapidAI, and Viz.AI, as well as the more recent Viz Aneurysm. We performed a systematic review of 684 articles and identified 84 articles exploring the applications of radiomics and AI in aneurysm treatment. Most studies were published between 2018 and 2024, with over half of articles in 2022 and 2023. Studies included categories such as aneurysm diagnosis (25.0%), rupture risk prediction (50.0%), growth rate prediction (4.8%), hemodynamic assessment (2.4%), clinical outcome prediction (11.9%), and occlusion or stenosis assessment (6.0%). Studies utilized molecular data (2.4%), radiologic data alone (51.2%), clinical data alone (28.6%), and combined radiologic and clinical data (17.9%). These results demonstrate the current status of this emerging and exciting field. An increased pace of innovation in this space is likely with the expansion of clinical applications of radiomics and AI in multiple vascular pathologies.
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Affiliation(s)
- Monica‐Rae Owens
- Spencer Fox Eccles School of MedicineUniversity of UtahSalt Lake CityUtahUSA
| | - Samuel A. Tenhoeve
- Spencer Fox Eccles School of MedicineUniversity of UtahSalt Lake CityUtahUSA
| | - Clayton Rawson
- College of Osteopathic MedicineNOORDA CollegeProvoUtahUSA
| | - Mohammed Azab
- Kasr Al Ainy School of MedicineCairo UniversityAl ManialEgypt
| | - Michael Karsy
- Department of NeurosurgeryUniversity of MichiganAnn ArborMichiganUSA
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Jiang J, Rezaeitaleshmahalleh M, Tang J, Gemmette J, Pandey A. Improving rupture status prediction for intracranial aneurysms using wall shear stress informatics. Acta Neurochir (Wien) 2025; 167:15. [PMID: 39812848 PMCID: PMC11735576 DOI: 10.1007/s00701-024-06404-4] [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: 02/22/2024] [Accepted: 12/11/2024] [Indexed: 01/16/2025]
Abstract
BACKGROUND Wall shear stress (WSS) plays a crucial role in the natural history of intracranial aneurysms (IA). However, spatial variations among WSS have rarely been utilized to correlate with IAs' natural history. This study aims to establish the feasibility of using spatial patterns of WSS data to predict IAs' rupture status (i.e., ruptured versus unruptured). METHODS "Patient-specific" computational fluid dynamics (CFD) simulations were performed for 112 IAs; each IA's rupture status was known from medical records. Recall that CFD-simulated hemodynamics data (wall shear stress and its derivatives) are located on unstructured meshes. Hence, we mapped WSS data from an unstructured grid onto a unit disk (i.e., a uniformly sampled polar coordinate system); data in a uniformly sampled polar system is equivalent to image data. Mapped WSS data (onto the unit disk) were readily available for Radiomics analysis to extract spatial patterns of WSS data. We named this innovative technology "WSS-informatics" (i.e., using informatics techniques to analyze WSS data); the usefulness of WSS-informatics was demonstrated during the predictive modeling of IAs' rupture status. RESULTS None of the conventional WSS parameters correlated to IAs' rupture status. However, WSS-informatics metrics were discriminative (p-value < 0.05) to IAs' rupture status. Furthermore, predictive models with WSS-informatics features could significantly improve the prediction performance (area under the receiver operating characteristic curve [AUROC]: 0.78 vs. 0.85; p-value < 0.01). CONCLUSION The proposed innovations enabled the first study to use spatial patterns of WSS data to improve the predictive modeling of IAs' rupture status.
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Affiliation(s)
- Jingfeng Jiang
- Department of Biomedical Engineering, Michigan Technological University, Mineral and Material Science and Engineering Building, Room 309, 1400 Townsend Drive, Houghton, MI, USA.
