1
|
Assis Y, Liao L, Pierre F, Anxionnat R, Kerrien E. Intracranial aneurysm detection: an object detection perspective. Int J Comput Assist Radiol Surg 2024; 19:1667-1675. [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] [MESH Headings] [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.
Collapse
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
| |
Collapse
|
2
|
Li Y, Zhang H, Sun Y, Fan Q, Wang L, Ji C, HuiGu, Chen B, Zhao S, Wang D, Yu P, Li J, Yang S, Zhang C, Wang X. Deep learning-based platform performs high detection sensitivity of intracranial aneurysms in 3D brain TOF-MRA: An external clinical validation study. Int J Med Inform 2024; 188:105487. [PMID: 38761459 DOI: 10.1016/j.ijmedinf.2024.105487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 05/06/2024] [Accepted: 05/15/2024] [Indexed: 05/20/2024]
Abstract
PURPOSE To evaluate the diagnostic efficacy of a developed artificial intelligence (AI) platform incorporating deep learning algorithms for the automated detection of intracranial aneurysms in time-of-flight (TOF) magnetic resonance angiography (MRA). METHOD This retrospective study encompassed 3D TOF MRA images acquired between January 2023 and June 2023, aiming to validate the presence of intracranial aneurysms via our developed AI platform. The manual segmentation results by experienced neuroradiologists served as the "gold standard". Following annotation of MRA images by neuroradiologists using InferScholar software, the AI platform conducted automatic segmentation of intracranial aneurysms. Various metrics including accuracy (ACC), balanced ACC, area under the curve (AUC), sensitivity (SE), specificity (SP), F1 score, Brier Score, and Net Benefit were utilized to evaluate the generalization of AI platform. Comparison of intracranial aneurysm identification performance was conducted between the AI platform and six radiologists with experience ranging from 3 to 12 years in interpreting MR images. Additionally, a comparative analysis was carried out between radiologists' detection performance based on independent visual diagnosis and AI-assisted diagnosis. Subgroup analyses were also performed based on the size and location of the aneurysms to explore factors impacting aneurysm detectability. RESULTS 510 patients were enrolled including 215 patients (42.16 %) with intracranial aneurysms and 295 patients (57.84 %) without aneurysms. Compared with six radiologists, the AI platform showed competitive discrimination power (AUC, 0.96), acceptable calibration (Brier Score loss, 0.08), and clinical utility (Net Benefit, 86.96 %). The AI platform demonstrated superior performance in detecting aneurysms with an overall SE, SP, ACC, balanced ACC, and F1 score of 91.63 %, 92.20 %, 91.96 %, 91.92 %, and 90.57 % respectively, outperforming the detectability of the two resident radiologists. For subgroup analysis based on aneurysm size and location, we observed that the SE of the AI platform for identifying tiny (diameter<3mm), small (3 mm ≤ diameter<5mm), medium (5 mm ≤ diameter<7mm) and large aneurysms (diameter ≥ 7 mm) was 87.80 %, 93.14 %, 95.45 %, and 100 %, respectively. Furthermore, the SE for detecting aneurysms in the anterior circulation was higher than that in the posterior circulation. Utilizing the AI assistance, six radiologists (i.e., two residents, two attendings and two professors) achieved statistically significant improvements in mean SE (residents: 71.40 % vs. 88.37 %; attendings: 82.79 % vs. 93.26 %; professors: 90.07 % vs. 97.44 %; P < 0.05) and ACC (residents: 85.29 % vs. 94.12 %; attendings: 91.76 % vs. 97.06 %; professors: 95.29 % vs. 98.82 %; P < 0.05) while no statistically significant change was observed in SP. Overall, radiologists' mean SE increased by 11.40 %, mean SP increased by 1.86 %, and mean ACC increased by 5.88 %, mean balanced ACC promoted by 6.63 %, mean F1 score grew by 7.89 %, and Net Benefit rose by 12.52 %, with a concurrent decrease in mean Brier score declined by 0.06. CONCLUSIONS The deep learning algorithms implemented in the AI platform effectively detected intracranial aneurysms on TOF-MRA and notably enhanced the diagnostic capabilities of radiologists. This indicates that the AI-based auxiliary diagnosis model can provide dependable and precise prediction to improve the diagnostic capacity of radiologists.
Collapse
Affiliation(s)
- Yuanyuan Li
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China; Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, China; Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, China
| | - Huiling Zhang
- Institute of Research, Infervision Medical Technology Co., Ltd, China
| | - Yun Sun
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China
| | - Qianrui Fan
- Institute of Research, Infervision Medical Technology Co., Ltd, China
| | - Long Wang
- Department of Cardiovascular Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China
| | - Congshan Ji
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China
| | - HuiGu
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China; Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, China; Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, China
| | - Baojin Chen
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China
| | - Shuo Zhao
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China; Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, China; Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, China
| | - Dawei Wang
- Institute of Research, Infervision Medical Technology Co., Ltd, China
| | - Pengxin Yu
- Institute of Research, Infervision Medical Technology Co., Ltd, China
| | - Junchen Li
- Department of Radiology, Changshu Hospital Affiliated to Nanjing University of Chinese Medicine, China
| | - Shifeng Yang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China.
| | - Chuanchen Zhang
- Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, China.
