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Lehnen NC, Schievelkamp AH, Gronemann C, Haase R, Krause I, Gansen M, Fleckenstein T, Dorn F, Radbruch A, Paech D. Impact of an AI software on the diagnostic performance and reading time for the detection of cerebral aneurysms on time of flight MR-angiography. Neuroradiology 2024; 66:1153-1160. [PMID: 38619571 DOI: 10.1007/s00234-024-03351-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 03/29/2024] [Indexed: 04/16/2024]
Abstract
PURPOSE To evaluate the impact of an AI-based software trained to detect cerebral aneurysms on TOF-MRA on the diagnostic performance and reading times across readers with varying experience levels. METHODS One hundred eighty-six MRI studies were reviewed by six readers to detect cerebral aneurysms. Initially, readings were assisted by the CNN-based software mdbrain. After 6 weeks, a second reading was conducted without software assistance. The results were compared to the consensus reading of two neuroradiological specialists and sensitivity (lesion and patient level), specificity (patient level), and false positives per case were calculated for the group of all readers, for the subgroup of physicians, and for each individual reader. Also, reading times for each reader were measured. RESULTS The dataset contained 54 aneurysms. The readers had no experience (three medical students), 2 years experience (resident in neuroradiology), 6 years experience (radiologist), and 12 years (neuroradiologist). Significant improvements of overall specificity and the overall number of false positives per case were observed in the reading with AI support. For the physicians, we found significant improvements of sensitivity on lesion and patient level and false positives per case. Four readers experienced reduced reading times with the software, while two encountered increased times. CONCLUSION In the reading with the AI-based software, we observed significant improvements in terms of specificity and false positives per case for the group of all readers and significant improvements of sensitivity and false positives per case for the physicians. Further studies are needed to investigate the effects of the AI-based software in a prospective setting.
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Affiliation(s)
- Nils C Lehnen
- Department of Neuroradiology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127, Bonn, Germany.
- Research Group Clinical Neuroimaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
| | - Arndt-Hendrik Schievelkamp
- Department of Neuroradiology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127, Bonn, Germany
| | - Christian Gronemann
- Department of Neuroradiology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127, Bonn, Germany
| | - Robert Haase
- Department of Neuroradiology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127, Bonn, Germany
| | - Inga Krause
- Department of Neuroradiology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127, Bonn, Germany
| | - Max Gansen
- Department of Neuroradiology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127, Bonn, Germany
| | - Tobias Fleckenstein
- Department of Neuroradiology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127, Bonn, Germany
| | - Franziska Dorn
- Department of Neuroradiology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127, Bonn, Germany
| | - Alexander Radbruch
- Department of Neuroradiology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127, Bonn, Germany
- Research Group Clinical Neuroimaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Daniel Paech
- Department of Neuroradiology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127, Bonn, Germany
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Qu J, Niu H, Li Y, Chen T, Peng F, Xia J, He X, Xu B, Chen X, Li R, Liu A, Zhang X, Li C. A deep learning framework for intracranial aneurysms automatic segmentation and detection on magnetic resonance T1 images. Eur Radiol 2024; 34:2838-2848. [PMID: 37843574 DOI: 10.1007/s00330-023-10295-x] [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: 11/16/2022] [Revised: 07/15/2023] [Accepted: 08/08/2023] [Indexed: 10/17/2023]
Abstract
OBJECTIVES To design a deep learning-based framework for automatic segmentation and detection of intracranial aneurysms (IAs) on magnetic resonance T1 images and test the robustness and performance of framework. METHODS A retrospective diagnostic study was conducted based on 159 IAs from 136 patients who underwent the T1 images. Among them, 127 cases were randomly selected for training and validation, and 32 cases were used to assess the accuracy and consistency of our algorithm. We developed and assembled three convolutional neural networks for the segmentation and detection of IAs. The segmentation and detection performance of the model were compared with the ground truth, and various metrics were calculated at the voxel level, IAs level, and patient level to show the performance of our framework. RESULTS Our assembled model achieved overall Dice, voxel-level sensitivity, specificity, balanced accuracy, and F1 score of 0.802, 0.874, 0.9998, 0.937, and 0.802, respectively. A coincidence greater than 0.7 between the aneurysms predicted by the model and the ground truth was considered as a true positive. For IAs detection, the sensitivity reached 90.63% with 0.58 false positives per case. The volume of IAs segmented by our model showed a high agreement and consistency with the volume of IAs labeled by experts. CONCLUSION The deep learning framework is achievable and robust for IAs segmentation and detection. Our model offers more clinical application opportunities compared to digital subtraction angiography (DSA)-based, CTA-based, and MRA-based methods. CLINICAL RELEVANCE STATEMENT Our deep learning framework effectively detects and segments intracranial aneurysms using clinical routine T1 sequences, showing remarkable effectiveness and offering great potential for improving the detection of latent intracranial aneurysms and enabling early identification. KEY POINTS •There is no segmentation method based on clinical routine T1 images. Our study shows that the proper deep learning framework can effectively localize the intracranial aneurysms. •The T1-based segmentation and detection method is more universal than other angiography-based detection methods, which can potentially reduce missed diagnoses caused by the absence of angiography images. •The deep learning framework is robust and has the potential to be applied in a clinical setting.
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Affiliation(s)
- Junda Qu
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, China
| | - Hao Niu
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yutang Li
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, China
| | - Ting Chen
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, China
| | - Fei Peng
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiaxiang Xia
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaoxin He
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Boya Xu
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xuge Chen
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Rui Li
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Aihua Liu
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| | - Xu Zhang
- School of Biomedical Engineering, Capital Medical University, Beijing, China.
- Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, China.
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, China.
| | - Chunlin Li
- School of Biomedical Engineering, Capital Medical University, Beijing, China.
- Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, China.
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, China.
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Assis Y, Liao L, Pierre F, Anxionnat R, Kerrien E. Intracranial aneurysm detection: an object detection perspective. Int J Comput Assist Radiol Surg 2024:10.1007/s11548-024-03132-z. [PMID: 38632166 DOI: 10.1007/s11548-024-03132-z] [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: 09/27/2023] [Accepted: 03/28/2024] [Indexed: 04/19/2024]
Abstract
PURPOSE Intracranial aneurysm detection from 3D Time-Of-Flight Magnetic Resonance Angiography images is a problem of increasing clinical importance. Recently, a streak of methods have shown promising performance by using segmentation neural networks. However, these methods may be less relevant in a clinical settings where diagnostic decisions rely on detecting objects rather than their segmentation. METHODS We introduce a 3D single-stage object detection method tailored for small object detection such as aneurysms. Our anchor-free method incorporates fast data annotation, adapted data sampling and generation to address class imbalance problem, and spherical representations for improved detection. RESULTS A comprehensive evaluation was conducted, comparing our method with the state-of-the-art SCPM-Net, nnDetection and nnUNet baselines, using two datasets comprising 402 subjects. The evaluation used adapted object detection metrics. Our method exhibited comparable or superior performance, with an average precision of 78.96%, sensitivity of 86.78%, and 0.53 false positives per case. CONCLUSION Our method significantly reduces the detection complexity compared to existing methods and highlights the advantages of object detection over segmentation-based approaches for aneurysm detection. It also holds potential for application to other small object detection problems.
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Affiliation(s)
- Youssef Assis
- Université de Lorraine, CNRS, Inria, LORIA, 54000, Nancy, France.
| | - Liang Liao
- Université de Lorraine, CNRS, Inria, LORIA, 54000, Nancy, France
- Department of Diagnostic and Therapeutic Interventional Neuroradiology, Université de Lorraine, CHRU-Nancy, 54000, Nancy, France
- Université de Lorraine, Inserm, IADI, 54000, Nancy, France
| | - Fabien Pierre
- Université de Lorraine, CNRS, Inria, LORIA, 54000, Nancy, France
| | - René Anxionnat
- Department of Diagnostic and Therapeutic Interventional Neuroradiology, Université de Lorraine, CHRU-Nancy, 54000, Nancy, France
- Université de Lorraine, Inserm, IADI, 54000, Nancy, France
| | - Erwan Kerrien
- Université de Lorraine, CNRS, Inria, LORIA, 54000, Nancy, France
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Wen Z, Wang Y, Zhong Y, Hu Y, Yang C, Peng Y, Zhan X, Zhou P, Zeng Z. Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images. Front Neurol 2024; 15:1391382. [PMID: 38694771 PMCID: PMC11061371 DOI: 10.3389/fneur.2024.1391382] [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: 02/25/2024] [Accepted: 04/02/2024] [Indexed: 05/04/2024] Open
Abstract
Intracranial aneurysm is a high-risk disease, with imaging playing a crucial role in their diagnosis and treatment. The rapid advancement of artificial intelligence in imaging technology holds promise for the development of AI-based radiomics predictive models. These models could potentially enable the automatic detection and diagnosis of intracranial aneurysms, assess their status, and predict outcomes, thereby assisting in the creation of personalized treatment plans. In addition, these techniques could improve diagnostic efficiency for physicians and patient prognoses. This article aims to review the progress of artificial intelligence radiomics in the study of intracranial aneurysms, addressing the challenges faced and future prospects, in hopes of introducing new ideas for the precise diagnosis and treatment of intracranial aneurysms.
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Affiliation(s)
- Zhongjian Wen
- School of Nursing, Southwest Medical University, Luzhou, China
- Wound Healing Basic Research and Clinical Application Key Laboratory of Luzhou, School of Nursing, Southwest Medical University, Luzhou, China
| | - Yiren Wang
- School of Nursing, Southwest Medical University, Luzhou, China
- Wound Healing Basic Research and Clinical Application Key Laboratory of Luzhou, School of Nursing, Southwest Medical University, Luzhou, China
| | - Yuxin Zhong
- School of Nursing, Guizhou Medical University, Guiyang, China
| | - Yiheng Hu
- Department of Medical Imaging, Southwest Medical University, Luzhou, China
| | - Cheng Yang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, China
| | - Yan Peng
- Department of Interventional Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xiang Zhan
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Ping Zhou
- Wound Healing Basic Research and Clinical Application Key Laboratory of Luzhou, School of Nursing, Southwest Medical University, Luzhou, China
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Zhen Zeng
- Psychiatry Department, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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Suzuki Y, Ueyama T, Sakata K, Kasahara A, Iwanaga H, Yasaka K, Abe O. High-angular resolution diffusion imaging generation using 3d u-net. Neuroradiology 2024; 66:371-387. [PMID: 38236423 DOI: 10.1007/s00234-024-03282-6] [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: 10/25/2023] [Accepted: 12/28/2023] [Indexed: 01/19/2024]
Abstract
PURPOSE To investigate the effects on tractography of artificial intelligence-based prediction of motion-probing gradients (MPGs) in diffusion-weighted imaging (DWI). METHODS The 251 participants in this study were patients with brain tumors or epileptic seizures who underwent MRI to depict tractography. DWI was performed with 64 MPG directions and b = 0 s/mm2 images. The dataset was divided into a training set of 191 (mean age 45.7 [± 19.1] years), a validation set of 30 (mean age 41.6 [± 19.1] years), and a test set of 30 (mean age 49.6 [± 18.3] years) patients. Supervised training of a convolutional neural network was performed using b = 0 images and the first 32 axes of MPG images as the input data and the second 32 axes as the reference data. The trained model was applied to the test data, and tractography was performed using (a) input data only; (b) input plus prediction data; and (c) b = 0 images and the 64 MPG data (as a reference). RESULTS In Q-ball imaging tractography, the average dice similarity coefficient (DSC) of the input plus prediction data was 0.715 (± 0.064), which was significantly higher than that of the input data alone (0.697 [± 0.070]) (p < 0.05). In generalized q-sampling imaging tractography, the average DSC of the input plus prediction data was 0.769 (± 0.091), which was also significantly higher than that of the input data alone (0.738 [± 0.118]) (p < 0.01). CONCLUSION Diffusion tractography is improved by adding predicted MPG images generated by an artificial intelligence model.