- Joint Center for Biocomputing and Digital Health, Health Research Institute, Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, USA.
| | - Mostafa Rezaeitaleshmahalleh
- Department of Biomedical Engineering, Michigan Technological University, Mineral and Material Science and Engineering Building, Room 309, 1400 Townsend Drive, Houghton, MI, USA
- Joint Center for Biocomputing and Digital Health, Health Research Institute, Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, USA
| | - Jinshan Tang
- Department of Health Administration and Policy, College of Public Health, George Mason University, Fairfax, VA, USA
| | - Joseph Gemmette
- Department of Radiology, College of Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Aditya Pandey
- Department of Neurosurgery, College of Medicine, University of Michigan, Ann Arbor, MI, USA
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Bizjak Ž, Špiclin Ž. Aneurysm growth evaluation and detection: a computer-assisted follow-up MRA analysis. Sci Rep 2024; 14:19609. [PMID: 39179696 PMCID: PMC11343880 DOI: 10.1038/s41598-024-70453-z] [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: 04/18/2024] [Accepted: 08/16/2024] [Indexed: 08/26/2024] Open
Abstract
Growing intracranial aneurysms pose a high risk of rupture, making the detection and quantification of the growth crucial for timely treatment strategy adoption. In this paper we propose a computer-assisted approach based on the extraction of IA shapes from associated baseline and follow-up angiographic scans and non-rigid morphing of the two shapes. From the obtained shape deformations we computed four novel features, including differential volume (dV), surface area (dSA), aneurysm-size normalized median deformation path length (dMPL), and integral of cumulative deformation distances (dICDD). An experienced neuroradiologist manually extracted the IA shape models from the baseline and follow-up MRAs and, by utilizing size change and visual assessments, classified each aneurysm into stable with morphology changes, stable or growing. We investigated the classification performance and found that three of the novel and one cross-sectional feature exhibited significantly different mean values (p-value < 0.05 ; Tukey's HSD test) between the stable and growing IA groups, while the mean dICDD was significantly different between all the three groups. The cross-sectional features has sensitivity to growing IAs in range 0.05-0.86, while novel features had generally higher sensitivity in range 0.81-0.90, making them promising candidates as surrogate follow-up imaging-based biomarkers for IA growth detection. These findings may offer valuable information for clinical management of patients with IAs based on follow-up imaging.
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Affiliation(s)
- Žiga Bizjak
- Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia.
| | - Žiga Špiclin
- Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia
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Li Y, Zhang H, Sun Y, Fan Q, Wang L, Ji C, HuiGu, Chen B, Zhao S, Wang D, Yu P, Li J, Yang S, Zhang C, Wang X. Deep learning-based platform performs high detection sensitivity of intracranial aneurysms in 3D brain TOF-MRA: An external clinical validation study. Int J Med Inform 2024; 188:105487. [PMID: 38761459 DOI: 10.1016/j.ijmedinf.2024.105487] [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: 10/25/2023] [Revised: 05/06/2024] [Accepted: 05/15/2024] [Indexed: 05/20/2024]
Abstract
PURPOSE To evaluate the diagnostic efficacy of a developed artificial intelligence (AI) platform incorporating deep learning algorithms for the automated detection of intracranial aneurysms in time-of-flight (TOF) magnetic resonance angiography (MRA). METHOD This retrospective study encompassed 3D TOF MRA images acquired between January 2023 and June 2023, aiming to validate the presence of intracranial aneurysms via our developed AI platform. The manual segmentation results by experienced neuroradiologists served as the "gold standard". Following annotation of MRA images by neuroradiologists using InferScholar software, the AI platform conducted automatic segmentation of intracranial aneurysms. Various metrics including accuracy (ACC), balanced ACC, area under the curve (AUC), sensitivity (SE), specificity (SP), F1 score, Brier Score, and Net Benefit were utilized to evaluate the generalization of AI platform. Comparison of intracranial aneurysm identification performance was conducted between the AI platform and six radiologists with experience ranging from 3 to 12 years in interpreting MR images. Additionally, a comparative analysis was carried out between radiologists' detection performance based on independent visual diagnosis and AI-assisted diagnosis. Subgroup analyses were also performed based on the size and location of the aneurysms to explore factors impacting aneurysm detectability. RESULTS 510 patients were enrolled including 215 patients (42.16 %) with intracranial aneurysms and 295 patients (57.84 %) without aneurysms. Compared with six radiologists, the AI platform showed competitive discrimination power (AUC, 0.96), acceptable calibration (Brier Score loss, 0.08), and clinical utility (Net Benefit, 86.96 %). The AI platform demonstrated superior performance in detecting aneurysms with an overall SE, SP, ACC, balanced ACC, and F1 score of 91.63 %, 92.20 %, 91.96 %, 91.92 %, and 90.57 % respectively, outperforming the detectability of the two resident radiologists. For subgroup analysis based on aneurysm size and location, we observed that the SE of the AI platform for identifying tiny (diameter<3mm), small (3 mm ≤ diameter<5mm), medium (5 mm ≤ diameter<7mm) and large aneurysms (diameter ≥ 7 mm) was 87.80 %, 93.14 %, 95.45 %, and 100 %, respectively. Furthermore, the SE for detecting aneurysms in the anterior circulation was higher than that in the posterior circulation. Utilizing the AI assistance, six radiologists (i.e., two residents, two attendings and two professors) achieved statistically significant improvements in mean SE (residents: 71.40 % vs. 88.37 %; attendings: 82.79 % vs. 93.26 %; professors: 90.07 % vs. 97.44 %; P < 0.05) and ACC (residents: 85.29 % vs. 94.12 %; attendings: 91.76 % vs. 97.06 %; professors: 95.29 % vs. 98.82 %; P < 0.05) while no statistically significant change was observed in SP. Overall, radiologists' mean SE increased by 11.40 %, mean SP increased by 1.86 %, and mean ACC increased by 5.88 %, mean balanced ACC promoted by 6.63 %, mean F1 score grew by 7.89 %, and Net Benefit rose by 12.52 %, with a concurrent decrease in mean Brier score declined by 0.06. CONCLUSIONS The deep learning algorithms implemented in the AI platform effectively detected intracranial aneurysms on TOF-MRA and notably enhanced the diagnostic capabilities of radiologists. This indicates that the AI-based auxiliary diagnosis model can provide dependable and precise prediction to improve the diagnostic capacity of radiologists.
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Affiliation(s)
- Yuanyuan Li
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China; Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, China; Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, China
| | - Huiling Zhang
- Institute of Research, Infervision Medical Technology Co., Ltd, China
| | - Yun Sun
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China
| | - Qianrui Fan
- Institute of Research, Infervision Medical Technology Co., Ltd, China
| | - Long Wang
- Department of Cardiovascular Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China
| | - Congshan Ji
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China
| | - HuiGu
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China; Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, China; Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, China
| | - Baojin Chen
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China
| | - Shuo Zhao
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China; Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, China; Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, China
| | - Dawei Wang
- Institute of Research, Infervision Medical Technology Co., Ltd, China
| | - Pengxin Yu
- Institute of Research, Infervision Medical Technology Co., Ltd, China
| | - Junchen Li
- Department of Radiology, Changshu Hospital Affiliated to Nanjing University of Chinese Medicine, China
| | - Shifeng Yang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China.
| | - Chuanchen Zhang
- Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, China.
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China; Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, China.
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Gu J, Cao Y, Chai L, Xu E, Liu K, Chong Z, Zhang Y, Zou D, Xu Y, Wang J, Müller O, Cao J, Zhu G, Lu G. Delayed care-seeking in international migrant workers with imported malaria in China. J Travel Med 2024; 31:taae021. [PMID: 38335249 DOI: 10.1093/jtm/taae021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 12/12/2023] [Accepted: 02/08/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND Imported malaria cases continue to pose major challenges in China as well as in other countries that have achieved elimination. Early diagnosis and treatment of each imported malaria case is the key to successfully maintaining malaria elimination success. This study aimed to build an easy-to-use predictive nomogram to predict and intervene against delayed care-seeking among international migrant workers with imported malaria. METHODS A prediction model was built based on cases with imported malaria from 2012 to 2019, in Jiangsu Province, China. Routine surveillance information (e.g. sex, age, symptoms, origin country and length of stay abroad), data on the place of initial care-seeking and the gross domestic product (GDP) of the destination city were extracted. Multivariate logistic regression was performed to identify independent predictors and a nomogram was established to predict the risk of delayed care-seeking. The discrimination and calibration of the nomogram was performed using area under the curve and calibration plots. In addition, four machine learning models were used to make a comparison. RESULTS Of 2255 patients with imported malaria, 636 (28.2%) sought care within 24 h after symptom onset, and 577 (25.6%) sought care 3 days after symptom onset. Development of symptoms before entry into China, initial care-seeking from superior healthcare facilities and a higher GDP level of the destination city were significantly associated with delayed care-seeking among migrant workers with imported malaria. Based on these independent risk factors, an easy-to-use and intuitive nomogram was established. The calibration curves of the nomogram showed good consistency. CONCLUSIONS The tool provides public health practitioners with a method for the early detection of delayed care-seeking risk among international migrant workers with imported malaria, which may be of significance in improving post-travel healthcare for labour migrants, reducing the risk of severe malaria, preventing malaria reintroduction and sustaining achievements in malaria elimination.