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, China; Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, China.
| |
Collapse
|
3
|
Zhou Z, Jin Y, Ye H, Zhang X, Liu J, Zhang W. Classification, detection, and segmentation performance of image-based AI in intracranial aneurysm: a systematic review. BMC Med Imaging 2024; 24:164. [PMID: 38956538 PMCID: PMC11218239 DOI: 10.1186/s12880-024-01347-9] [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: 04/29/2024] [Accepted: 06/25/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND The detection and management of intracranial aneurysms (IAs) are vital to prevent life-threatening complications like subarachnoid hemorrhage (SAH). Artificial Intelligence (AI) can analyze medical images, like CTA or MRA, spotting nuances possibly overlooked by humans. Early detection facilitates timely interventions and improved outcomes. Moreover, AI algorithms offer quantitative data on aneurysm attributes, aiding in long-term monitoring and assessing rupture risks. METHODS We screened four databases (PubMed, Web of Science, IEEE and Scopus) for studies using artificial intelligence algorithms to identify IA. Based on algorithmic methodologies, we categorized them into classification, segmentation, detection and combined, and then their merits and shortcomings are compared. Subsequently, we elucidate potential challenges that contemporary algorithms might encounter within real-world clinical diagnostic contexts. Then we outline prospective research trajectories and underscore key concerns in this evolving field. RESULTS Forty-seven studies of IA recognition based on AI were included based on search and screening criteria. The retrospective results represent that current studies can identify IA in different modal images and predict their risk of rupture and blockage. In clinical diagnosis, AI can effectively improve the diagnostic accuracy of IA and reduce missed detection and false positives. CONCLUSIONS The AI algorithm can detect unobtrusive IA more accurately in communicating arteries and cavernous sinus arteries to avoid further expansion. In addition, analyzing aneurysm rupture and blockage before and after surgery can help doctors plan treatment and reduce the uncertainties in the treatment process.
Collapse
Affiliation(s)
- Zhiyue Zhou
- School of Medicine, Southern University of Science and Technology, Southern University of Science and Technology, Shenzhen, China
| | - Yuxuan Jin
- School of Medicine, Southern University of Science and Technology, Southern University of Science and Technology, Shenzhen, China
| | - Haili Ye
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Xiaoqing Zhang
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China.
| | - Jiang Liu
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China.
- School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, China.
| | - Wenyong Zhang
- School of Medicine, Southern University of Science and Technology, Southern University of Science and Technology, Shenzhen, China.
| |
Collapse
|
4
|
Koizumi S, Kin T, Shono N, Kiyofuji S, Umekawa M, Sato K, Saito N. Patient-specific cerebral 3D vessel model reconstruction using deep learning. Med Biol Eng Comput 2024:10.1007/s11517-024-03136-6. [PMID: 38802608 DOI: 10.1007/s11517-024-03136-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 05/18/2024] [Indexed: 05/29/2024]
Abstract
Three-dimensional vessel model reconstruction from patient-specific magnetic resonance angiography (MRA) images often requires some manual maneuvers. This study aimed to establish the deep learning (DL)-based method for vessel model reconstruction. Time of flight MRA of 40 patients with internal carotid artery aneurysms was prepared, and three-dimensional vessel models were constructed using the threshold and region-growing method. Using those datasets, supervised deep learning using 2D U-net was performed to reconstruct 3D vessel models. The accuracy of the DL-based vessel segmentations was assessed using 20 MRA images outside the training dataset. The dice coefficient was used as the indicator of the model accuracy, and the blood flow simulation was performed using the DL-based vessel model. The created DL model could successfully reconstruct a three-dimensional model in all 60 cases. The dice coefficient in the test dataset was 0.859. Of note, the DL-generated model proved its efficacy even for large aneurysms (> 10 mm in their diameter). The reconstructed model was feasible in performing blood flow simulation to assist clinical decision-making. Our DL-based method could successfully reconstruct a three-dimensional vessel model with moderate accuracy. Future studies are warranted to exhibit that DL-based technology can promote medical image processing.