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Affiliation(s)
- Yuichi Suzuki
- Radiology Center, The University of Tokyo Hospital, Tokyo, Japan
| | - Tsuyoshi Ueyama
- Radiology Center, The University of Tokyo Hospital, Tokyo, Japan
| | - Kentarou Sakata
- Radiology Center, The University of Tokyo Hospital, Tokyo, Japan
| | - Akihiro Kasahara
- Radiology Center, The University of Tokyo Hospital, Tokyo, Japan
| | - Hideyuki Iwanaga
- Radiology Center, The University of Tokyo Hospital, Tokyo, Japan
| | - Koichiro Yasaka
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan.
| | - Osamu Abe
- Radiology Center, The University of Tokyo Hospital, Tokyo, Japan
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
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Nishi H, Cancelliere NM, Rustici A, Charbonnier G, Chan V, Spears J, Marotta TR, Mendes Pereira V. Deep learning-based cerebral aneurysm segmentation and morphological analysis with three-dimensional rotational angiography. J Neurointerv Surg 2024; 16:197-203. [PMID: 37192786 DOI: 10.1136/jnis-2023-020192] [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: 02/16/2023] [Accepted: 04/14/2023] [Indexed: 05/18/2023]
Abstract
BACKGROUND The morphological assessment of cerebral aneurysms based on cerebral angiography is an essential step when planning strategy and device selection in endovascular treatment, but manual evaluation by human raters only has moderate interrater/intrarater reliability. METHODS We collected data for 889 cerebral angiograms from consecutive patients with suspected cerebral aneurysms at our institution from January 2017 to October 2021. The automatic morphological analysis model was developed on the derivation cohort dataset consisting of 388 scans with 437 aneurysms, and the performance of the model was tested on the validation cohort dataset consisting of 96 scans with 124 aneurysms. Five clinically important parameters were automatically calculated by the model: aneurysm volume, maximum aneurysm size, neck size, aneurysm height, and aspect ratio. RESULTS On the validation cohort dataset the average aneurysm size was 7.9±4.6 mm. The proposed model displayed high segmentation accuracy with a mean Dice similarity index of 0.87 (median 0.93). All the morphological parameters were significantly correlated with the reference standard (all P<0.0001; Pearson correlation analysis). The difference in the maximum aneurysm size between the model prediction and reference standard was 0.5±0.7 mm (mean±SD). The difference in neck size between the model prediction and reference standard was 0.8±1.7 mm (mean±SD). CONCLUSION The automatic aneurysm analysis model based on angiography data exhibited high accuracy for evaluating the morphological characteristics of cerebral aneurysms.
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Affiliation(s)
- Hidehisa Nishi
- Department of Surgery, Division of Neurosurgery, St Michael's Hospital, Toronto, Ontario, Canada
- RADIS Lab, Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada
| | - Nicole M Cancelliere
- Department of Surgery, Division of Neurosurgery, St Michael's Hospital, Toronto, Ontario, Canada
- RADIS Lab, Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada
| | - Ariana Rustici
- RADIS Lab, Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada
| | - Guillaume Charbonnier
- RADIS Lab, Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada
| | - Vanessa Chan
- RADIS Lab, Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada
| | - Julian Spears
- Department of Surgery, Division of Neurosurgery, St Michael's Hospital, Toronto, Ontario, Canada
| | - Thomas R Marotta
- Department of Medical Imaging, St Michael's Hospital, Toronto, Ontario, Canada
| | - Vitor Mendes Pereira
- Department of Surgery, Division of Neurosurgery, St Michael's Hospital, Toronto, Ontario, Canada
- RADIS Lab, Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada
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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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Abdollahifard S, Farrokhi A, Kheshti F, Jalali M, Mowla A. Application of convolutional network models in detection of intracranial aneurysms: A systematic review and meta-analysis. Interv Neuroradiol 2023; 29:738-747. [PMID: 35549574 PMCID: PMC10680951 DOI: 10.1177/15910199221097475] [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: 03/03/2022] [Accepted: 04/11/2022] [Indexed: 11/15/2022] Open
Abstract
INTRODUCTION Intracranial aneurysms have a high prevalence in human population. It also has a heavy burden of disease and high mortality rate in the case of rupture. Convolutional neural network(CNN) is a type of deep learning architecture which has been proven powerful to detect intracranial aneurysms. METHODS Four databases were searched using artificial intelligence, intracranial aneurysms, and synonyms to find eligible studies. Articles which had applied CNN for detection of intracranial aneurisms were included in this review. Sensitivity and specificity of the models and human readers regarding modality, size, and location of aneurysms were sought to be extracted. Random model was the preferred model for analyses using CMA 2 to determine pooled sensitivity and specificity. RESULTS Overall, 20 studies were used in this review. Deep learning models could detect intracranial aneurysms with a sensitivity of 90/6% (CI: 87/2-93/2%) and specificity of 94/6% (CI: 0/914-0/966). CTA was the most sensitive modality (92.0%(CI:85/2-95/8%)). Overall sensitivity of the models for aneurysms more than 3 mm was above 98% (98%-100%) and 74.6 for aneurysms less than 3 mm. With the aid of AI, the clinicians' sensitivity increased to 12/8% and interrater agreement to 0/193. CONCLUSION CNN models had an acceptable sensitivity for detection of intracranial aneurysms, surpassing human readers in some fields. The logical approach for application of deep learning models would be its use as a highly capable assistant. In essence, deep learning models are a groundbreaking technology that can assist clinicians and allow them to diagnose intracranial aneurysms more accurately.
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Affiliation(s)
- Saeed Abdollahifard
- Research center for neuromodulation and pain, Shiraz, Iran
- Student research committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Amirmohammad Farrokhi
- Research center for neuromodulation and pain, Shiraz, Iran
- Student research committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Fatemeh Kheshti
- Research center for neuromodulation and pain, Shiraz, Iran
- Student research committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mahtab Jalali
- Research center for neuromodulation and pain, Shiraz, Iran
- Student research committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Ashkan Mowla
- Division of Stroke and Endovascular Neurosurgery, Department of Neurological Surgery, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA, USA
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Dundamadappa SK. AI tools in Emergency Radiology reading room: a new era of Radiology. Emerg Radiol 2023; 30:647-657. [PMID: 37420044 DOI: 10.1007/s10140-023-02154-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 06/20/2023] [Indexed: 07/09/2023]
Abstract
Artificial intelligence tools in radiology practices have surged, with modules developed to target specific findings becoming increasingly prevalent and proving valuable in the daily emergency room radiology practice. The number of US Food and Drug Administration-cleared radiology-related algorithms has soared from just 10 in early 2017 to over 200 presently. This review will concentrate on the present utilization of AI tools in clinical ER radiology setting, including a brief discussion of the limitations of the technique. As radiologists, it is essential that we embrace this technology, comprehend its constraints, and use it to improve patient care.
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Nader R, Bourcier R, Autrusseau F. Using deep learning for an automatic detection and classification of the vascular bifurcations along the Circle of Willis. Med Image Anal 2023; 89:102919. [PMID: 37619447 DOI: 10.1016/j.media.2023.102919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 06/01/2023] [Accepted: 07/31/2023] [Indexed: 08/26/2023]
Abstract
Most of the intracranial aneurysms (ICA) occur on a specific portion of the cerebral vascular tree named the Circle of Willis (CoW). More particularly, they mainly arise onto fifteen of the major arterial bifurcations constituting this circular structure. Hence, for an efficient and timely diagnosis it is critical to develop some methods being able to accurately recognize each Bifurcation of Interest (BoI). Indeed, an automatic extraction of the bifurcations presenting the higher risk of developing an ICA would offer the neuroradiologists a quick glance at the most alarming areas. Due to the recent efforts on Artificial Intelligence, Deep Learning turned out to be the best performing technology for many pattern recognition tasks. Moreover, various methods have been particularly designed for medical image analysis purposes. This study intends to assist the neuroradiologists to promptly locate any bifurcation presenting a high risk of ICA occurrence. It can be seen as a Computer Aided Diagnosis scheme, where the Artificial Intelligence facilitates the access to the regions of interest within the MRI. In this work, we propose a method for a fully automatic detection and recognition of the bifurcations of interest forming the Circle of Willis. Several neural networks architectures have been tested, and we thoroughly evaluate the bifurcation recognition rate.
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Affiliation(s)
- Rafic Nader
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, F-44000 Nantes, France
| | - Romain Bourcier
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, F-44000 Nantes, France
| | - Florent Autrusseau
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, F-44000 Nantes, France; Nantes Université, Polytech'Nantes, LTeN, U-6607, Rue Ch. Pauc, 44306, Nantes, France.
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Gilotra K, Swarna S, Mani R, Basem J, Dashti R. Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease. Front Hum Neurosci 2023; 17:1254417. [PMID: 37746051 PMCID: PMC10516608 DOI: 10.3389/fnhum.2023.1254417] [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: 07/07/2023] [Accepted: 08/23/2023] [Indexed: 09/26/2023] Open
Abstract
Introduction Cerebrovascular diseases are known to cause significant morbidity and mortality to the general population. In patients with cerebrovascular disease, prompt clinical evaluation and radiographic interpretation are both essential in optimizing clinical management and in triaging patients for critical and potentially life-saving neurosurgical interventions. With recent advancements in the domains of artificial intelligence (AI) and machine learning (ML), many AI and ML algorithms have been developed to further optimize the diagnosis and subsequent management of cerebrovascular disease. Despite such advances, further studies are needed to substantively evaluate both the diagnostic accuracy and feasibility of these techniques for their application in clinical practice. This review aims to analyze the current use of AI and MI algorithms in the diagnosis of, and clinical decision making for cerebrovascular disease, and to discuss both the feasibility and future applications of utilizing such algorithms. Methods We review the use of AI and ML algorithms to assist clinicians in the diagnosis and management of ischemic stroke, hemorrhagic stroke, intracranial aneurysms, and arteriovenous malformations (AVMs). After identifying the most widely used algorithms, we provide a detailed analysis of the accuracy and effectiveness of these algorithms in practice. Results The incorporation of AI and ML algorithms for cerebrovascular patients has demonstrated improvements in time to detection of intracranial pathologies such as intracerebral hemorrhage (ICH) and infarcts. For ischemic and hemorrhagic strokes, commercial AI software platforms such as RapidAI and Viz.AI have bene implemented into routine clinical practice at many stroke centers to expedite the detection of infarcts and ICH, respectively. Such algorithms and neural networks have also been analyzed for use in prognostication for such cerebrovascular pathologies. These include predicting outcomes for ischemic stroke patients, hematoma expansion, risk of aneurysm rupture, bleeding of AVMs, and in predicting outcomes following interventions such as risk of occlusion for various endovascular devices. Preliminary analyses have yielded promising sensitivities when AI and ML are used in concert with imaging modalities and a multidisciplinary team of health care providers. Conclusion The implementation of AI and ML algorithms to supplement clinical practice has conferred a high degree of accuracy, efficiency, and expedited detection in the clinical and radiographic evaluation and management of ischemic and hemorrhagic strokes, AVMs, and aneurysms. Such algorithms have been explored for further purposes of prognostication for these conditions, with promising preliminary results. Further studies should evaluate the longitudinal implementation of such techniques into hospital networks and residency programs to supplement clinical practice, and the extent to which these techniques improve patient care and clinical outcomes in the long-term.
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Affiliation(s)
| | | | | | | | - Reza Dashti
- Dashti Lab, Department of Neurological Surgery, Stony Brook University Hospital, Stony Brook, NY, United States
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12
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Hu T, Yang H, Ni W. A framework for intracranial aneurysm detection and rupture analysis on DSA. J Clin Neurosci 2023; 115:101-107. [PMID: 37542820 DOI: 10.1016/j.jocn.2023.07.025] [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: 05/21/2023] [Revised: 07/10/2023] [Accepted: 07/27/2023] [Indexed: 08/07/2023]
Abstract
BACKGROUND Intracranial aneurysm is a severe cerebrovascular disease that can result in subarachnoid hemorrhage (SAH), leading to high incidence and mortality rates. Computer-aided detection of aneurysms can assist doctors in enhancing diagnostic accuracy. The analysis of aneurysm imaging holds considerable predictive value for aneurysm rupture. This paper presents a method for the detection of aneurysms and analysis of ruptures using digital subtraction angiography (DSA). METHODS A total of 263 aneurysms were analyzed, with 125 being ruptured and 138 being unruptured. Firstly, a filter based on the eigenvalues of the Hessian matrix was proposed for aneurysm detection. The filter's detection parameters can be automatically obtained through Bayesian optimization. Aneurysms were detected based on their structure and the response of the filter. Secondly, considering the variations in blood flow and morphology among aneurysms in DSA, intensity, texture, and blood perfusion features were extracted from the ruptured aneurysms and unruptured aneurysms. Subsequently, a sparse representation (SR) method was utilized to classify unruptured and ruptured aneurysms. RESULTS The experimental results for aneurysm detection showed that the F1-score was 94.1%. In the classification of ruptured and unruptured aneurysms, the accuracy, sensitivity, specificity, and area under curve (AUC) were 96.1%, 94.4%, 97.5%, and 0.982, respectively. CONCLUSION This paper presents a scheme combining an aneurysm detection filter and machine learning, offering a reliable solution for the diagnosis and prediction of aneurysm rupture.