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Affiliation(s)
- Jiyue Gu
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu Province, 225009, China
| | - Yuanyuan Cao
- National Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Wuxi, Jiangsu Province, 214064, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu Province, 211166, China
| | - Liying Chai
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu Province, 225009, China
| | - Enyu Xu
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu Province, 225009, China
| | - Kaixuan Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu Province, 225009, China
| | - Zeyin Chong
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu Province, 225009, China
| | - Yuying Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu Province, 225009, China
| | - Dandan Zou
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu Province, 225009, China
| | - Yuhui Xu
- Center for Disease Control and Prevention, Yangzhou, Jiangsu Province, 225007, China
| | - Jian Wang
- Yangzhou Schistosomiasis and Parasitic Disease Control Office, Yangzhou, Jiangsu Province, 225007, China
| | - Olaf Müller
- Institute of Global Health, Medical School, Ruprecht-Karls-University Heidelberg, Heidelberg, 69117, Germany
| | - Jun Cao
- National Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Wuxi, Jiangsu Province, 214064, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu Province, 211166, China
| | - Guoding Zhu
- National Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Wuxi, Jiangsu Province, 214064, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu Province, 211166, China
| | - Guangyu Lu
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu Province, 225009, China
- Jiangsu Key Laboratory of Zoonosis, Yangzhou, 225009, China
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Cao H, Zeng H, Lv L, Wang Q, Ouyang H, Gui L, Hua P, Yang S. Assessment of intracranial aneurysm rupture risk using a point cloud-based deep learning model. Front Physiol 2024; 15:1293380. [PMID: 38426204 PMCID: PMC10901972 DOI: 10.3389/fphys.2024.1293380] [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: 09/26/2023] [Accepted: 01/26/2024] [Indexed: 03/02/2024] Open
Abstract
Background and Purpose: Precisely assessing the likelihood of an intracranial aneurysm rupturing is critical for guiding clinical decision-making. The objective of this study is to construct and validate a deep learning framework utilizing point clouds to forecast the likelihood of aneurysm rupturing. Methods: The dataset included in this study consisted of a total of 623 aneurysms, with 211 of them classified as ruptured and 412 as unruptured, which were obtained from two separate projects within the AneuX morphology database. The HUG project, which included 124 ruptured aneurysms and 340 unruptured aneurysms, was used to train and internally validate the model. For external validation, another project named @neurIST was used, which included 87 ruptured and 72 unruptured aneurysms. A standardized method was employed to isolate aneurysms and a segment of their parent vessels from the original 3D vessel models. These models were then converted into a point cloud format using open3d package to facilitate training of the deep learning network. The PointNet++ architecture was utilized to process the models and generate risk scores through a softmax layer. Finally, two models, the dome and cut1 model, were established and then subjected to a comprehensive comparison of statistical indices with the LASSO regression model built by the dataset authors. Results: The cut1 model outperformed the dome model in the 5-fold cross-validation, with the mean AUC values of 0.85 and 0.81, respectively. Furthermore, the cut1 model beat the morphology-based LASSO regression model with an AUC of 0.82. However, as the original dataset authors stated, we observed potential generalizability concerns when applying trained models to datasets with different selection biases. Nevertheless, our method outperformed the LASSO regression model in terms of generalizability, with an AUC of 0.71 versus 0.67. Conclusion: The point cloud, as a 3D visualization technique for intracranial aneurysms, can effectively capture the spatial contour and morphological aspects of aneurysms. More structural features between the aneurysm and its parent vessels can be exposed by keeping a portion of the parent vessels, enhancing the model's performance. The point cloud-based deep learning model exhibited good performance in predicting rupture risk while also facing challenges in generalizability.