Collapse
Affiliation(s)
- Satoshi Koizumi
- Department of Neurosurgery, The University of Tokyo Hospital, 7-3-1Bunkyo-Ku, HongoTokyo, 113-8655, Japan.
| | - Taichi Kin
- Department of Neurosurgery, The University of Tokyo Hospital, 7-3-1Bunkyo-Ku, HongoTokyo, 113-8655, Japan
- Department of Medical Information Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Naoyuki Shono
- Department of Neurosurgery, The University of Tokyo Hospital, 7-3-1Bunkyo-Ku, HongoTokyo, 113-8655, Japan
| | - Satoshi Kiyofuji
- Department of Neurosurgery, The University of Tokyo Hospital, 7-3-1Bunkyo-Ku, HongoTokyo, 113-8655, Japan
| | - Motoyuki Umekawa
- Department of Neurosurgery, The University of Tokyo Hospital, 7-3-1Bunkyo-Ku, HongoTokyo, 113-8655, Japan
| | - Katsuya Sato
- Department of Neurosurgery, The University of Tokyo Hospital, 7-3-1Bunkyo-Ku, HongoTokyo, 113-8655, Japan
| | - Nobuhito Saito
- Department of Neurosurgery, The University of Tokyo Hospital, 7-3-1Bunkyo-Ku, HongoTokyo, 113-8655, Japan
| |
Collapse
|
5
|
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.
Collapse
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.
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
Kuwabara M, Ikawa F, Sakamoto S, Okazaki T, Ishii D, Hosogai M, Maeda Y, Chiku M, Kitamura N, Choppin A, Takamiya D, Shimahara Y, Nakayama T, Kurisu K, Horie N. Effectiveness of tuning an artificial intelligence algorithm for cerebral aneurysm diagnosis: a study of 10,000 consecutive cases. Sci Rep 2023; 13:16202. [PMID: 37758849 PMCID: PMC10533861 DOI: 10.1038/s41598-023-43418-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 09/23/2023] [Indexed: 09/29/2023] Open
Abstract
Diagnostic image analysis for unruptured cerebral aneurysms using artificial intelligence has a very high sensitivity. However, further improvement is needed because of a relatively high number of false positives. This study aimed to confirm the clinical utility of tuning an artificial intelligence algorithm for cerebral aneurysm diagnosis. We extracted 10,000 magnetic resonance imaging scans of participants who underwent brain screening using the "Brain Dock" system. The sensitivity and false positives/case for aneurysm detection were compared before and after tuning the algorithm. The initial diagnosis included only cases for which feedback to the algorithm was provided. In the primary analysis, the sensitivity of aneurysm diagnosis decreased from 96.5 to 90% and the false positives/case improved from 2.06 to 0.99 after tuning the algorithm (P < 0.001). In the secondary analysis, the sensitivity of aneurysm diagnosis decreased from 98.8 to 94.6% and the false positives/case improved from 1.99 to 1.03 after tuning the algorithm (P < 0.001). The false positives/case reduced without a significant decrease in sensitivity. Using large clinical datasets, we demonstrated that by tuning the algorithm, we could significantly reduce false positives with a minimal decline in sensitivity.
Collapse
Affiliation(s)
- Masashi Kuwabara
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Fusao Ikawa
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan.
- Department of Neurosurgery, Shimane Prefectural Central Hospital, 4-1-1 Himebara, Izumo, Shimane, 693-8555, Japan.
| | - Shigeyuki Sakamoto
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Takahito Okazaki
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Daizo Ishii
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Masahiro Hosogai
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Yuyo Maeda
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Masaaki Chiku
- Department of Neurosurgery, Medical Check Studio, Tokyo Ginza Clinic, 1-2-4 Ginza, Chuo-ku, Tokyo, 104-0061, Japan
| | - Naoyuki Kitamura
- Department of Diagnostic Radiology, Kasumi Clinic, 1-2-27 Shinonomehommachi, Minami-ku, Hiroshima, Hiroshima, 734-0023, Japan
| | - Antoine Choppin
- LPIXEL Inc., 1-6-1 Otemachi, Chiyoda-ku, Tokyo, 100-0004, Japan
| | | | - Yuki Shimahara
- LPIXEL Inc., 1-6-1 Otemachi, Chiyoda-ku, Tokyo, 100-0004, Japan
| | - Takeo Nakayama
- Department of Health Informatics, School of Public Health, Graduate School of Medicine, Kyoto University, Yoshida-Konoe, Sakyo-ku, Kyoto, Kyoto, 606-8501, Japan
| | - Kaoru Kurisu
- Chugoku Rosai Hospital, 1-5-1 Hirotagaya, Kure, Hiroshima, 737-0193, Japan
| | - Nobutaka Horie
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan
| |
Collapse
|
9
|
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.
Collapse
Affiliation(s)
| | | | | | | | - Reza Dashti
- Dashti Lab, Department of Neurological Surgery, Stony Brook University Hospital, Stony Brook, NY, United States
| |
Collapse
|
10
|
Levinson S, Pendharkar AV, Gauden AJ, Heit JJ. Modern Imaging of Aneurysmal Subarachnoid Hemorrhage. Radiol Clin North Am 2023; 61:457-465. [PMID: 36931762 DOI: 10.1016/j.rcl.2023.01.004] [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] [Indexed: 02/21/2023]
Abstract
In this review, we discuss the imaging of aneurysmal subarachnoid hemorrhage (SAH). We discuss emergency brain imaging, aneurysm detection techniques, and the management of CTA-negative SAH. We also review the concepts of cerebral vasospasm and delayed cerebral ischemia that occurs after aneurysm rupture and their impact on patient outcomes. These pathologies are distinct, and the use of multimodal imaging modalities is essential for prompt diagnosis and management to minimize morbidity from these conditions. Lastly, new advances in artificial intelligence and advanced imaging modalities such as PET and MR imaging scans have been shown to improve the detection of aneurysms and potentially predict outcomes early in the course of SAH.