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Affiliation(s)
- Tao Hu
- Department of Electronic Engineering, 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
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Irfan M, Malik KM, Ahmad J, Malik G. StrokeNet: An automated approach for segmentation and rupture risk prediction of intracranial aneurysm. Comput Med Imaging Graph 2023; 108:102271. [PMID: 37556901 DOI: 10.1016/j.compmedimag.2023.102271] [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: 03/08/2023] [Revised: 06/19/2023] [Accepted: 07/05/2023] [Indexed: 08/11/2023]
Abstract
Intracranial Aneurysms (IA) present a complex challenge for neurosurgeons as the risks associated with surgical intervention, such as Subarachnoid Hemorrhage (SAH) mortality and morbidity, may outweigh the benefits of aneurysmal occlusion in some cases. Hence, there is a critical need for developing techniques that assist physicians in assessing the risk of aneurysm rupture to determine which aneurysms require treatment. However, a reliable IA rupture risk prediction technique is currently unavailable. To address this issue, this study proposes a novel approach for aneurysm segmentation and multidisciplinary rupture prediction using 2D Digital Subtraction Angiography (DSA) images. The proposed method involves training a fully connected convolutional neural network (CNN) to segment aneurysm regions in DSA images, followed by extracting and fusing different features using a multidisciplinary approach, including deep features, geometrical features, Fourier descriptor, and shear pressure on the aneurysm wall. The proposed method also adopts a fast correlation-based filter approach to drop highly correlated features from the set of fused features. Finally, the selected fused features are passed through a Decision Tree classifier to predict the rupture severity of the associated aneurysm into four classes: Mild, Moderate, Severe, and Critical. The proposed method is evaluated on a newly developed DSA image dataset and on public datasets to assess its generalizability. The system's performance is also evaluated on DSA images annotated by expert neurosurgeons for the rupture risk assessment of the segmented aneurysm. The proposed system outperforms existing state-of-the-art segmentation methods, achieving an 85 % accuracy against annotated DSA images for the risk assessment of aneurysmal rupture.
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Affiliation(s)
- Muhammad Irfan
- SMILES LAB, Department of Computer Science and Engineering, Oakland University, Rochester, MI, 48309, USA
| | - Khalid Mahmood Malik
- SMILES LAB, Department of Computer Science and Engineering, Oakland University, Rochester, MI, 48309, USA.
| | - Jamil Ahmad
- Department of Computer Vision, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates
| | - Ghaus Malik
- Executive Vice-Chair at Department of Neurosurgery, Henry Ford Health System, Detroit, MI, USA
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Zhang J, Zhao Y, Liu X, Jiang J, Li Y. FSTIF-UNet: A Deep Learning-Based Method Towards Automatic Segmentation of Intracranial Aneurysms in Un-Reconstructed 3D-RA. IEEE J Biomed Health Inform 2023; 27:4028-4039. [PMID: 37216251 DOI: 10.1109/jbhi.2023.3278472] [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: 05/24/2023]
Abstract
Segmentation of intracranial aneurysms (IAs) is an important step for the diagnosis and treatment of IAs. However, the process by which clinicians manually recognize and localize IAs is overly labor intensive. This study aims to develop a deep-learning-based framework (defined as FSTIF-UNet) towards IAs segmentation in un-reconstructed 3D Rotational Angiography (3D-RA) images. 3D-RA sequences from 300 patients with IAs from Beijing Tiantan Hospital are enrolled. Inspired by radiologists' clincial skills, a Skip-Review attention mechanism is proposed to repeatedly fuse the long-term spatiotemporal features of several images with the most obvious IA's features (sellected by a pre-detection network). Then, a Conv-LSTM is used to fuse the short-term spatiotemporal features of the selected 15 3D-RA images from the equally-spaced viewing angles. The combination of the two modules realizes the full-scale spatiotemporal information fusion of the 3D-RA sequence. FSTIF-UNet achieves DSC, IoU, Sens, Haus, and F1-Score of 0.9109, 0.8586, 0.9314, 1.358 and 0.8883, respectively, and time taken for network segmentation is 0.89 s/case. The results show significant improvement in IA segmentation performance with FSTIF-UNet compared with baseline networks (with DSC from 0.8486 - 0.8794). The proposed FSTIF-UNet establishes a practical method to assist the radiologists in clinical diagnosis.
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Ham S, Seo J, Yun J, Bae YJ, Kim T, Sunwoo L, Yoo S, Jung SC, Kim JW, Kim N. Automated detection of intracranial aneurysms using skeleton-based 3D patches, semantic segmentation, and auxiliary classification for overcoming data imbalance in brain TOF-MRA. Sci Rep 2023; 13:12018. [PMID: 37491504 PMCID: PMC10368697 DOI: 10.1038/s41598-023-38586-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 07/11/2023] [Indexed: 07/27/2023] Open
Abstract
Accurate and reliable detection of intracranial aneurysms is vital for subsequent treatment to prevent bleeding. However, the detection of intracranial aneurysms can be time-consuming and even challenging, and there is great variability among experts, especially in the case of small aneurysms. This study aimed to detect intracranial aneurysms accurately using a convolutional neural network (CNN) with 3D time-of-flight magnetic resonance angiography (TOF-MRA). A total of 154 3D TOF-MRA datasets with intracranial aneurysms were acquired, and the gold standards were manually drawn by neuroradiologists. We also obtained 113 subjects from a public dataset for external validation. These angiograms were pre-processed by using skull-stripping, signal intensity normalization, and N4 bias correction. The 3D patches along the vessel skeleton from MRA were extracted. Values of the ratio between the aneurysmal and the normal patches ranged from 1:1 to 1:5. The semantic segmentation on intracranial aneurysms was trained using a 3D U-Net with an auxiliary classifier to overcome the imbalance in patches. The proposed method achieved an accuracy of 0.910 in internal validation and external validation accuracy of 0.883 with a 2:1 ratio of normal to aneurysmal patches. This multi-task learning method showed that the aneurysm segmentation performance was sufficient to be helpful in an actual clinical setting.
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Affiliation(s)
- Sungwon Ham
- Healthcare Readiness Institute for Unified Korea, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan City, Gyeonggi-do, 15355, Republic of Korea
| | - Jiyeon Seo
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Jihye Yun
- Department of Radiology, University of Ulsan College of Medicine & Asan Medical Center, Seoul, Republic of Korea
| | - Yun Jung Bae
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Tackeun Kim
- Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Leonard Sunwoo
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Sooyoung Yoo
- Healthcare ICT Research Center, Office of eHealth Research and Businesses, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Seung Chai Jung
- Department of Radiology, University of Ulsan College of Medicine & Asan Medical Center, Seoul, Republic of Korea
| | - Jeong-Whun Kim
- Department of Otorhinolaryngology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
| | - Namkug Kim
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
- Department of Radiology, University of Ulsan College of Medicine & Asan Medical Center, Seoul, Republic of Korea.
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Zhao Y, Wang S, Zhang Y, Qiao S, Zhang M. WRANet: wavelet integrated residual attention U-Net network for medical image segmentation. COMPLEX INTELL SYST 2023:1-13. [PMID: 37361970 PMCID: PMC10248349 DOI: 10.1007/s40747-023-01119-y] [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: 01/09/2023] [Accepted: 05/16/2023] [Indexed: 06/28/2023]
Abstract
Medical image segmentation is crucial for the diagnosis and analysis of disease. Deep convolutional neural network methods have achieved great success in medical image segmentation. However, they are highly susceptible to noise interference during the propagation of the network, where weak noise can dramatically alter the network output. As the network deepens, it can face problems such as gradient explosion and vanishing. To improve the robustness and segmentation performance of the network, we propose a wavelet residual attention network (WRANet) for medical image segmentation. We replace the standard downsampling modules (e.g., maximum pooling and average pooling) in CNNs with discrete wavelet transform, decompose the features into low- and high-frequency components, and remove the high-frequency components to eliminate noise. At the same time, the problem of feature loss can be effectively addressed by introducing an attention mechanism. The combined experimental results show that our method can effectively perform aneurysm segmentation, achieving a Dice score of 78.99%, an IoU score of 68.96%, a precision of 85.21%, and a sensitivity score of 80.98%. In polyp segmentation, a Dice score of 88.89%, an IoU score of 81.74%, a precision rate of 91.32%, and a sensitivity score of 91.07% were achieved. Furthermore, our comparison with state-of-the-art techniques demonstrates the competitiveness of the WRANet network.
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Affiliation(s)
- Yawu Zhao
- School of Computer Science and Technology, China University of Petroleum, Qingdao, Shandong China
| | - Shudong Wang
- School of Computer Science and Technology, China University of Petroleum, Qingdao, Shandong China
| | - Yulin Zhang
- College of Mathematics and System Science, Shandong University of Science and Technology, Qingdao, Shandong China
| | - Sibo Qiao
- School of Computer Science and Technology, China University of Petroleum, Qingdao, Shandong China
| | - Mufei Zhang
- Inspur Cloud Information Technology Co, Inspur, Jinan, Shandong China
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Improving the diagnostic performance of computed tomography angiography for intracranial large arterial stenosis by a novel super-resolution algorithm based on multi-scale residual denoising generative adversarial network. Clin Imaging 2023; 96:1-8. [PMID: 36731372 DOI: 10.1016/j.clinimag.2023.01.009] [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: 08/24/2022] [Revised: 01/12/2023] [Accepted: 01/18/2023] [Indexed: 01/30/2023]
Abstract
BACKGROUND Computed tomography angiography (CTA) is very popular because it is characterized by rapidity and accessibility. However, CTA is inferior to digital subtraction angiography (DSA) in the diagnosis of intracranial artery stenosis or occlusion. DSA is an invasive examination, so we optimized the quality of cephalic CTA images. METHODS We used 5000 CTA images to train multi-scale residual denoising generative adversarial network (MRDGAN). And then 71 CTA images with intracranial large arterial stenosis were treated by Super-Resolution based on Generative Adversarial Network (SRGAN), Enhanced Super-Resolution based on Generative Adversarial Network (ESRGAN) and post-trained MRDGAN, respectively. Peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) of the SRGAN, ESRGAN, MRDGAN and original CTA images were measured respectively. The qualities of MRDGAN and original images were visually assessed using a 4-point scale. The diagnostic coherence of digital subtraction angiography (DSA) with MRDGAN and original images was analyzed. RESULTS The PSNR was significantly higher in the MRDGAN CTA images (35.96 ± 1.51) than in the original (31.51 ± 1.43), SRGAN (25.75 ± 1.18) and ESRGAN (30.36 ± 1.05) CTA images (all P < 0.001). The SSIM was significantly higher in the MRDGAN CTA images (0.95 ± 0.02) than in the SRGAN (0.88 ± 0.03) and ESRGAN (0.90 ± 0.02) CTA images (all P < 0.01). The visual assessment was significantly higher in the MRDGAN CTA images (3.52 ± 0.58) than in the original CTA images (2.39 ± 0.69) (P < 0.05). The diagnostic coherence between MRDGAN and DSA (κ = 0.89) was superior to that between original images and DSA (κ = 0.62). CONCLUSION Our MRDGAN can effectively optimize original CTA images and improve its clinical diagnostic value for intracranial large artery stenosis.
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18
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Din M, Agarwal S, Grzeda M, Wood DA, Modat M, Booth TC. Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis. J Neurointerv Surg 2023; 15:262-271. [PMID: 36375834 PMCID: PMC9985742 DOI: 10.1136/jnis-2022-019456] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 10/11/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND Subarachnoid hemorrhage from cerebral aneurysm rupture is a major cause of morbidity and mortality. Early aneurysm identification, aided by automated systems, may improve patient outcomes. Therefore, a systematic review and meta-analysis of the diagnostic accuracy of artificial intelligence (AI) algorithms in detecting cerebral aneurysms using CT, MRI or DSA was performed. METHODS MEDLINE, Embase, Cochrane Library and Web of Science were searched until August 2021. Eligibility criteria included studies using fully automated algorithms to detect cerebral aneurysms using MRI, CT or DSA. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis: Diagnostic Test Accuracy (PRISMA-DTA), articles were assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Meta-analysis included a bivariate random-effect model to determine pooled sensitivity, specificity, and area under the receiver operator characteristic curve (ROC-AUC). PROSPERO CRD42021278454. RESULTS 43 studies were included, and 41/43 (95%) were retrospective. 34/43 (79%) used AI as a standalone tool, while 9/43 (21%) used AI assisting a reader. 23/43 (53%) used deep learning. Most studies had high bias risk and applicability concerns, limiting conclusions. Six studies in the standalone AI meta-analysis gave (pooled) 91.2% (95% CI 82.2% to 95.8%) sensitivity; 16.5% (95% CI 9.4% to 27.1%) false-positive rate (1-specificity); 0.936 ROC-AUC. Five reader-assistive AI studies gave (pooled) 90.3% (95% CI 88.0% - 92.2%) sensitivity; 7.9% (95% CI 3.5% to 16.8%) false-positive rate; 0.910 ROC-AUC. CONCLUSION AI has the potential to support clinicians in detecting cerebral aneurysms. Interpretation is limited due to high risk of bias and poor generalizability. Multicenter, prospective studies are required to assess AI in clinical practice.
