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Claux F, Baudouin M, Bogey C, Rouchaud A. Dense, deep learning-based intracranial aneurysm detection on TOF MRI using two-stage regularized U-Net. J Neuroradiol 2023; 50:9-15. [PMID: 35307554 DOI: 10.1016/j.neurad.2022.03.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 03/11/2022] [Accepted: 03/11/2022] [Indexed: 01/31/2023]
Abstract
BACKGROUND AND PURPOSE The prevalence of unruptured intracranial aneurysms in the general population is high and aneurysms are usually asymptomatic. Their diagnosis is often fortuitous on MRI and might be difficult and time consuming for the radiologist. The purpose of this study was to develop a deep learning neural network tool for automated segmentation of intracranial arteries and automated detection of intracranial aneurysms from 3D time-of-flight magnetic resonance angiography (TOF-MRA). MATERIALS AND METHODS 3D TOF-MRA with aneurysms were retrospectively extracted. All were confirmed with angiography. The data were divided into two sets: a training set of 24 examinations and a test set of 25 examinations. Manual annotations of intracranial blood vessels and aneurysms were performed by neuroradiologists. A double convolutional neuronal network based on the U-Net architecture with regularization was used to increase performance despite a small amount of training data. The performance was evaluated for the test set. Subgroup analyses according to size and location of aneurysms were performed. RESULTS The average processing time was 15 min. Overall, the sensitivity and the positive predictive value of the proposed algorithm were 78% (21 of 27; 95% CI: 62-94) and 62% (21 of 34; 95%CI: 46-78) respectively, with 0.5 FP/case. Despite gradual improvement in sensitivity regarding aneurysm size, there was no significant difference of sensitivity detection between subgroups of size and location. CONCLUSIONS This developed tool based on a double CNN with regularization trained with small dataset, enables accurate intracranial arteries segmentation as well as effective aneurysm detection on 3D TOF MRA.
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Affiliation(s)
- Frédéric Claux
- Univ. Limoges, CNRS, XLIM, UMR 7252, F-87000 Limoges, France.
| | - Maxime Baudouin
- Limoges university hospital, Department of radiology, Limoges, France.
| | - Clément Bogey
- Limoges university hospital, Department of radiology, Limoges, France
| | - Aymeric Rouchaud
- Univ. Limoges, CNRS, XLIM, UMR 7252, F-87000 Limoges, France; Limoges university hospital, Department of radiology, Limoges, France
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How feasible is end-to-end deep learning for clinical neuroimaging? J Neuroradiol 2022; 49:399-400. [DOI: 10.1016/j.neurad.2022.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 10/09/2022] [Indexed: 11/06/2022]
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Zhong YW, Jiang Y, Dong S, Wu WJ, Wang LX, Zhang J, Huang MW. Tumor radiomics signature for artificial neural network-assisted detection of neck metastasis in patient with tongue cancer. J Neuroradiol 2021; 49:213-218. [PMID: 34358534 DOI: 10.1016/j.neurad.2021.07.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 07/23/2021] [Accepted: 07/23/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND AND PURPOSE To determine the neck management of tongue cancer, this study attempted to construct an artificial neural network (ANN)-assisted model based on computed tomography (CT) radiomics of primary tumors to predict neck lymph node (LN) status in patients with tongue squamous cell carcinoma (SCC). MATERIALS AND METHODS Three hundred thirteen patients with tongue SCC were retrospectively included and randomly divided into training (60%), validation (20%) and internally independent test (20%) sets. In total, 1673 feature values were extracted after the semiautomatic segmentation of primary tumors and set as input layers of a classical 3-layer ANN incorporated with or without clinical LN (cN) status after dimension reduction. The receiver operating characteristic (ROC) curve, accuracy (ACC), sensitivity (SEN), specificity (SPE), area under curve (AUC) and Net Reclassification Index (NRI), were used to evaluate and compare the models. RESULTS Four models with different settings were constructed. The ACC, SEN, SPE and AUC reached 84.1%, 93.1%, 76.5% and 0.943 (95% confidence interval: 0.891-0.996, p<.001), respectively, in the test set. The NRI of models compared with radiologists reached 40% (p<.001). The occult nodal metastasis rate was reduced from 30.9% to a minimum of 12.7% in the T1-2 group. CONCLUSION ANN-based models that incorporated CT radiomics of primary tumors with traditional LN evaluation were constructed and validated to more precisely predict neck LN metastasis in patients with tongue SCC than with naked eyes, especially in early-stage cancer.
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Affiliation(s)
- Yi-Wei Zhong
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, PR China
| | - Yin Jiang
- Department of Physics, Beihang University, Beijing, PR China; Beijing Advanced Innovation Center for Big Data-based Precision Medicine, Beihang University, Beijing, PR China
| | - Shuang Dong
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, PR China
| | - Wen-Jie Wu
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, PR China.
| | - Ling-Xiao Wang
- Department of Physics, Tsinghua University, Beijing, PR China; Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
| | - Jie Zhang
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, PR China
| | - Ming-Wei Huang
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, PR China
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Shafaat O, Bernstock JD, Shafaat A, Yedavalli VS, Elsayed G, Gupta S, Sotoudeh E, Sair HI, Yousem DM, Sotoudeh H. Leveraging artificial intelligence in ischemic stroke imaging. J Neuroradiol 2021; 49:343-351. [PMID: 33984377 DOI: 10.1016/j.neurad.2021.05.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 05/02/2021] [Accepted: 05/03/2021] [Indexed: 11/30/2022]
Abstract
Artificial intelligence (AI) is having a disruptive and transformative effect on clinical medicine. Prompt clinical diagnosis and imaging are critical for minimizing the morbidity and mortality associated with ischemic strokes. Clinicians must understand the current strengths and limitations of AI to provide optimal patient care. Ischemic stroke is one of the medical fields that have been extensively evaluated by artificial intelligence. Presented herein is a review of artificial intelligence applied to clinical management of stroke, geared toward clinicians. In this review, we explain the basic concept of AI and machine learning. This review is without coding and mathematical details and targets the clinicians involved in stroke management without any computer or mathematics' background. Here the AI application in ischemic stroke is summarized and classified into stroke imaging (automated diagnosis of brain infarction, automated ASPECT score calculation, infarction segmentation), prognosis prediction, and patients' selection for treatment.
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Affiliation(s)
- Omid Shafaat
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287, USA.
| | - Joshua D Bernstock
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Hale Building, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Amir Shafaat
- Department of Mechanical Engineering, Arak University of Technology, Daneshgah St, 38181-41167 Arak, Iran.
| | - Vivek S Yedavalli
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287, USA.
| | - Galal Elsayed
- Department of Neurosurgery, University of Alabama at Birmingham, 1960 6th Ave. S., Birmingham, AL 35233, USA.
| | - Saksham Gupta
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Hale Building, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Ehsan Sotoudeh
- Department of Surgery, Iranian Hospital in Dubai, P.O.BOX: 2330, Al-Wasl Road, Dubai 2330, UAE.
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287, USA; Radiology Artificial Intelligence Lab (RAIL), Malone Center for Engineering in Healthcare, Johns Hopkins University Whiting School of Engineering, 600 North Wolfe Street, Baltimore, MD 21287, USA.
| | - David M Yousem
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287, USA.
| | - Houman Sotoudeh
- Department of Radiology, University of Alabama at Birmingham, 619 19th St S, Birmingham, AL 35294, USA.
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