Collapse
Affiliation(s)
- Simon Levinson
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Arjun V Pendharkar
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Andrew J Gauden
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Jeremy J Heit
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA; Stanford School of Medicine, 453 Quarry Road, Palo Alto, CA 94304, USA.
| |
Collapse
|
11
|
Lee SB, Hong Y, Cho YJ, Jeong D, Lee J, Yoon SH, Lee S, Choi YH, Cheon JE. Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic Segmentation. Korean J Radiol 2023; 24:294-304. [PMID: 36907592 PMCID: PMC10067697 DOI: 10.3348/kjr.2022.0588] [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: 08/17/2022] [Revised: 12/19/2022] [Accepted: 01/24/2023] [Indexed: 03/14/2023] Open
Abstract
OBJECTIVE We aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction methods. MATERIALS AND METHODS We collected contrast-enhanced dual-energy CT of the abdomen that was obtained using various reconstruction methods, including filtered back projection, iterative reconstruction, optimum contrast, and monoenergetic images with 40, 60, and 80 keV. A deep learning based image conversion algorithm was developed to standardize the CT images using 142 CT examinations (128 for training and 14 for tuning). A separate set of 43 CT examinations from 42 patients (mean age, 10.1 years) was used as the test data. A commercial software program (MEDIP PRO v2.0.0.0, MEDICALIP Co. Ltd.) based on 2D U-NET was used to create liver segmentation masks with liver volume. The original 80 keV images were used as the ground truth. We used the paired t-test to compare the segmentation performance in the Dice similarity coefficient (DSC) and difference ratio of the liver volume relative to the ground truth volume before and after image standardization. The concordance correlation coefficient (CCC) was used to assess the agreement between the segmented liver volume and ground-truth volume. RESULTS The original CT images showed variable and poor segmentation performances. The standardized images achieved significantly higher DSCs for liver segmentation than the original images (DSC [original, 5.40%-91.27%] vs. [standardized, 93.16%-96.74%], all P < 0.001). The difference ratio of liver volume also decreased significantly after image conversion (original, 9.84%-91.37% vs. standardized, 1.99%-4.41%). In all protocols, CCCs improved after image conversion (original, -0.006-0.964 vs. standardized, 0.990-0.998). CONCLUSION Deep learning-based CT image standardization can improve the performance of automated hepatic segmentation using CT images reconstructed using various methods. Deep learning-based CT image conversion may have the potential to improve the generalizability of the segmentation network.
Collapse
Affiliation(s)
- Seul Bi Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Youngtaek Hong
- CONNECT-AI R&D Center, Yonsei University College of Medicine, Seoul, Korea
| | - Yeon Jin Cho
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
| | - Dawun Jeong
- CONNECT-AI R&D Center, Yonsei University College of Medicine, Seoul, Korea.,Brain Korea, 21 PLUS Project for Medical Science, Yonsei University, Seoul, Korea
| | - Jina Lee
- CONNECT-AI R&D Center, Yonsei University College of Medicine, Seoul, Korea.,Brain Korea, 21 PLUS Project for Medical Science, Yonsei University, Seoul, Korea
| | - Soon Ho Yoon
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.,MEDICALIP Co. Ltd., Seoul, Korea
| | - Seunghyun Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Young Hun Choi
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Jung-Eun Cheon
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
| |
Collapse
|
12
|
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.
Collapse
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
| |
Collapse
|
13
|
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.
Collapse
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
| |
Collapse
|
14
|
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.
Collapse
|
15
|
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.