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Affiliation(s)
- Munaib Din
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Siddharth Agarwal
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Mariusz Grzeda
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - David A Wood
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, King's College Hospital NHS Foundation Trust, London, UK
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Choi D, Kim T, Jang J, Sunwoo L, Lee KJ. Intracranial steno-occlusive lesion detection on time-of-flight MR angiography using multi-task learning. Comput Med Imaging Graph 2023; 107:102220. [PMID: 37023509 DOI: 10.1016/j.compmedimag.2023.102220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 03/04/2023] [Accepted: 03/19/2023] [Indexed: 03/28/2023]
Abstract
Steno-occlusive lesions in intracranial arteries refer to segments of narrowed or occluded blood vessels that increase the risk of ischemic strokes. Steno-occlusive lesion detection is crucial in clinical settings; however, automatic detection methods have hardly been studied. Therefore, we propose a novel automatic method to detect steno-occlusive lesions in sequential transverse slices on time-of-flight magnetic resonance angiography. Our method simultaneously detects lesions while segmenting blood vessels based on end-to-end multi-task learning, reflecting that the lesions are closely related to the connectivity of blood vessels. We design classification and localization modules that can be attached to arbitrary segmentation network. As blood vessels are segmented, both modules simultaneously predict the presence and location of lesions for each transverse slice. By combining outputs from the two modules, we devise a simple operation that boosts the performance of lesion localization. Experimental results show that lesion prediction and localization performance is improved by incorporating blood vessel extraction. Our ablation study demonstrates that the proposed operation enhances lesion localization accuracy. We also verify the effectiveness of multi-task learning by comparing our approach with those that individually detect lesions with extracted blood vessels.
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Liu X, Mao J, Sun N, Yu X, Chai L, Tian Y, Wang J, Liang J, Tao H, Yuan L, Lu J, Wang Y, Zhang B, Wu K, Wang Y, Chen M, Wang Z, Lu L. Deep Learning for Detection of Intracranial Aneurysms from Computed Tomography Angiography Images. J Digit Imaging 2023; 36:114-123. [PMID: 36085330 PMCID: PMC9984635 DOI: 10.1007/s10278-022-00698-5] [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: 09/26/2021] [Revised: 07/31/2022] [Accepted: 08/23/2022] [Indexed: 10/14/2022] Open
Abstract
The accuracy of computed tomography angiography (CTA) image interpretation depends on the radiologist. This study aims to develop a new method for automatically detecting intracranial aneurysms from CTA images using deep learning, based on a convolutional neural network (CNN) implemented on the DeepMedic platform. Ninety CTA scans of patients with intracranial aneurysms are collected and divided into two datasets: training (80 subjects) and test (10 subjects) datasets. Subsequently, a deep learning architecture with a three-dimensional (3D) CNN model is implemented on the DeepMedic platform for the automatic segmentation and detection of intracranial aneurysms from the CTA images. The samples in the training dataset are used to train the CNN model, and those in the test dataset are used to assess the performance of the established system. Sensitivity, positive predictive value (PPV), and false positives are evaluated. The overall sensitivity and PPV of this system for detecting intracranial aneurysms from CTA images are 92.3% and 100%, respectively, and the segmentation sensitivity is 92.3%. The performance of the system in the detection of intracranial aneurysms is closely related to their size. The detection sensitivity for small intracranial aneurysms (≤ 3 mm) is 66.7%, whereas the sensitivity of detection for large (> 10 mm) and medium-sized (3-10 mm) intracranial aneurysms is 100%. The deep learning architecture with a 3D CNN model on the DeepMedic platform can reliably segment and detect intracranial aneurysms from CTA images with high sensitivity.
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Affiliation(s)
- Xiujuan Liu
- Department of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Kangning Road, Xiangzhou District, Zhuhai, Guangdong, 519000, China
| | - Jun Mao
- Department of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Kangning Road, Xiangzhou District, Zhuhai, Guangdong, 519000, China
| | - Ning Sun
- Engineering Research Center of Wideband Wireless Communication Technology, Ministry of Education, Nanjing University of Posts and Telecommunications, Jiangsu, Nanjing, 210000, China
| | - Xiangrong Yu
- Department of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Kangning Road, Xiangzhou District, Zhuhai, Guangdong, 519000, China
| | - Lei Chai
- Engineering Research Center of Wideband Wireless Communication Technology, Ministry of Education, Nanjing University of Posts and Telecommunications, Jiangsu, Nanjing, 210000, China
| | - Ye Tian
- Department of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Kangning Road, Xiangzhou District, Zhuhai, Guangdong, 519000, China
| | - Jianming Wang
- Department of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Kangning Road, Xiangzhou District, Zhuhai, Guangdong, 519000, China
| | - Jianchao Liang
- Department of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Kangning Road, Xiangzhou District, Zhuhai, Guangdong, 519000, China
| | - Haiquan Tao
- Department of Neurosurgery, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University) Zhuhai, Guangdong, 519000, China
| | - Lihua Yuan
- Department of Radiology, The Affiliated Drum Tower Hospital, Nanjing University Medical School, Nanjing, 210000, Jiangsu, China
| | - Jiaming Lu
- Department of Radiology, The Affiliated Drum Tower Hospital, Nanjing University Medical School, Nanjing, 210000, Jiangsu, China
| | - Yang Wang
- Department of Radiology, The Affiliated Drum Tower Hospital, Nanjing University Medical School, Nanjing, 210000, Jiangsu, China
| | - Bing Zhang
- Department of Radiology, The Affiliated Drum Tower Hospital, Nanjing University Medical School, Nanjing, 210000, Jiangsu, China
| | - Kaihua Wu
- Department of Neurosurgery, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University) Zhuhai, Guangdong, 519000, China
| | - Yiding Wang
- CT Room, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China
| | - Mengjiao Chen
- CT Room, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China
| | - Zhishun Wang
- Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, NY, 10032, USA.
| | - Ligong Lu
- Department of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Kangning Road, Xiangzhou District, Zhuhai, Guangdong, 519000, China.
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Zhang Z, Wang Y, Zhou S, Li Z, Peng Y, Gao S, Zhu G, Wu F, Wu B. The automatic evaluation of steno-occlusive changes in time-of-flight magnetic resonance angiography of moyamoya patients using a 3D coordinate attention residual network. Quant Imaging Med Surg 2023; 13:1009-1022. [PMID: 36819290 PMCID: PMC9929428 DOI: 10.21037/qims-22-799] [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: 08/01/2022] [Accepted: 11/21/2022] [Indexed: 12/15/2022]
Abstract
Background Moyamoya disease (MMD) is a rare cerebrovascular occlusive disease with progressive stenosis of the terminal portion of internal cerebral artery (ICA) and its main branches, which can cause complications, such as high risks of disability and increased mortality. Accurate and timely diagnosis may be difficult for physicians who are unfamiliar to MMD. Therefore, this study aims to achieve a preoperative deep-learning-based evaluation of MMD by detecting steno-occlusive changes in the middle cerebral artery or distal ICA areas. Methods A fine-tuned deep learning model was developed using a three-dimensional (3D) coordinate attention residual network (3D CA-ResNet). This study enrolled 50 preoperative patients with MMD and 50 controls, and the corresponding time of flight magnetic resonance angiography (TOF-MRA) imaging data were acquired. The 3D CA-ResNet was trained based on sub-volumes and tested using patch-based and subject-based methods. The performance of the 3D CA-ResNet, as evaluated by the area under the curve (AUC) of receiving-operator characteristic, was compared with that of three other conventional 3D networks. Results With the resulting network, the patch-based test achieved an AUC value of 0.94 for the 3D CA-ResNet in 480 patches from 10 test patients and 10 test controls, which is significantly higher than the results of the others. The 3D CA-ResNet correctly classified the MMD patients and normal healthy controls, and the vascular lesion distribution in subjects with the disease was investigated by generating a stenosis probability map and 3D vascular structure segmentation. Conclusions The results demonstrated the reliability of the proposed 3D CA-ResNet in detecting stenotic areas on TOF-MRA imaging, and it outperformed three other models in identifying vascular steno-occlusive changes in patients with MMD.
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Affiliation(s)
- Zeru Zhang
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China;,The School of Health Humanities, Peking University, Beijing, China
| | - Yituo Wang
- Department of Radiology, Seventh Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Shuai Zhou
- Department of Radiology, Shijiazhuang People’s Hospital, Shijiazhuang, China
| | - Zhaotong Li
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China;,The School of Health Humanities, Peking University, Beijing, China
| | - Ying Peng
- Department of Radiology, Seventh Medical Center of Chinese PLA General Hospital, Beijing, China;,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Song Gao
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Guangming Zhu
- Department of Neurology, College of Medicine, University of Arizona, Tucson, Arizona, USA
| | - Fengliang Wu
- Beijing Key Laboratory of Spinal Disease Research, Engineering Research Center of Bone and Joint Precision Medicine, Department of Orthopedics, Peking University Third Hospital, Beijing, China
| | - Bing Wu
- Department of Radiology, Seventh Medical Center of Chinese PLA General Hospital, Beijing, China;,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
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22
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Claux F, Baudouin M, Bogey C, Rouchaud A. Dense, deep learning-based intracranial aneurysm detection on TOF MRI using two-stage regularized U-Net. J Neuroradiol 2023; 50:9-15. [PMID: 35307554 DOI: 10.1016/j.neurad.2022.03.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 03/11/2022] [Accepted: 03/11/2022] [Indexed: 01/31/2023]
Abstract
BACKGROUND AND PURPOSE The prevalence of unruptured intracranial aneurysms in the general population is high and aneurysms are usually asymptomatic. Their diagnosis is often fortuitous on MRI and might be difficult and time consuming for the radiologist. The purpose of this study was to develop a deep learning neural network tool for automated segmentation of intracranial arteries and automated detection of intracranial aneurysms from 3D time-of-flight magnetic resonance angiography (TOF-MRA). MATERIALS AND METHODS 3D TOF-MRA with aneurysms were retrospectively extracted. All were confirmed with angiography. The data were divided into two sets: a training set of 24 examinations and a test set of 25 examinations. Manual annotations of intracranial blood vessels and aneurysms were performed by neuroradiologists. A double convolutional neuronal network based on the U-Net architecture with regularization was used to increase performance despite a small amount of training data. The performance was evaluated for the test set. Subgroup analyses according to size and location of aneurysms were performed. RESULTS The average processing time was 15 min. Overall, the sensitivity and the positive predictive value of the proposed algorithm were 78% (21 of 27; 95% CI: 62-94) and 62% (21 of 34; 95%CI: 46-78) respectively, with 0.5 FP/case. Despite gradual improvement in sensitivity regarding aneurysm size, there was no significant difference of sensitivity detection between subgroups of size and location. CONCLUSIONS This developed tool based on a double CNN with regularization trained with small dataset, enables accurate intracranial arteries segmentation as well as effective aneurysm detection on 3D TOF MRA.
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Affiliation(s)
- Frédéric Claux
- Univ. Limoges, CNRS, XLIM, UMR 7252, F-87000 Limoges, France.
| | - Maxime Baudouin
- Limoges university hospital, Department of radiology, Limoges, France.
| | - Clément Bogey
- Limoges university hospital, Department of radiology, Limoges, France
| | - Aymeric Rouchaud
- Univ. Limoges, CNRS, XLIM, UMR 7252, F-87000 Limoges, France; Limoges university hospital, Department of radiology, Limoges, France
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23
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Chen G, Yifang B, Jiajun Z, Dongdong W, Zhiyong Z, Ruoyu D, Bin D, Sirong P, Daoying G, Meng C, Yakang D, Yuxin L. Automated unruptured cerebral aneurysms detection in TOF MR angiography images using dual-channel SE-3D UNet: a multi-center research. Eur Radiol 2023; 33:3532-3543. [PMID: 36725720 DOI: 10.1007/s00330-022-09385-z] [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: 05/23/2022] [Revised: 11/29/2022] [Accepted: 12/18/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVES Time of flight magnetic resonance angiography (TOF-MRA) is the primary non-invasive screening method for cerebral aneurysms. We aimed to develop a computer-aided aneurysm detection method to improve the diagnostic efficiency and accuracy, especially decrease the false positive rate. METHODS This is a retrospective multicenter study. The dataset contained 1160 TOF-MRA examinations composed of unruptured aneurysms (n = 1096) and normal controls (n = 166) from six hospitals. A total of 1037 examinations acquired from 2013 to 2019 were used as training set; 123 examinations acquired from 2020 to 2021 were used as external test set. We proposed an equalized augmentation strategy based on aneurysm location and constructed a detection model based on dual channel SE-3D UNet. The model was trained with a 5-fold cross-validation in the training set, then tested on the external test set. RESULTS The proposed method achieved 82.46% sensitivity on patient-level, 73.85% sensitivity on lesion-level, and 0.88 false positives per case in the external test set. The performance did not show significant differences in subgroups according to the aneurysm site (except ACA), aneurysm size (except smaller than 3 mm), or MRI scanners. The performance preceded the basic SE-3D UNet by increasing 15.79% patient-level sensitivity and decreasing 4.19 FPs/case. CONCLUSIONS The proposed automated aneurysm detection method achieved acceptable sensitivity while controlling fairly low false positives per case. It might provide a useful auxiliary tool of cerebral aneurysms MRA screening. KEY POINTS • The need for automated cerebral aneurysms detecting is growing. • The strategy of equalized augmentation based on aneurysm location and dual-channel input could improve the model performance. • The retrospective multi-center study showed that the proposed automated cerebral aneurysms detection using dual-channel SE-3D UNet could achieve acceptable sensitivity while controlling a low false positive rate.