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Affiliation(s)
- Heshan Cao
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Hui Zeng
- Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Lei Lv
- Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qi Wang
- Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Hua Ouyang
- Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Long Gui
- Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ping Hua
- Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Songran Yang
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Biobank and Bioinformatics, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
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Habibi MA, Fakhfouri A, Mirjani MS, Razavi A, Mortezaei A, Soleimani Y, Lotfi S, Arabi S, Heidaresfahani L, Sadeghi S, Minaee P, Eazi S, Rashidi F, Shafizadeh M, Majidi S. Prediction of cerebral aneurysm rupture risk by machine learning algorithms: a systematic review and meta-analysis of 18,670 participants. Neurosurg Rev 2024; 47:34. [PMID: 38183490 DOI: 10.1007/s10143-023-02271-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 12/08/2023] [Accepted: 12/29/2023] [Indexed: 01/08/2024]
Abstract
It is possible to identify unruptured intracranial aneurysms (UIA) using machine learning (ML) algorithms, which can be a life-saving strategy, especially in high-risk populations. To better understand the importance and effectiveness of ML algorithms in practice, a systematic review and meta-analysis were conducted to predict cerebral aneurysm rupture risk. PubMed, Scopus, Web of Science, and Embase were searched without restrictions until March 20, 2023. Eligibility criteria included studies that used ML approaches in patients with cerebral aneurysms confirmed by DSA, CTA, or MRI. Out of 35 studies included, 33 were cohort, and 11 used digital subtraction angiography (DSA) as their reference imaging modality. Middle cerebral artery (MCA) and anterior cerebral artery (ACA) were the commonest locations of aneurysmal vascular involvement-51% and 40%, respectively. The aneurysm morphology was saccular in 48% of studies. Ten of 37 studies (27%) used deep learning techniques such as CNNs and ANNs. Meta-analysis was performed on 17 studies: sensitivity of 0.83 (95% confidence interval (CI), 0.77-0.88); specificity of 0.83 (95% CI, 0.75-0.88); positive DLR of 4.81 (95% CI, 3.29-7.02) and the negative DLR of 0.20 (95% CI, 0.14-0.29); a diagnostic score of 3.17 (95% CI, 2.55-3.78); odds ratio of 23.69 (95% CI, 12.75-44.01). ML algorithms can effectively predict the risk of rupture in cerebral aneurysms with good levels of accuracy, sensitivity, and specificity. However, further research is needed to enhance their diagnostic performance in predicting the rupture status of IA.
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Affiliation(s)
- Mohammad Amin Habibi
- Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Science, Tehran, Iran.