Collapse
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
| |
Collapse
|
16
|
Wu K, Gu D, Qi P, Cao X, Wu D, Chen L, Qu G, Wang J, Pan X, Wang X, Chen Y, Chen L, Xue Z, Lyu J. Evaluation of an automated intracranial aneurysm detection and rupture analysis approach using cascade detection and classification networks. Comput Med Imaging Graph 2022; 102:102126. [PMID: 36242993 DOI: 10.1016/j.compmedimag.2022.102126] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 09/05/2022] [Accepted: 09/30/2022] [Indexed: 12/05/2022]
Abstract
Intracranial aneurysm is commonly found in human brains especially for the elderly, and its rupture accounts for a high rate of subarachnoid hemorrhages. However, it is time-consuming and requires special expertise to pinpoint small aneurysms from computed tomography angiography (CTA) images. Deep learning-based detection has helped improve much efficiency but false-positives still render difficulty to be ruled out. To study the feasibility of deep learning algorithms for aneurysm analysis in clinical applications, this paper proposes a pipeline for aneurysm detection, segmentation, and rupture classification and validates its performance using CTA images of 1508 subjects. A cascade aneurysm detection model is employed by first using a fine-tuned feature pyramid network (FPN) for candidate detection and then applying a dual-channel ResNet aneurysm classifier to further reduce false positives. Detected aneurysms are then segmented by applying a traditional 3D V-Net to their image patches. Radiomics features of aneurysms are extracted after detection and segmentation. The machine-learning-based and deep learning-based rupture classification can be used to distinguish ruptured and un-ruptured ones. Experimental results show that the dual-channel ResNet aneurysm classifier utilizing image and vesselness information helps boost sensitivity of detection compared to single image channel input. Overall, the proposed pipeline can achieve a sensitivity of 90 % for 1 false positive per image, and 95 % for 2 false positives per image. For rupture classification the area under curve (AUC) of 0.906 can be achieved for the testing dataset. The results suggest feasibility of the pipeline for potential clinical use to assist radiologists in aneurysm detection and classification of ruptured and un-ruptured aneurysms.
Collapse
Affiliation(s)
- Ke Wu
- Department of Radiology, The First Medical Center of PLA General Hospital, Beijing, China
| | - Dongdong Gu
- Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China
| | - Peihong Qi
- Zhengzhou People's Hospital, Zhengzhou, China
| | - Xiaohuan Cao
- Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China
| | - Dijia Wu
- Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China
| | - Lei Chen
- Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China
| | - Guoxiang Qu
- Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China
| | - Jiayu Wang
- Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China
| | - Xianpan Pan
- Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China
| | - Xuechun Wang
- Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China
| | - Yuntian Chen
- West China Hospital of Sichuan University, Chengdu, China
| | - Lizhou Chen
- West China Hospital of Sichuan University, Chengdu, China
| | - Zhong Xue
- Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China.
| | - Jinhao Lyu
- Department of Radiology, The First Medical Center of PLA General Hospital, Beijing, China.
| |
Collapse
|
17
|
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]
|
18
|
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.
Collapse
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
| |
Collapse
|
19
|
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.
Collapse
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
| |
Collapse
|
20
|
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]
|
21
|
Yu AC, Mohajer B, Eng J. External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review. Radiol Artif Intell 2022; 4:e210064. [PMID: 35652114 DOI: 10.1148/ryai.210064] [Citation(s) in RCA: 94] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 03/09/2022] [Accepted: 04/12/2022] [Indexed: 01/17/2023]
Abstract
Purpose To assess generalizability of published deep learning (DL) algorithms for radiologic diagnosis. Materials and Methods In this systematic review, the PubMed database was searched for peer-reviewed studies of DL algorithms for image-based radiologic diagnosis that included external validation, published from January 1, 2015, through April 1, 2021. Studies using nonimaging features or incorporating non-DL methods for feature extraction or classification were excluded. Two reviewers independently evaluated studies for inclusion, and any discrepancies were resolved by consensus. Internal and external performance measures and pertinent study characteristics were extracted, and relationships among these data were examined using nonparametric statistics. Results Eighty-three studies reporting 86 algorithms were included. The vast majority (70 of 86, 81%) reported at least some decrease in external performance compared with internal performance, with nearly half (42 of 86, 49%) reporting at least a modest decrease (≥0.05 on the unit scale) and nearly a quarter (21 of 86, 24%) reporting a substantial decrease (≥0.10 on the unit scale). No study characteristics were found to be associated with the difference between internal and external performance. Conclusion Among published external validation studies of DL algorithms for image-based radiologic diagnosis, the vast majority demonstrated diminished algorithm performance on the external dataset, with some reporting a substantial performance decrease.Keywords: Meta-Analysis, Computer Applications-Detection/Diagnosis, Neural Networks, Computer Applications-General (Informatics), Epidemiology, Technology Assessment, Diagnosis, Informatics Supplemental material is available for this article. © RSNA, 2022.