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Affiliation(s)
- Geng Chen
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- Jinan Guoke Medical Technology Development Co. Ltd., Jinan, China
| | - Bao Yifang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Zhang Jiajun
- School of Electronic & Information Engineering, Suzhou University of Science and Technology, Suzhou, China
| | - Wang Dongdong
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Zhou Zhiyong
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Di Ruoyu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Dai Bin
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Piao Sirong
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Geng Daoying
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Chen Meng
- School of Biomedical Engineering, Xuzhou Medical University, Xuzhou, China
| | - Dai Yakang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
- Jinan Guoke Medical Technology Development Co. Ltd., Jinan, China.
| | - Li Yuxin
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.
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24
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Detection of intracranial aneurysms using deep learning-based CAD system: usefulness of the scores of CNN's final layer for distinguishing between aneurysm and infundibular dilatation. Jpn J Radiol 2023; 41:131-141. [PMID: 36173510 PMCID: PMC9889446 DOI: 10.1007/s11604-022-01341-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 09/12/2022] [Indexed: 02/04/2023]
Abstract
PURPOSE We evaluated the diagnostic performance of a clinically available deep learning-based computer-assisted diagnosis software for detecting unruptured aneurysms (UANs) using magnetic resonance angiography and assessed the functionality of the convolutional neural network (CNN) final layer score for distinguishing between UAN and infundibular dilatation (ID). MATERIALS AND METHODS EIRL brain aneurysm (EIRL_BA) was used in this study. The subjects were 117 UAN and/or ID cases including 100 UAN lesions (average sizes of 2.56 ± 1.45 mm) and 40 ID lesions (average sizes of 1.75 ± 0.41 mm) in any of internal carotid artery, middle cerebral artery, and anterior communicating artery, and 123 normal controls. The sensitivity, specificity, and accuracy of EIRL_BA were determined for UAN and ID or UAN only. Furthermore, the relationship between the lesion category and score was examined using a linear regression analysis model, and the receiver operating characteristic (ROC) analysis was used to assess whether the scores represent UAN-like characteristics. RESULTS EIRL_BA showed a total of 203 candidates (an average of 1.73/case) in UAN and/or ID cases and 98 candidates (an average of 0.80/case) in normal controls. For diagnosing either UAN/ID, EIRL_BA showed an overall sensitivity of 80%, specificity of 84.2%, and accuracy of 83.7%, resulting in the positive likelihood ratio of 5.0. For diagnosing UAN only, the overall sensitivity of 89.0, specificity of 82.6%, and accuracy of 83.2% resulting in the positive likelihood ratio of 5.1. In a linear regression analysis, the scores significantly increased in the candidates' first and second ranks in UAN (p < 0.05) but not in ID. An ROC analysis using the score for diagnosing UAN showed an area under the curve of 0.836. CONCLUSION EIRL_BA is applicable for detecting small UAN, and the CNN's final layer scores may be an effective index for discriminating UAN and ID and representing the likelihood of UAN.
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25
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Towards Automated Brain Aneurysm Detection in TOF-MRA: Open Data, Weak Labels, and Anatomical Knowledge. Neuroinformatics 2023; 21:21-34. [PMID: 35982364 PMCID: PMC9931814 DOI: 10.1007/s12021-022-09597-0] [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] [Accepted: 08/01/2022] [Indexed: 10/15/2022]
Abstract
Brain aneurysm detection in Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) has undergone drastic improvements with the advent of Deep Learning (DL). However, performances of supervised DL models heavily rely on the quantity of labeled samples, which are extremely costly to obtain. Here, we present a DL model for aneurysm detection that overcomes the issue with "weak" labels: oversized annotations which are considerably faster to create. Our weak labels resulted to be four times faster to generate than their voxel-wise counterparts. In addition, our model leverages prior anatomical knowledge by focusing only on plausible locations for aneurysm occurrence. We first train and evaluate our model through cross-validation on an in-house TOF-MRA dataset comprising 284 subjects (170 females / 127 healthy controls / 157 patients with 198 aneurysms). On this dataset, our best model achieved a sensitivity of 83%, with False Positive (FP) rate of 0.8 per patient. To assess model generalizability, we then participated in a challenge for aneurysm detection with TOF-MRA data (93 patients, 20 controls, 125 aneurysms). On the public challenge, sensitivity was 68% (FP rate = 2.5), ranking 4th/18 on the open leaderboard. We found no significant difference in sensitivity between aneurysm risk-of-rupture groups (p = 0.75), locations (p = 0.72), or sizes (p = 0.15). Data, code and model weights are released under permissive licenses. We demonstrate that weak labels and anatomical knowledge can alleviate the necessity for prohibitively expensive voxel-wise annotations.
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26
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Zhu G, Luo X, Yang T, Cai L, Yeo JH, Yan G, Yang J. Deep learning-based recognition and segmentation of intracranial aneurysms under small sample size. Front Physiol 2022; 13:1084202. [PMID: 36601346 PMCID: PMC9806214 DOI: 10.3389/fphys.2022.1084202] [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: 10/30/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
The manual identification and segmentation of intracranial aneurysms (IAs) involved in the 3D reconstruction procedure are labor-intensive and prone to human errors. To meet the demands for routine clinical management and large cohort studies of IAs, fast and accurate patient-specific IA reconstruction becomes a research Frontier. In this study, a deep-learning-based framework for IA identification and segmentation was developed, and the impacts of image pre-processing and convolutional neural network (CNN) architectures on the framework's performance were investigated. Three-dimensional (3D) segmentation-dedicated architectures, including 3D UNet, VNet, and 3D Res-UNet were evaluated. The dataset used in this study included 101 sets of anonymized cranial computed tomography angiography (CTA) images with 140 IA cases. After the labeling and image pre-processing, a training set and test set containing 112 and 28 IA lesions were used to train and evaluate the convolutional neural network mentioned above. The performances of three convolutional neural networks were compared in terms of training performance, segmentation performance, and segmentation efficiency using multiple quantitative metrics. All the convolutional neural networks showed a non-zero voxel-wise recall (V-Recall) at the case level. Among them, 3D UNet exhibited a better overall segmentation performance under the relatively small sample size. The automatic segmentation results based on 3D UNet reached an average V-Recall of 0.797 ± 0.140 (3.5% and 17.3% higher than that of VNet and 3D Res-UNet), as well as an average dice similarity coefficient (DSC) of 0.818 ± 0.100, which was 4.1%, and 11.7% higher than VNet and 3D Res-UNet. Moreover, the average Hausdorff distance (HD) of the 3D UNet was 3.323 ± 3.212 voxels, which was 8.3% and 17.3% lower than that of VNet and 3D Res-UNet. The three-dimensional deviation analysis results also showed that the segmentations of 3D UNet had the smallest deviation with a max distance of +1.4760/-2.3854 mm, an average distance of 0.3480 mm, a standard deviation (STD) of 0.5978 mm, a root mean square (RMS) of 0.7269 mm. In addition, the average segmentation time (AST) of the 3D UNet was 0.053s, equal to that of 3D Res-UNet and 8.62% shorter than VNet. The results from this study suggested that the proposed deep learning framework integrated with 3D UNet can provide fast and accurate IA identification and segmentation.
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Affiliation(s)
- Guangyu Zhu
- School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an, China,*Correspondence: Guangyu Zhu, ; Jian Yang,
| | - Xueqi Luo
- School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Tingting Yang
- School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Li Cai
- Xi’an Key Laboratory of Scientific Computation and Applied Statistics, Xi’an, China,School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, China
| | - Joon Hock Yeo
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
| | - Ge Yan
- Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Jian Yang
- Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China,*Correspondence: Guangyu Zhu, ; Jian Yang,
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27
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Qiu J, Tan G, Lin Y, Guan J, Dai Z, Wang F, Zhuang C, Wilman AH, Huang H, Cao Z, Tang Y, Jia Y, Li Y, Zhou T, Wu R. Automated detection of intracranial artery stenosis and occlusion in magnetic resonance angiography: A preliminary study based on deep learning. Magn Reson Imaging 2022; 94:105-111. [PMID: 36174873 DOI: 10.1016/j.mri.2022.09.006] [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: 04/05/2022] [Revised: 09/12/2022] [Accepted: 09/22/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND AND OBJECTIVES Intracranial atherosclerotic stenosis of a major intracranial artery is the common cause of ischemic stroke. We evaluate the feasibility of using deep learning to automatically detect intracranial arterial steno-occlusive lesions from time-of-flight magnetic resonance angiography. METHODS In a retrospective study, magnetic resonance images with radiological reports of intracranial arterial stenosis and occlusion were extracted. The images were randomly divided into a training set and a test set. The manual annotation of lesions with a bounding box labeled "moderate stenosis," "severe stenosis," "occlusion," and "absence of signal" was considered as ground truth. A deep learning algorithm based on you only look once version 5 (YOLOv5) detection model was developed with the training set, and its sensitivity and positive predictive values to detect lesions were evaluated in the test set. RESULTS A dataset of 200 examinations consisted of a total of 411 lesions-242 moderate stenoses, 84 severe stenoses, 70 occlusions, and 15 absence of signal. The magnetic resonance images contained 291 lesions in the training set and 120 lesions in the test set. The sensitivity and positive predictive values were 64.2 and 83.7%, respectively. The detection sensitivity in relation to the location was greatest in the internal carotid artery (86.2%). CONCLUSIONS Applying deep learning algorithms in the automated detection of intracranial arterial steno-occlusive lesions from time-of-flight magnetic resonance angiography is feasible and has great potential.
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Affiliation(s)
- Jinming Qiu
- Department of Radiology, the Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong, PR China; Department of Radiology, the Sixth Affiliated Hospital, South China University of Technology, Foshan 528000, Guangdong, PR China
| | - Guanru Tan
- Department of Computer Science, Shantou University, Shantou 515041, Guangdong, PR China
| | - Yan Lin
- Department of Radiology, the Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong, PR China
| | - Jitian Guan
- Department of Radiology, the Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong, PR China
| | - Zhuozhi Dai
- Department of Radiology, the Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong, PR China
| | - Fei Wang
- Department of Computer Science, Shantou University, Shantou 515041, Guangdong, PR China
| | - Caiyu Zhuang
- Department of Radiology, the Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong, PR China
| | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Huaidong Huang
- Department of Radiology, the Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong, PR China
| | - Zhen Cao
- Department of Radiology, the Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong, PR China
| | - Yanyan Tang
- Department of Medical Imaging, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, Guangdong, PR China
| | - Yanlong Jia
- Department of Radiology, the Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong, PR China
| | - Yan Li
- Department of Radiology, the Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong, PR China
| | - Teng Zhou
- Department of Computer Science, Shantou University, Shantou 515041, Guangdong, PR China
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Shantou 515800, China
| | - Renhua Wu
- Department of Radiology, the Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong, PR China
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28
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Lehnen NC, Haase R, Schmeel FC, Vatter H, Dorn F, Radbruch A, Paech D. Automated Detection of Cerebral Aneurysms on TOF-MRA Using a Deep Learning Approach: An External Validation Study. AJNR Am J Neuroradiol 2022; 43:1700-1705. [PMID: 36357154 DOI: 10.3174/ajnr.a7695] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 10/05/2022] [Indexed: 11/12/2022]
Abstract
BACKGROUND AND PURPOSE Cerebral aneurysms yield the risk of rupture, severe disability and death. Thus, early detection of cerebral aneurysms is crucial to ensure timely treatment, if necessary. AI-based software tools are expected to enhance radiologists' performance in detecting pathologies like cerebral aneurysms in the future. Our aim was to evaluate the diagnostic performance of an artificial intelligence-based software designed to detect intracranial aneurysms on TOF-MRA. MATERIALS AND METHODS One hundred ninety-one MR imaging data sets were analyzed using the software mdbrain for the presence of intracranial aneurysms on TOF-MRA obtained using two 3T MR imaging scanners or a 1.5T MR imaging scanner according to our clinical standard protocol. The results were compared with the reading of an experienced radiologist as a criterion standard to measure the sensitivity, specificity, positive and negative predictive values, and accuracy of the software. Additionally, detection rates depending on size, morphology, and location of the aneurysms were evaluated. RESULTS Fifty-four aneurysms were detected by the expert reader. The overall sensitivity of the software for the detection of cerebral aneurysms was 72.6%, the specificity was 87.2%, and the accuracy was 82.6%. The positive predictive value was 67.9%, and the negative predictive value was 88.5%. We observed a sensitivity of 100% for saccular aneurysms of >5 mm without signs of thrombosis and low detection rates for fusiform or thrombosed aneurysms of 33.3% and 16.7%, respectively. Of 8 aneurysms that were not included in the initial written reports but were detected by the expert reader, retrospectively, 4 were detected by the software. CONCLUSIONS Our data suggest that the software can assist radiologists in reporting TOF-MRA. The software was highly reliable in detecting saccular aneurysms, while for fusiform or thrombosed aneurysms, further improvements are needed. Further studies are necessary to investigate the impact of the software on detection rates, interrater reliability, and reading times.