| | - Amirata Fakhfouri
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Mohammad Sina Mirjani
- Student Research Committee, Faculty of Medicine, Qom University of Medical Sciences, Qom, Iran
| | - Alireza Razavi
- Student Research Committee, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Ali Mortezaei
- Student Research Committee, Gonabad University of Medical Sciences, Gonabad, Iran
| | - Yasna Soleimani
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Sohrab Lotfi
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Shayan Arabi
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Ladan Heidaresfahani
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Sara Sadeghi
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Poriya Minaee
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - SeyedMohammad Eazi
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Farhang Rashidi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Milad Shafizadeh
- Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Science, Tehran, Iran
| | - Shahram Majidi
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, 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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Xie Y, Liu S, Lin H, Wu M, Shi F, Pan F, Zhang L, Song B. Automatic risk prediction of intracranial aneurysm on CTA image with convolutional neural networks and radiomics analysis. Front Neurol 2023; 14:1126949. [PMID: 37456640 PMCID: PMC10345199 DOI: 10.3389/fneur.2023.1126949] [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: 12/18/2022] [Accepted: 05/30/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND Intracranial aneurysm (IA) is a nodular protrusion of the arterial wall caused by the localized abnormal enlargement of the lumen of a brain artery, which is the primary cause of subarachnoid hemorrhage. Accurate rupture risk prediction can effectively aid treatment planning, but conventional rupture risk estimation based on clinical information is subjective and time-consuming. METHODS We propose a novel classification method based on the CTA images for differentiating aneurysms that are prone to rupture. The main contribution of this study is that the learning-based method proposed in this study leverages deep learning and radiomics features and integrates clinical information for a more accurate prediction of the risk of rupture. Specifically, we first extracted the provided aneurysm regions from the CTA images as 3D patches with the lesions located at their centers. Then, we employed an encoder using a 3D convolutional neural network (CNN) to extract complex latent features automatically. These features were then combined with radiomics features and clinical information. We further applied the LASSO regression method to find optimal features that are highly relevant to the rupture risk information, which is fed into a support vector machine (SVM) for final rupture risk prediction. RESULTS The experimental results demonstrate that our classification method can achieve accuracy and AUC scores of 89.78% and 89.09%, respectively, outperforming all the alternative methods. DISCUSSION Our study indicates that the incorporation of CNN and radiomics analysis can improve the prediction performance, and the selected optimal feature set can provide essential biomarkers for the determination of rupture risk, which is also of great clinical importance for individualized treatment planning and patient care of IA.
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Affiliation(s)
- Yuan Xie
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Shuyu Liu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Hen Lin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Min Wu
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Feng Pan
- Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Lichi Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
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Chen R, Mo X, Chen Z, Feng P, Li H. An Integrated Model Combining Machine Learning and Deep Learning Algorithms for Classification of Rupture Status of IAs. Front Neurol 2022; 13:868395. [PMID: 35645962 PMCID: PMC9133352 DOI: 10.3389/fneur.2022.868395] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 04/12/2022] [Indexed: 02/04/2023] Open
Abstract
Background The rupture risk assessment of intracranial aneurysms (IAs) is clinically relevant. How to accurately assess the rupture risk of IAs remains a challenge in clinical decision-making. Purpose We aim to build an integrated model to improve the assessment of the rupture risk of IAs. Materials and Methods A total of 148 (39 ruptured and 109 unruptured) IA subjects were retrospectively computed with computational fluid dynamics (CFDs), and the integrated models were proposed by combining machine learning (ML) and deep learning (DL) algorithms. ML algorithms that include random forest (RF), k-nearest neighbor (KNN), XGBoost (XGB), support vector machine (SVM), and LightGBM were, respectively, adopted to classify ruptured and unruptured IAs. A Pointnet DL algorithm was applied to extract hemodynamic cloud features from the hemodynamic clouds obtained from CFD. Morphological variables and hemodynamic parameters along with the extracted hemodynamic cloud features were acted as the inputs to the classification models. The classification results with and without hemodynamic cloud features are computed and compared. Results Without consideration of hemodynamic cloud features, the classification accuracy of RF, KNN, XGB, SVM, and LightGBM was 0.824, 0.759, 0.839, 0.860, and 0.829, respectively, and the AUCs of them were 0.897, 0.584, 0.892, 0.925, and 0.890, respectively. With the consideration of hemodynamic cloud features, the accuracy successively increased to 0.908, 0.873, 0.900, 0.926, and 0.917. Meanwhile, the AUCs reached 0.952, 0.881, 0.950, 0.969, and 0.965 eventually. Adding consideration of hemodynamic cloud features, the SVM could perform best with the highest accuracy of 0.926 and AUC of 0.969, respectively. Conclusion The integrated model combining ML and DL algorithms could improve the classification of IAs. Adding consideration of hemodynamic cloud features could bring more accurate classification, and hemodynamic cloud features were important for the discrimination of ruptured IAs.
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Mensah E, Pringle C, Roberts G, Gurusinghe N, Golash A, Alalade AF. Deep Learning in the Management of Intracranial Aneurysms and Cerebrovascular Diseases: A Review of the Current Literature. World Neurosurg 2022; 161:39-45. [DOI: 10.1016/j.wneu.2022.02.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/31/2022] [Accepted: 02/01/2022] [Indexed: 01/10/2023]
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