Collapse
Affiliation(s)
- Alice C Yu
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287
| | - Bahram Mohajer
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287
| | - John Eng
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287
| |
Collapse
|
22
|
Deep learning reconstruction for 1.5 T cervical spine MRI: effect on interobserver agreement in the evaluation of degenerative changes. Eur Radiol 2022; 32:6118-6125. [DOI: 10.1007/s00330-022-08729-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 02/23/2022] [Accepted: 03/07/2022] [Indexed: 12/22/2022]
|
23
|
Artificial Intelligence-Enabled Medical Analysis for Intracranial Cerebral Hemorrhage Detection and Classification. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:2017223. [PMID: 35356628 PMCID: PMC8959996 DOI: 10.1155/2022/2017223] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/24/2022] [Accepted: 02/02/2022] [Indexed: 12/24/2022]
Abstract
Intracranial hemorrhage (ICH) becomes a crucial healthcare emergency, which requires earlier detection and accurate assessment. Owing to the increased death rate (around 40%), the earlier recognition and classification of disease using computed tomography (CT) images are necessary to ensure a favourable prediction and restrain the existence of neurologic deficits. Since the manual diagnosis approach is time-consuming, automated ICH detection and classification models using artificial intelligence (AI) models are required. With this motivation, this study introduces an AI-enabled medical analysis tool for ICH detection and classification (AIMA-ICHDC) using CT images. The proposed AIMA-ICHDC technique aims at identifying the presence of ICH and identifying the different grades. In addition, the AIMA-ICHDC technique involves the design of glowworm swarm optimization with fuzzy entropy clustering (GSO-FEC) technique for the segmentation process. Besides, the VGG-19 model was executed for generating a collection of feature vectors and the optimal mixed-kernel-based extreme learning machine (OMKELM) model is utilized as a classifier. To optimally select the weight parameter of the MKELM technique, the coyote optimization algorithm (COA) was utilized. A wide range of simulation analyses are carried out under varying aspects. As part of the AIMA-ICHDC method, ICH can be detected and graded using a single sample. For segmentation, the AIMA-ICHDC technique uses the GSO-FEC method, which is the design of glowworm swarm optimization (GSO). The comparative outcomes highlighted the betterment of the AIMA-ICHDC technique compared to the recent state-of-the-art ICH classification approaches in terms of several measures.
Collapse
|
24
|
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.
Collapse
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.
| |
Collapse
|
25
|
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]
|
26
|
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.
Collapse
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
| |
Collapse
|
27
|
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]
|
28
|
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).
Collapse
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
| |
Collapse
|
29
|
Decuyper M, Maebe J, Van Holen R, Vandenberghe S. Artificial intelligence with deep learning in nuclear medicine and radiology. EJNMMI Phys 2021; 8:81. [PMID: 34897550 PMCID: PMC8665861 DOI: 10.1186/s40658-021-00426-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 11/19/2021] [Indexed: 12/19/2022] Open
Abstract
The use of deep learning in medical imaging has increased rapidly over the past few years, finding applications throughout the entire radiology pipeline, from improved scanner performance to automatic disease detection and diagnosis. These advancements have resulted in a wide variety of deep learning approaches being developed, solving unique challenges for various imaging modalities. This paper provides a review on these developments from a technical point of view, categorizing the different methodologies and summarizing their implementation. We provide an introduction to the design of neural networks and their training procedure, after which we take an extended look at their uses in medical imaging. We cover the different sections of the radiology pipeline, highlighting some influential works and discussing the merits and limitations of deep learning approaches compared to other traditional methods. As such, this review is intended to provide a broad yet concise overview for the interested reader, facilitating adoption and interdisciplinary research of deep learning in the field of medical imaging.
Collapse
Affiliation(s)
- Milan Decuyper
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Jens Maebe
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Roel Van Holen
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Stefaan Vandenberghe
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| |
Collapse
|
30
|
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.
Collapse
|
31
|
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.
Collapse
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
| |
Collapse
|
32
|
Sohn B, Park KY, Choi J, Koo JH, Han K, Joo B, Won SY, Cha J, Choi HS, Lee SK. Deep Learning-Based Software Improves Clinicians' Detection Sensitivity of Aneurysms on Brain TOF-MRA. AJNR Am J Neuroradiol 2021; 42:1769-1775. [PMID: 34385143 DOI: 10.3174/ajnr.a7242] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 05/05/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE The detection of cerebral aneurysms on MRA is a challenging task. Recent studies have used deep learning-based software for automated detection of aneurysms on MRA and have reported high performance. The purpose of this study was to evaluate the incremental value of using deep learning-based software for the detection of aneurysms on MRA by 2 radiologists, a neurosurgeon, and a neurologist. MATERIALS AND METHODS TOF-MRA examinations of intracranial aneurysms were retrospectively extracted. Four physicians interpreted the MRA blindly. After a washout period, they interpreted MRA again using the software. Sensitivity and specificity per patient, sensitivity per lesion, and the number of false-positives per case were measured. Diagnostic performances, including subgroup analysis of lesions, were compared. Logistic regression with a generalized estimating equation was used. RESULTS A total of 332 patients were evaluated; 135 patients had positive findings with 169 lesions. With software assistance, patient-based sensitivity was statistically improved after the washout period (73.5% versus 86.5%, P < .001). The neurosurgeon and neurologist showed a significant increase in patient-based sensitivity with software assistance (74.8% versus 85.2%, P = .03, and 56.3% versus 84.4%, P < .001, respectively), while the number of false-positive cases did not increase significantly (23 versus 30, P = .20, and 22 versus 24, P = .75, respectively). CONCLUSIONS Software-aided reading showed significant incremental value in the sensitivity of clinicians in the detection of aneurysms on MRA without a significant increase in false-positive findings, especially for the neurosurgeon and neurologist. Software-aided reading showed equivocal value for the radiologist.