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Affiliation(s)
- N C Lehnen
- From the Departments of Neuroradiology (N.C.L., R.H., F.C.S., F.D., A.R., D.P.)
| | - R Haase
- From the Departments of Neuroradiology (N.C.L., R.H., F.C.S., F.D., A.R., D.P.)
| | - F C Schmeel
- From the Departments of Neuroradiology (N.C.L., R.H., F.C.S., F.D., A.R., D.P.)
| | - H Vatter
- Neurosurgery (H.V.), University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - F Dorn
- From the Departments of Neuroradiology (N.C.L., R.H., F.C.S., F.D., A.R., D.P.)
| | - A Radbruch
- From the Departments of Neuroradiology (N.C.L., R.H., F.C.S., F.D., A.R., D.P.)
| | - D Paech
- From the Departments of Neuroradiology (N.C.L., R.H., F.C.S., F.D., A.R., D.P.)
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29
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Ou C, Qian Y, Chong W, Hou X, Zhang M, Zhang X, Si W, Duan CZ. A deep learning-based automatic system for intracranial aneurysms diagnosis on three-dimensional digital subtraction angiographic images. Med Phys 2022; 49:7038-7053. [PMID: 35792717 DOI: 10.1002/mp.15846] [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: 03/21/2022] [Revised: 06/11/2022] [Accepted: 06/27/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Intracranial aneurysms (IAs) are a life-threatening disease. Their rupture can lead to hemorrhagic stroke. Most studies applying deep learning for the detection of aneurysms are based on angiographic images. However, critical diagnostic information such as morphology and aneurysm location are not captured by deep learning algorithms and still require manual assessments. PURPOSE Digital subtraction angiography (DSA) is the gold standard for aneurysm diagnosis. To facilitate the fully automatic diagnosis of aneurysms, we proposed a comprehensive system for the detection, morphology measurement, and location classification of aneurysms on three-dimensional DSA images, allowing automatic diagnosis without further human input. METHODS The system comprised three neural networks: a network for aneurysm detection, a network for morphology measurement, and a network for aneurysm location identification. A cross-scale dual-path transformer module was proposed to effectively fuse local and global information to capture aneurysms of varying sizes. A multitask learning approach was also proposed to allow an accurate localization of aneurysm neck for morphology measurement. RESULTS The cross-scale dual-path transformer module was shown to outperform other state-of-the-art network architectures, improving segmentation, and classification accuracy. The detection network in our system achieved an F2 score of 0.946 (recall 93%, precision 100%), better than the winning team in the Cerebral Aneurysm Detection and Analysis challenge. The measurement network achieved a relative error of less than 10% for morphology measurement, at the same level as human operators. Perfect accuracy (100%) was achieved on aneurysm location classification. CONCLUSIONS We have demonstrated that a comprehensive system can automatically detect, measure morphology and report the aneurysm location of aneurysms without human intervention. This can be a potential tool for the diagnosis of IAs, improving radiologists' performance and reducing their workload.
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Affiliation(s)
- Chubin Ou
- Neurosurgery Center, Department of Cerebrovascular Surgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China.,Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Yi Qian
- Neurosurgery Center, Department of Cerebrovascular Surgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | | | - Xiaoxi Hou
- Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Mingzi Zhang
- Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Xin Zhang
- Neurosurgery Center, Department of Cerebrovascular Surgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Weixin Si
- Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Chuan-Zhi Duan
- Neurosurgery Center, Department of Cerebrovascular Surgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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30
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Chen M, Geng C, Wang D, Zhou Z, Di R, Li F, Piao S, Zhang J, Li Y, Dai Y. A coarse-to-fine cascade deep learning neural network for segmenting cerebral aneurysms in time-of-flight magnetic resonance angiography. Biomed Eng Online 2022; 21:71. [PMID: 36163014 PMCID: PMC9513890 DOI: 10.1186/s12938-022-01041-3] [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: 04/24/2022] [Accepted: 09/16/2022] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Accurate segmentation of unruptured cerebral aneurysms (UCAs) is essential to treatment planning and rupture risk assessment. Currently, three-dimensional time-of-flight magnetic resonance angiography (3D TOF-MRA) has been the most commonly used method for screening aneurysms due to its noninvasiveness. The methods based on deep learning technologies can assist radiologists in achieving accurate and reliable analysis of the size and shape of aneurysms, which may be helpful in rupture risk prediction models. However, the existing methods did not accomplish accurate segmentation of cerebral aneurysms in 3D TOF-MRA. METHODS This paper proposed a CCDU-Net for segmenting UCAs of 3D TOF-MRA images. The CCDU-Net was a cascade of a convolutional neural network for coarse segmentation and the proposed DU-Net for fine segmentation. Especially, the dual-channel inputs of DU-Net were composed of the vessel image and its contour image which can augment the vascular morphological information. Furthermore, a newly designed weighted loss function was used in the training process of DU-Net to promote the segmentation performance. RESULTS A total of 270 patients with UCAs were enrolled in this study. The images were divided into the training (N = 174), validation (N = 43), and testing (N = 53) cohorts. The CCDU-Net achieved a dice similarity coefficient (DSC) of 0.616 ± 0.167, Hausdorff distance (HD) of 5.686 ± 7.020 mm, and volumetric similarity (VS) of 0.752 ± 0.226 in the testing cohort. Compared with the existing best method, the DSC and VS increased by 18% and 5%, respectively, while the HD decreased by one-tenth. CONCLUSIONS We proposed a CCDU-Net for segmenting UCAs in 3D TOF-MRA, and the obtained results show that the proposed method outperformed other existing methods.
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Affiliation(s)
- Meng Chen
- Xuzhou Medical University, 209 Tongshan Road, Xuzhou, 221000, China
| | - Chen Geng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163, China
| | - Dongdong Wang
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai, 200000, China
| | - Zhiyong Zhou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163, China.,Jinan Guoke Medical Engineering Technology Development Co., Ltd, Jinan, 250000, China
| | - Ruoyu Di
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai, 200000, China
| | - Fengmei Li
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163, China
| | - Sirong Piao
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai, 200000, China
| | - Jiajun Zhang
- Suzhou University of Science and Technology, 99 Xuefu Road, Suzhou, 215009, China
| | - Yuxin Li
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai, 200000, China.
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163, China.
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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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Heit JJ, Honce JM, Yedavalli VS, Baccin CE, Tatit RT, Copeland K, Timpone VM. RAPID Aneurysm: Artificial intelligence for unruptured cerebral aneurysm detection on CT angiography. J Stroke Cerebrovasc Dis 2022; 31:106690. [PMID: 35933764 DOI: 10.1016/j.jstrokecerebrovasdis.2022.106690] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 07/13/2022] [Accepted: 07/26/2022] [Indexed: 01/10/2023] Open
Abstract
OBJECTIVES Cerebral aneurysms may result in significant morbidity and mortality. Identification of these aneurysms on CT Angiography (CTA) studies is critical to guide patient treatment. Artificial intelligence platforms to assist with automated aneurysm detection are of high interest. We determined the performance of a semi-automated artificial intelligence software program (RAPID Aneurysm) for the detection of cerebral aneurysms. MATERIALS AND METHODS RAPID Aneurysm was used to detect retrospectively the presence of cerebral aneurysms in CTA studies performed between January 2019 and December 2020. The gold standard was aneurysm presence and location as determined by the consensus of three expert neuroradiologists. Aneurysm detection accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and positive and negative likelihood ratios by RAPID Aneurysm were determined. RESULTS 51 patients (mean age, 56±15; 24 women [47.1%]) with a single CTA were included. A total of 60 aneurysms were identified. RAPID Aneurysm had a sensitivity of 0.950 (95% CI: 0.863-0.983), specificity of 1.000 (95% CI: 0.996-1.000), a positive predictive value (PPV) of 1.000 (95% CI: 0.937-1.000), a negative predictive value (NPV) of 0.997 (95% CI: 0.991-0.999), and an accuracy of 0.997 (95% CI: 0.991-0.999) for cerebral aneurysm detection. CONCLUSIONS RAPID Aneurysm is highly accurate for the detection of cerebral aneurysms on CTA.
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Affiliation(s)
- Jeremy J Heit
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States.
| | - Justin M Honce
- Department of Radiology, University of Colorado School of Medicine, Denver, CO, United States
| | - Vivek S Yedavalli
- Department of Radiology, Johns Hopkins Medical School, Baltimore, MD, United States
| | - Carlos E Baccin
- Department of Radiology, Albert Einstein Hospital, São Paulo, Brazil
| | - Rafael T Tatit
- Department of Radiology, Albert Einstein Hospital, São Paulo, Brazil
| | | | - Vincent M Timpone
- Department of Radiology, University of Colorado School of Medicine, Denver, CO, United States
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Zhou J, Xia N, Li Q, Zheng K, Jia X, Wang H, Zhao B, Liu J, Yang Y, Chen Y. Predicting the rupture status of small middle cerebral artery aneurysms using random forest modeling. Front Neurol 2022; 13:921404. [PMID: 35968311 PMCID: PMC9366079 DOI: 10.3389/fneur.2022.921404] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 07/05/2022] [Indexed: 01/04/2023] Open
Abstract
Objective Small intracranial aneurysms are increasingly being detected; however, a prediction model for their rupture is rare. Random forest modeling was used to predict the rupture status of small middle cerebral artery (MCA) aneurysms with morphological features. Methods From January 2009 to June 2020, we retrospectively reviewed patients with small MCA aneurysms (<7 mm). The aneurysms were randomly split into training (70%) and internal validation (30%) cohorts. Additional independent datasets were used for the external validation of 78 small MCA aneurysms from another four hospitals. Aneurysm morphology was determined using computed tomography angiography (CTA). Prediction models were developed using the random forest and multivariate logistic regression. Results A total of 426 consecutive patients with 454 small MCA aneurysms (<7 mm) were included. A multivariate logistic regression analysis showed that size ratio (SR), aspect ratio (AR), and daughter dome were associated with aneurysm rupture, whereas aneurysm angle and multiplicity were inversely associated with aneurysm rupture. The areas under the receiver operating characteristic (ROC) curves (AUCs) of random forest models using the five independent risk factors in the training, internal validation, and external validation cohorts were 0.922, 0.889, and 0.92, respectively. The random forest model outperformed the logistic regression model (p = 0.048). A nomogram was developed to assess the rupture of small MCA aneurysms. Conclusion Random forest modeling is a good tool for evaluating the rupture status of small MCA aneurysms and may be considered for the management of small aneurysms.