Collapse
Affiliation(s)
- B Sohn
- From the Department of Radiology (B.S., J.C., J.H.K., K.H., B.J., S.Y.W., J.C., H.S.C., S.-K.L.), Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - K-Y Park
- Department of Neurosurgery (K.-Y.P.), Yonsei University College of Medicine, Seoul, South Korea
| | - J Choi
- From the Department of Radiology (B.S., J.C., J.H.K., K.H., B.J., S.Y.W., J.C., H.S.C., S.-K.L.), Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
- Department of Neurology (J.C.), Yonsei University College of Medicine, Seoul, South Korea
- Department of Neurology (J.C.), Seoul Medical Center, Seoul, South Korea
| | - J H Koo
- From the Department of Radiology (B.S., J.C., J.H.K., K.H., B.J., S.Y.W., J.C., H.S.C., S.-K.L.), Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - K Han
- From the Department of Radiology (B.S., J.C., J.H.K., K.H., B.J., S.Y.W., J.C., H.S.C., S.-K.L.), Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - B Joo
- From the Department of Radiology (B.S., J.C., J.H.K., K.H., B.J., S.Y.W., J.C., H.S.C., S.-K.L.), Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - S Y Won
- From the Department of Radiology (B.S., J.C., J.H.K., K.H., B.J., S.Y.W., J.C., H.S.C., S.-K.L.), Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - J Cha
- From the Department of Radiology (B.S., J.C., J.H.K., K.H., B.J., S.Y.W., J.C., H.S.C., S.-K.L.), Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - H S Choi
- From the Department of Radiology (B.S., J.C., J.H.K., K.H., B.J., S.Y.W., J.C., H.S.C., S.-K.L.), Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
- Department of Radiology (H.S.C.), Seoul Medical Center, Seoul, South Korea
| | - S-K Lee
- From the Department of Radiology (B.S., J.C., J.H.K., K.H., B.J., S.Y.W., J.C., H.S.C., S.-K.L.), Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| |
Collapse
|
33
|
Nomura Y, Hanaoka S, Nakao T, Hayashi N, Yoshikawa T, Miki S, Watadani T, Abe O. Performance changes due to differences in training data for cerebral aneurysm detection in head MR angiography images. Jpn J Radiol 2021; 39:1039-1048. [PMID: 34125368 DOI: 10.1007/s11604-021-01153-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 06/08/2021] [Indexed: 01/10/2023]
Abstract
PURPOSE The performance of computer-aided detection (CAD) software depends on the quality and quantity of the dataset used for machine learning. If the data characteristics in development and practical use are different, the performance of CAD software degrades. In this study, we investigated changes in detection performance due to differences in training data for cerebral aneurysm detection software in head magnetic resonance angiography images. MATERIALS AND METHODS We utilized three types of CAD software for cerebral aneurysm detection in MRA images, which were based on 3D local intensity structure analysis, graph-based features, and convolutional neural network. For each type of CAD software, we compared three types of training pattern, which were two types of training using single-site data and one type of training using multisite data. We also carried out internal and external evaluations. RESULTS In training using single-site data, the performance of CAD software largely and unpredictably fluctuated when the training dataset was changed. Training using multisite data did not show the lowest performance among the three training patterns for any CAD software and dataset. CONCLUSION The training of cerebral aneurysm detection software using data collected from multiple sites is desirable to ensure the stable performance of the software.
Collapse
Affiliation(s)
- Yukihiro Nomura
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
| | - Shouhei Hanaoka
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Takahiro Nakao
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Naoto Hayashi
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Takeharu Yoshikawa
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Soichiro Miki
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Takeyuki Watadani
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Osamu Abe
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| |
Collapse
|
34
|
Ahn JH, Kim HC, Rhim JK, Park JJ, Sigmund D, Park MC, Jeong JH, Jeon JP. Multi-View Convolutional Neural Networks in Rupture Risk Assessment of Small, Unruptured Intracranial Aneurysms. J Pers Med 2021; 11:jpm11040239. [PMID: 33805171 PMCID: PMC8064331 DOI: 10.3390/jpm11040239] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 03/17/2021] [Accepted: 03/23/2021] [Indexed: 12/29/2022] Open
Abstract
Auto-detection of cerebral aneurysms via convolutional neural network (CNN) is being increasingly reported. However, few studies to date have accurately predicted the risk, but not the diagnosis itself. We developed a multi-view CNN for the prediction of rupture risk involving small unruptured intracranial aneurysms (UIAs) based on three-dimensional (3D) digital subtraction angiography (DSA). The performance of a multi-view CNN-ResNet50 in accurately predicting the rupture risk (high vs. non-high) of UIAs in the anterior circulation measuring less than 7 mm in size was compared with various CNN architectures (AlexNet and VGG16), with similar type but different layers (ResNet101 and ResNet152), and single image-based CNN (single-view ResNet50). The sensitivity, specificity, and overall accuracy of risk prediction were estimated and compared according to CNN architecture. The study included 364 UIAs in training and 93 in test datasets. A multi-view CNN-ResNet50 exhibited a sensitivity of 81.82 (66.76–91.29)%, a specificity of 81.63 (67.50–90.76)%, and an overall accuracy of 81.72 (66.98–90.92)% for risk prediction. AlexNet, VGG16, ResNet101, ResNet152, and single-view CNN-ResNet50 showed similar specificity. However, the sensitivity and overall accuracy were decreased (AlexNet, 63.64% and 76.34%; VGG16, 68.18% and 74.19%; ResNet101, 68.18% and 73.12%; ResNet152, 54.55% and 72.04%; and single-view CNN-ResNet50, 50.00% and 64.52%) compared with multi-view CNN-ResNet50. Regarding F1 score, it was the highest in multi-view CNN-ResNet50 (80.90 (67.29–91.81)%). Our study suggests that multi-view CNN-ResNet50 may be feasible to assess the rupture risk in small-sized UIAs.
Collapse
Affiliation(s)
- Jun Hyong Ahn
- Department of Neurosurgery, College of Medicine, Hallym University, Chuncheon 24252, Korea;
| | - Heung Cheol Kim
- Department of Radioilogy, College of Medicine, Hallym University, Chuncheon 24252, Korea;
| | - Jong Kook Rhim
- Department of Neurosurgery, College of Medicine, Jeju National University, Jeju 63243, Korea;
| | - Jeong Jin Park
- Department of Neurology, Konkuk University Medical Center, Seoul 05030, Korea;
| | - Dick Sigmund
- AIDOT Inc., Seoul 05854, Korea; (D.S.); (M.C.P.); (J.H.J.)
| | - Min Chan Park
- AIDOT Inc., Seoul 05854, Korea; (D.S.); (M.C.P.); (J.H.J.)
| | - Jae Hoon Jeong
- AIDOT Inc., Seoul 05854, Korea; (D.S.); (M.C.P.); (J.H.J.)
| | - Jin Pyeong Jeon
- Department of Neurosurgery, College of Medicine, Hallym University, Chuncheon 24252, Korea;
- Genetic and Research Inc., Chuncheon 24252, Korea
- Correspondence: ; Tel.: +82-33-240-5171
| |
Collapse
|
35
|
Senbekov M, Saliev T, Bukeyeva Z, Almabayeva A, Zhanaliyeva M, Aitenova N, Toishibekov Y, Fakhradiyev I. The Recent Progress and Applications of Digital Technologies in Healthcare: A Review. Int J Telemed Appl 2020; 2020:8830200. [PMID: 33343657 PMCID: PMC7732404 DOI: 10.1155/2020/8830200] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 11/16/2020] [Accepted: 11/17/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The implementation of medical digital technologies can provide better accessibility and flexibility of healthcare for the public. It encompasses the availability of open information on the health, treatment, complications, and recent progress on biomedical research. At present, even in low-income countries, diagnostic and medical services are becoming more accessible and available. However, many issues related to digital health technologies remain unmet, including the reliability, safety, testing, and ethical aspects. PURPOSE The aim of the review is to discuss and analyze the recent progress on the application of big data, artificial intelligence, telemedicine, block-chain platforms, smart devices in healthcare, and medical education. Basic Design. The publication search was carried out using Google Scholar, PubMed, Web of Sciences, Medline, Wiley Online Library, and CrossRef databases. The review highlights the applications of artificial intelligence, "big data," telemedicine and block-chain technologies, and smart devices (internet of things) for solving the real problems in healthcare and medical education. Major Findings. We identified 252 papers related to the digital health area. However, the number of papers discussed in the review was limited to 152 due to the exclusion criteria. The literature search demonstrated that digital health technologies became highly sought due to recent pandemics, including COVID-19. The disastrous dissemination of COVID-19 through all continents triggered the need for fast and effective solutions to localize, manage, and treat the viral infection. In this regard, the use of telemedicine and other e-health technologies might help to lessen the pressure on healthcare systems. Summary. Digital platforms can help optimize diagnosis, consulting, and treatment of patients. However, due to the lack of official regulations and recommendations, the stakeholders, including private and governmental organizations, are facing the problem with adequate validation and approbation of novel digital health technologies. In this regard, proper scientific research is required before a digital product is deployed for the healthcare sector.
Collapse
Affiliation(s)
- Maksut Senbekov
- S.D. Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
| | - Timur Saliev
- S.D. Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
| | | | | | | | - Nazym Aitenova
- NJSC “Astana Medical University”, Nur-Sultan, Kazakhstan
| | | | - Ildar Fakhradiyev
- S.D. Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
| |
Collapse
|