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Affiliation(s)
- Jiafeng Zhou
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Nengzhi Xia
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qiong Li
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Department of Radiology, Wenzhou Central Hospital, Wenzhou, China
| | - Kuikui Zheng
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiufen Jia
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Hao Wang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Bing Zhao
- Department of Neurosurgery, Renji Hospital Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Jinjin Liu
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yunjun Yang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- *Correspondence: Yunjun Yang
| | - Yongchun Chen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Yongchun Chen
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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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Gu F, Wu X, Wu W, Wang Z, Yang X, Chen Z, Wang Z, Chen G. Performance of deep learning in the detection of intracranial aneurysm: a systematic review and meta-analysis. Eur J Radiol 2022; 155:110457. [DOI: 10.1016/j.ejrad.2022.110457] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 06/17/2022] [Accepted: 07/25/2022] [Indexed: 12/12/2022]
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36
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Chen G, Meng C, Ruoyu D, Dongdong W, Liqin Y, Wei X, Yuxin L, Daoying G. An Automatic Detection Method Of Cerebral Aneurysms In Time-Of-Flight Magnetic Resonance Angiography Images Based On Attention 3D U-Net. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 225:106998. [PMID: 35939977 DOI: 10.1016/j.cmpb.2022.106998] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 01/23/2022] [Accepted: 06/30/2022] [Indexed: 01/10/2023]
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Deep Learning-Based Magnetic Resonance Imaging in Diagnosis and Treatment of Intracranial Aneurysm. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:1683475. [PMID: 35734777 PMCID: PMC9208965 DOI: 10.1155/2022/1683475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 05/15/2022] [Accepted: 05/17/2022] [Indexed: 11/20/2022]
Abstract
This study was focused on the positioning of the intracranial aneurysm in the magnetic resonance imaging (MRI) images using the deep learning-based U-Net model, to realize the computer-aided diagnosis of the intracranial aneurysm. First, a network was established based on the three-dimensional (3D) U-Net model, and the collected image data were input into the network to realize the automatic location and segmentation of the aneurysm. The 3D convolutional neural network (CNN) network can extract the aneurysm blood vessels to locate and identify the areas of possible aneurysms. Next, 40 patients highly suspected of intracranial aneurysm were selected as research subjects, and they were subjected to magnetic resonance angiography (MRA) and digital subtraction angiography (DSA) examinations. The results showed that based on the U-Net algorithm model, 40 patients' hemangiomas were completely contained in the labeling bounding box, one patient's hemangioma was at the edge of the labeling bounding box, and 4 patients' hemangiomas were outside the labeling box. The final accuracy coefficient was 88.9%, and it was in good agreement with the doctor's manual labelling results. Under the 3D CNN network test, the sensitivity, specificity, and accuracy of DSA for intracranial aneurysm were 91.46%, 86.01%, and 90.2%, respectively; the sensitivity, specificity, and accuracy of MRA for intracranial aneurysm were 95.87%, 100%, and 97.19%, respectively. In conclusion, the 3D CNN can successfully realize the positioning of intracranial aneurysm in MRA images, providing a certain theoretical basis for subsequent imaging diagnosis of aneurysm.
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OTO-Net: An Automated MRA Image Segmentation Network for Intracranial Aneurysms. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5333589. [PMID: 35463249 PMCID: PMC9023216 DOI: 10.1155/2022/5333589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/08/2022] [Accepted: 03/17/2022] [Indexed: 11/17/2022]
Abstract
Intracranial aneurysms are local dilations of the cerebral blood vessels; people with intracranial aneurysms have a high risk to cause bleeding in the brain, which is related to high mortality and morbidity rates. Accurate detection and segmentation of intracranial aneurysms from Magnetic Resonance Angiography (MRA) images are essential in the clinical routine. Manual annotations used to assess the intracranial aneurysms on MRA images are substantial interobserver variability for both aneurysm detection and assessment of aneurysm size and growth. Many prior automated segmentation works have focused their efforts on tackling the problem, but there is still room for performance improvement due to the significant variability of lesions in the location, size, structure, and morphological appearance. To address these challenges, we propose a novel One-Two-One Fully Convolutional Networks (OTO-Net) for intracranial aneurysms automated segmentation in MRA images. The OTO-Net uses full convolution to achieve intracranial aneurysms automated segmentation through the combination of downsampling, upsampling, and skip connection. In addition, loss ensemble is used as the objective function to steadily improve the backpropagation efficiency of the network structure during the training process. We evaluated the proposed OTO-Net on one public benchmark dataset and one private dataset. Our proposed model can achieve the automated segmentation accuracy with 98.37% and 97.86%, average surface distances with 1.081 and 0.753, dice similarity coefficients with 0.9721 and 0.9813, and Hausdorff distance with 0.578 and 0.642 on these two datasets, respectively.
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Alwalid O, Long X, Xie M, Han P. Artificial Intelligence Applications in Intracranial Aneurysm: Achievements, Challenges and Opportunities. Acad Radiol 2022; 29 Suppl 3:S201-S214. [PMID: 34376335 DOI: 10.1016/j.acra.2021.06.013] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 06/22/2021] [Accepted: 06/29/2021] [Indexed: 01/10/2023]
Abstract
Intracranial aneurysms present in about 3% of the general population and the number of detected aneurysms is continuously rising with the advances in imaging techniques. Intracranial aneurysm rupture carries a high risk of death or permanent disabilities; therefore assessment of the intracranial aneurysm along the entire course is of great clinical importance. Given the outstanding performance of artificial intelligence (AI) in image-based tasks, many AI-based applications have emerged in recent years for the assessment of intracranial aneurysms. In this review we will summarize the state-of-the-art of AI applications in intracranial aneurysms, emphasizing the achievements, and exploring the challenges. We will also discuss the future prospects and potential opportunities. This article provides an updated view of the AI applications in intracranial aneurysms and may act as a basis for guiding the related future works.
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Affiliation(s)
- Osamah Alwalid
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan 430022, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Xi Long
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan 430022, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Mingfei Xie
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan 430022, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Ping Han
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan 430022, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China.
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40
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Cummins DD, Caton MT, Shah V, Meisel K, Glastonbury C, Amans MR. MRI and MR angiography evaluation of pulsatile tinnitus: A focused, physiology-based protocol. J Neuroimaging 2022; 32:253-263. [PMID: 34910345 PMCID: PMC8917066 DOI: 10.1111/jon.12955] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND AND PURPOSE Pulsatile tinnitus (PT) is the subjective sensation of a pulse-synchronous sound, most often due to a cerebrovascular etiology. PT can severely impact quality of life and may indicate a life-threatening process, yet a timely and accurate diagnosis can often lead to effective treatment. Clinical assessment with a history and physical examination can often suggest a diagnosis for PT, but is rarely definitive. Therefore, PT should be evaluated with a comprehensive and targeted radiographic imaging protocol. MR imaging provides a safe and effective means to evaluate PT. Specific MR sequences may be used to highlight different elements of cerebrovascular anatomy and physiology. However, routine MR evaluation of PT must comply with economic and practical constraints, while effectively capturing both common and rarer, life-threatening etiologies of PT. METHODS In this state-of-the-art review, we describe our institutional MR protocol for evaluating PT. RESULTS This protocol includes the following dedicated sequences: time-of-flight magnetic resonance angiography; arterial spin labeling; spoiled gradient recalled acquisition in the steady state; time-resolved imaging of contrast kinetics; diffusion weighted imaging, and 3-dimensional fluid-attenuated inversion recovery. CONCLUSIONS We describe the physiologic and clinical rationale for including each MR sequence in a comprehensive PT imaging protocol, and detail the role of MR within the broader evaluation of PT, from clinical presentation to treatment.
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Affiliation(s)
- Daniel D. Cummins
- School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Michael T. Caton
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Vinil Shah
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Karl Meisel
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Christine Glastonbury
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Matthew R. Amans
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA,Corresponding author: Matthew R. Amans, Address: 505 Parnassus Ave, Room L349, San Francisco, CA 94143, Telephone: 415-353-1863, Fax: 415-353-8606,
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Ou C, Li C, Qian Y, Duan CZ, Si W, Zhang X, Li X, Morgan M, Dou Q, Heng PA. Morphology-aware multi-source fusion-based intracranial aneurysms rupture prediction. Eur Radiol 2022; 32:5633-5641. [PMID: 35182202 DOI: 10.1007/s00330-022-08608-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 12/29/2021] [Accepted: 01/23/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES We proposed a new approach to train deep learning model for aneurysm rupture prediction which only uses a limited amount of labeled data. METHOD Using segmented aneurysm mask as input, a backbone model was pretrained using a self-supervised method to learn deep embeddings of aneurysm morphology from 947 unlabeled cases of angiographic images. Subsequently, the backbone model was finetuned using 120 labeled cases with known rupture status. Clinical information was integrated with deep embeddings to further improve prediction performance. The proposed model was compared with radiomics and conventional morphology models in prediction performance. An assistive diagnosis system was also developed based on the model and was tested with five neurosurgeons. RESULT Our method achieved an area under the receiver operating characteristic curve (AUC) of 0.823, outperforming deep learning model trained from scratch (0.787). By integrating with clinical information, the proposed model's performance was further improved to AUC = 0.853, making the results significantly better than model based on radiomics (AUC = 0.805, p = 0.007) or model based on conventional morphology parameters (AUC = 0.766, p = 0.001). Our model also achieved the highest sensitivity, PPV, NPV, and accuracy among the others. Neurosurgeons' prediction performance was improved from AUC=0.877 to 0.945 (p = 0.037) with the assistive diagnosis system. CONCLUSION Our proposed method could develop competitive deep learning model for rupture prediction using only a limited amount of data. The assistive diagnosis system could be useful for neurosurgeons to predict rupture. KEY POINTS • A self-supervised learning method was proposed to mitigate the data-hungry issue of deep learning, enabling training deep neural network with a limited amount of data. • Using the proposed method, deep embeddings were extracted to represent intracranial aneurysm morphology. Prediction model based on deep embeddings was significantly better than conventional morphology model and radiomics model. • An assistive diagnosis system was developed using deep embeddings for case-based reasoning, which was shown to significantly improve neurosurgeons' performance to predict rupture.
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Affiliation(s)
- Chubin Ou
- Neurosurgery Center, Department of Cerebrovascular Surgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China.,Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Caizi Li
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yi Qian
- Neurosurgery Center, Department of Cerebrovascular Surgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
| | - Chuan-Zhi Duan
- Neurosurgery Center, Department of Cerebrovascular Surgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
| | - Weixin Si
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
| | - Xin Zhang
- Neurosurgery Center, Department of Cerebrovascular Surgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Xifeng Li
- Neurosurgery Center, Department of Cerebrovascular Surgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Michael Morgan
- Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Qi Dou
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR
| | - Pheng-Ann Heng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR
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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: 1.0] [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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Terasaki Y, Yokota H, Tashiro K, Maejima T, Takeuchi T, Kurosawa R, Yamauchi S, Takada A, Mukai H, Ohira K, Ota J, Horikoshi T, Mori Y, Uno T, Suyari H. Multidimensional Deep Learning Reduces False-Positives in the Automated Detection of Cerebral Aneurysms on Time-Of-Flight Magnetic Resonance Angiography: A Multi-Center Study. Front Neurol 2022; 12:742126. [PMID: 35115991 PMCID: PMC8805516 DOI: 10.3389/fneur.2021.742126] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 12/07/2021] [Indexed: 11/16/2022] Open
Abstract
Current deep learning-based cerebral aneurysm detection demonstrates high sensitivity, but produces numerous false-positives (FPs), which hampers clinical application of automated detection systems for time-of-flight magnetic resonance angiography. To reduce FPs while maintaining high sensitivity, we developed a multidimensional convolutional neural network (MD-CNN) designed to unite planar and stereoscopic information about aneurysms. This retrospective study enrolled time-of-flight magnetic resonance angiography images of cerebral aneurysms from three institutions from June 2006 to April 2019. In the internal test, 80% of the entire data set was used for model training and 20% for the test, while for the external tests, data from different pairs of the three institutions were used for training and the remaining one for testing. Images containing aneurysms > 15 mm and images without aneurysms were excluded. Three deep learning models [planar information-only (2D-CNN), stereoscopic information-only (3D-CNN), and multidimensional information (MD-CNN)] were trained to classify whether the voxels contained aneurysms, and they were evaluated on each test. The performance of each model was assessed using free-response operating characteristic curves. In total, 732 aneurysms (5.9 ± 2.5 mm) of 559 cases (327, 120, and 112 from institutes A, B, and C; 469 and 263 for 1.5T and 3.0T MRI) were included in this study. In the internal test, the highest sensitivities were 80.4, 87.4, and 82.5%, and the FPs were 6.1, 7.1, and 5.0 FPs/case at a fixed sensitivity of 80% for the 2D-CNN, 3D-CNN, and MD-CNN, respectively. In the external test, the highest sensitivities were 82.1, 86.5, and 89.1%, and 5.9, 7.4, and 4.2 FPs/cases for them, respectively. MD-CNN was a new approach to maintain sensitivity and reduce the FPs simultaneously.
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Affiliation(s)
- Yuki Terasaki
- Graduate School of Science and Engineering, Chiba University, Chiba, Japan
- Department of EC Platform, ZOZO Technologies, Inc., Tokyo, Japan
| | - Hajime Yokota
- Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan
- *Correspondence: Hajime Yokota
| | - Kohei Tashiro
- Graduate School of Science and Engineering, Chiba University, Chiba, Japan
- Kohei Tashiro
| | - Takuma Maejima
- Department of Radiology, Chiba University Hospital, Chiba, Japan
| | - Takashi Takeuchi
- Department of Radiology, Chiba University Hospital, Chiba, Japan
| | - Ryuna Kurosawa
- Department of Radiology, Chiba University Hospital, Chiba, Japan
| | - Shoma Yamauchi
- Department of Radiology, Chiba University Hospital, Chiba, Japan
| | - Akiyo Takada
- Department of Radiology, Chiba University Hospital, Chiba, Japan
| | - Hiroki Mukai
- Department of Radiology, Chiba University Hospital, Chiba, Japan
| | - Kenji Ohira
- Department of Radiology, Chiba University Hospital, Chiba, Japan
| | - Joji Ota
- Department of Radiology, Chiba University Hospital, Chiba, Japan
| | - Takuro Horikoshi
- Department of Radiology, Chiba University Hospital, Chiba, Japan
| | - Yasukuni Mori
- Graduate School of Engineering, Chiba University, Chiba, Japan
| | - Takashi Uno
- Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Hiroki Suyari
- Graduate School of Engineering, Chiba University, Chiba, Japan
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Chen L, Liu K, Shen H, Ye H, Liu H, Yu L, Li J, Zhao K, Zhu W. Multi-Modality Attention-Guided Three-Dimensional Detection of Non-Small Cell Lung Cancer in 18F-FDG PET/CT Images. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022. [DOI: 10.1109/trpms.2021.3072064] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Artificial Intelligence Assistance Improves the Accuracy and Efficiency of Intracranial Aneurysm Detection with CT Angiography. Eur J Radiol 2022; 149:110169. [DOI: 10.1016/j.ejrad.2022.110169] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 01/06/2022] [Accepted: 01/15/2022] [Indexed: 01/10/2023]
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Ivantsits M, Goubergrits L, Kuhnigk JM, Huellebrand M, Bruening J, Kossen T, Pfahringer B, Schaller J, Spuler A, Kuehne T, Jia Y, Li X, Shit S, Menze B, Su Z, Ma J, Nie Z, Jain K, Liu Y, Lin Y, Hennemuth A. Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge. Med Image Anal 2021; 77:102333. [PMID: 34998111 DOI: 10.1016/j.media.2021.102333] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 10/12/2021] [Accepted: 12/07/2021] [Indexed: 01/10/2023]
Abstract
The Cerebral Aneurysm Detection and Analysis (CADA) challenge was organized to support the development and benchmarking of algorithms for detecting, analyzing, and risk assessment of cerebral aneurysms in X-ray rotational angiography (3DRA) images. 109 anonymized 3DRA datasets were provided for training, and 22 additional datasets were used to test the algorithmic solutions. Cerebral aneurysm detection was assessed using the F2 score based on recall and precision, and the fit of the delivered bounding box was assessed using the distance to the aneurysm. The segmentation quality was measured using the Jaccard index and a combination of different surface distance measures. Systematic errors were analyzed using volume correlation and bias. Rupture risk assessment was evaluated using the F2 score. 158 participants from 22 countries registered for the CADA challenge. The U-Net-based detection solutions presented by the community show similar accuracy compared to experts (F2 score 0.92), with a small number of missed aneurysms with diameters smaller than 3.5 mm. In addition, the delineation of these structures, based on U-Net variations, is excellent, with a Jaccard score of 0.92. The rupture risk estimation methods achieved an F2 score of 0.71. The performance of the detection and segmentation solutions is equivalent to that of human experts. The best results are obtained in rupture risk estimation by combining different image-based, morphological, and computational fluid dynamic parameters using machine learning methods. Furthermore, we evaluated the best methods pipeline, from detecting and delineating the vessel dilations to estimating the risk of rupture. The chain of these methods achieves an F2-score of 0.70, which is comparable to applying the risk prediction to the ground-truth delineation (0.71).
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Affiliation(s)
- Matthias Ivantsits
- Charit Universittsmedizin Berlin, Augustenburger Pl. 1, Berlin 13353, Germany.
| | - Leonid Goubergrits
- Charit Universittsmedizin Berlin, Augustenburger Pl. 1, Berlin 13353, Germany; Einstein Center Digital Future, Wilhelmstrae 67, Berlin 10117, Germany
| | | | - Markus Huellebrand
- Charit Universittsmedizin Berlin, Augustenburger Pl. 1, Berlin 13353, Germany; Fraunhofer MEVIS, Am Fallturm 1, Bremen 28359, Germany
| | - Jan Bruening
- Charit Universittsmedizin Berlin, Augustenburger Pl. 1, Berlin 13353, Germany
| | - Tabea Kossen
- Charit Universittsmedizin Berlin, Augustenburger Pl. 1, Berlin 13353, Germany
| | - Boris Pfahringer
- Charit Universittsmedizin Berlin, Augustenburger Pl. 1, Berlin 13353, Germany
| | - Jens Schaller
- Charit Universittsmedizin Berlin, Augustenburger Pl. 1, Berlin 13353, Germany
| | - Andreas Spuler
- Helios Hospital Berlin-Buch, Schwanebecker Chaussee 50, Berlin 13125, Germany
| | - Titus Kuehne
- Charit Universittsmedizin Berlin, Augustenburger Pl. 1, Berlin 13353, Germany; German Heart Centre Berlin, Augustenburger Pl. 1, Berlin 13353, Germany; DZHK (German Centre for Cardiovascular Research), Berlin, Germany
| | - Yizhuan Jia
- Mediclouds Medical Technology, Beijing, China
| | - Xuesong Li
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
| | - Suprosanna Shit
- Departments of Informatics, Technical University Munich, Germany; TranslaTUM Center for Translational Cancer Research, Munich, Germany
| | - Bjoern Menze
- Departments of Informatics, Technical University Munich, Germany; TranslaTUM Center for Translational Cancer Research, Munich, Germany; Department of Quantitative Biomedicine of UZH, Zurich, Switzerland
| | - Ziyu Su
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
| | - Jun Ma
- Department of Mathematics, Nanjing University of Science and Technology, Nanjing, China
| | - Ziwei Nie
- Department of Mathematics, Nanjing University, Nanjing, China
| | - Kartik Jain
- Faculty of Engineering Technology, University of Twente, P.O. Box 217, Enschede 7500, AE, the Netherlands
| | - Yanfei Liu
- Jarvis Lab, Tencent, Shenzhen, China; Shenzhen United Imaging Research Institute of Innovative Medical Equipment Innovation Research, Shenzhen, China
| | - Yi Lin
- Jarvis Lab, Tencent, Shenzhen, China
| | - Anja Hennemuth
- Charit Universittsmedizin Berlin, Augustenburger Pl. 1, Berlin 13353, Germany; Fraunhofer MEVIS, Am Fallturm 1, Bremen 28359, Germany; German Heart Centre Berlin, Augustenburger Pl. 1, Berlin 13353, Germany; DZHK (German Centre for Cardiovascular Research), Berlin, Germany
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Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:171-182. [PMID: 34862541 DOI: 10.1007/978-3-030-85292-4_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
This chapter describes technical considerations and current and future clinical applications of lesion detection using machine learning in the clinical setting. Lesion detection is central to neuroradiology and precedes all further processes which include but are not limited to lesion characterization, quantification, longitudinal disease assessment, prognosis, and prediction of treatment response. A number of machine learning algorithms focusing on lesion detection have been developed or are currently under development which may either support or extend the imaging process. Examples include machine learning applications in stroke, aneurysms, multiple sclerosis, neuro-oncology, neurodegeneration, and epilepsy.
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Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:319-331. [PMID: 34862556 DOI: 10.1007/978-3-030-85292-4_36] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Machine learning (ML) is a rapidly rising research tool in biomedical sciences whose applications include segmentation, classification, disease detection, and outcome prediction. With respect to traditional statistical methods, ML algorithms have the potential to learn and improve their predictive performance when fed with large data sets without the need of being specifically programmed. In recent years, this technology has been increasingly applied for tackling clinical issues in intracranial aneurysm (IA) research. Several studies attempted to provide reliable models for enhanced aneurysm detection. Convolutional neural networks trained with variable degrees of human interaction on data from diverse imaging modalities showed high sensitivity in aneurysm detection tasks, also outperforming expert image analysis. Algorithms were also shown to differentiate ruptured from unruptured IAs, with however limited clinical relevance. For prediction of rupture and stability assessment, ML was preliminarily shown to achieve better performance compared to conventional statistical methods and existing risk scores. ML-based complication and functional outcome prediction in the event of SAH have been more extensively reported, in contrast with periprocedural outcome investigation in unruptured IA patients. ML has the potential to be a game changer in IA patient management. Currently clinical translation of experimental results is limited.
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Stumpo V, Kernbach JM, van Niftrik CHB, Sebök M, Fierstra J, Regli L, Serra C, Staartjes VE. Machine Learning Algorithms in Neuroimaging: An Overview. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:125-138. [PMID: 34862537 DOI: 10.1007/978-3-030-85292-4_17] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Machine learning (ML) and artificial intelligence (AI) applications in the field of neuroimaging have been on the rise in recent years, and their clinical adoption is increasing worldwide. Deep learning (DL) is a field of ML that can be defined as a set of algorithms enabling a computer to be fed with raw data and progressively discover-through multiple layers of representation-more complex and abstract patterns in large data sets. The combination of ML and radiomics, namely the extraction of features from medical images, has proven valuable, too: Radiomic information can be used for enhanced image characterization and prognosis or outcome prediction. This chapter summarizes the basic concepts underlying ML application for neuroimaging and discusses technical aspects of the most promising algorithms, with a specific focus on Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), in order to provide the readership with the fundamental theoretical tools to better understand ML in neuroimaging. Applications are highlighted from a practical standpoint in the last section of the chapter, including: image reconstruction and restoration, image synthesis and super-resolution, registration, segmentation, classification, and outcome prediction.
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Affiliation(s)
- Vittorio Stumpo
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Julius M Kernbach
- Neurosurgical Artificial Intelligence Lab Aachen (NAILA), Department of Neurosurgery, RWTH University Hospital, Aachen, Germany
- Department of Neurosurgery, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
| | - Christiaan H B van Niftrik
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Martina Sebök
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Jorn Fierstra
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Luca Regli
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Carlo Serra
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Victor E Staartjes
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
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Joo B, Choi HS, Ahn SS, Cha J, Won SY, Sohn B, Kim H, Han K, Kim HP, Choi JM, Lee SM, Kim TG, Lee SK. A Deep Learning Model with High Standalone Performance for Diagnosis of Unruptured Intracranial Aneurysm. Yonsei Med J 2021; 62:1052-1061. [PMID: 34672139 PMCID: PMC8542476 DOI: 10.3349/ymj.2021.62.11.1052] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 07/29/2021] [Accepted: 08/30/2021] [Indexed: 02/07/2023] Open
Abstract
PURPOSE This study aimed to investigate whether a deep learning model for automated detection of unruptured intracranial aneurysms on time-of-flight (TOF) magnetic resonance angiography (MRA) can achieve a target diagnostic performance comparable to that of human radiologists for approval from the Korean Ministry of Food and Drug Safety as an artificial intelligence-applied software. MATERIALS AND METHODS In this single-center, retrospective, confirmatory clinical trial, the diagnostic performance of the model was evaluated in a predetermined test set. After sample size estimation, the test set consisted of 135 aneurysm-containing examinations with 168 intracranial aneurysms and 197 aneurysm-free examinations. The target sensitivity and specificity were set as 87% and 92%, respectively. The patient-wise sensitivity and specificity of the model were analyzed. Moreover, the lesion-wise sensitivity and false-positive detection rate per case were also investigated. RESULTS The sensitivity and specificity of the model were 91.11% [95% confidence interval (CI): 84.99, 95.32] and 93.91% (95% CI: 89.60, 96.81), respectively, which met the target performance values. The lesion-wise sensitivity was 92.26%. The overall false-positive detection rate per case was 0.123. Of the 168 aneurysms, 13 aneurysms from 12 examinations were missed by the model. CONCLUSION The present deep learning model for automated detection of unruptured intracranial aneurysms on TOF MRA achieved the target diagnostic performance comparable to that of human radiologists. With high standalone performance, this model may be useful for accurate and efficient diagnosis of intracranial aneurysm.
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Affiliation(s)
- Bio Joo
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hyun Seok Choi
- Department of Radiology, Seoul Medical Center, Seoul, Korea
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea.
| | - Sung Soo Ahn
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Jihoon Cha
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - So Yeon Won
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Beomseok Sohn
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Hwiyoung Kim
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
| | | | | | | | | | - Seung-Koo Lee
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
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