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Timmins KM, Schaaf ICVD, Vos IN, Ruigrok YM, Velthuis BK, Kuijf HJ. Geometric Deep Learning Using Vascular Surface Meshes for Modality-Independent Unruptured Intracranial Aneurysm Detection. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3451-3460. [PMID: 37347626 DOI: 10.1109/tmi.2023.3288746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
Early detection of unruptured intracranial aneurysms (UIAs) enables better rupture risk and preventative treatment assessment. UIAs are usually diagnosed on Time-of-Flight Magnetic Resonance Angiographs (TOF-MRA) or contrast-enhanced Computed Tomography Angiographs (CTA). Various automatic voxel-based deep learning UIA detection methods have been developed, but these are limited to a single modality. We propose a modality-independent UIA detection method using a geometric deep learning model with high resolution surface meshes of brain vessels. A mesh convolutional neural network with ResU-Net style architecture was used. UIA detection performance was investigated with different input and pooling mesh resolutions, and including additional edge input features (shape index and curvedness). Both a higher resolution mesh (15,000 edges) and additional curvature edge features improved performance (average sensitivity: 65.6%, false positive count/image (FPC/image): 1.61). UIAs were detected in an independent TOF-MRA test set and a CTA test set with average sensitivity of 52.0% and 48.3% and average FPC/image of 1.04 and 1.05 respectively. We provide modality-independent UIA detection using a deep-learning vascular surface mesh model with comparable performance to state-of-the-art UIA detection methods.
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Tajima T, Akai H, Yasaka K, Kunimatsu A, Yoshioka N, Akahane M, Ohtomo K, Abe O, Kiryu S. Comparison of 1.5 T and 3 T magnetic resonance angiography for detecting cerebral aneurysms using deep learning-based computer-assisted detection software. Neuroradiology 2023; 65:1473-1482. [PMID: 37646791 DOI: 10.1007/s00234-023-03216-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 08/22/2023] [Indexed: 09/01/2023]
Abstract
PURPOSE To compare the diagnostic performance of 1.5 T versus 3 T magnetic resonance angiography (MRA) for detecting cerebral aneurysms with clinically available deep learning-based computer-assisted detection software (EIRL aneurysm® [EIRL_an]), which has been approved by the Japanese Pharmaceuticals and Medical Devices Agency. We also sought to analyze the causes of potential false positives. METHODS In this single-center, retrospective study, we evaluated the MRA scans of 90 patients who underwent head MRA (1.5 T and 3 T in 45 patients each) in clinical practice. Overall, 51 patients had 70 aneurysms. We used MRI from a vendor not included in the dataset used to create the EIRL_an algorithm. Two radiologists determined the ground truth, the accuracy of the candidates noted by EIRL_an, and the causes of false positives. The sensitivity, number of false positives per case (FPs/case), and the causes of false positives were compared between 1.5 T and 3 T MRA. Pearson's χ2 test, Fisher's exact test, and the Mann‒Whitney U test were used for the statistical analyses as appropriate. RESULTS The sensitivity was high for 1.5 T and 3 T MRA (0.875‒1), but the number of FPs/case was significantly higher with 3 T MRA (1.511 vs. 2.578, p < 0.001). The most common causes of false positives (descending order) were the origin/bifurcation of vessels/branches, flow-related artifacts, and atherosclerosis and were similar between 1.5 T and 3 T MRA. CONCLUSION EIRL_an detected significantly more false-positive lesions with 3 T than with 1.5 T MRA in this external validation study. Our data may help physicians with limited experience with MRA to correctly diagnose aneurysms using EIRL_an.
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Affiliation(s)
- Taku Tajima
- Department of Radiology, International University of Health and Welfare Mita Hospital, 1-4-3 Mita, Minato-Ku, Tokyo, 108-8329, Japan
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan
| | - Hiroyuki Akai
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-Ku, Tokyo, 108-8639, Japan
| | - Koichiro Yasaka
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan
| | - Akira Kunimatsu
- Department of Radiology, International University of Health and Welfare Mita Hospital, 1-4-3 Mita, Minato-Ku, Tokyo, 108-8329, Japan
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan
| | - Naoki Yoshioka
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan
| | - Masaaki Akahane
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan
| | - Kuni Ohtomo
- International University of Health and Welfare, 2600-1 Kitakanamaru, Otawara, Tochigi, 324-8501, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan
| | - Shigeru Kiryu
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan.
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Kuwabara M, Ikawa F, Sakamoto S, Okazaki T, Ishii D, Hosogai M, Maeda Y, Chiku M, Kitamura N, Choppin A, Takamiya D, Shimahara Y, Nakayama T, Kurisu K, Horie N. Effectiveness of tuning an artificial intelligence algorithm for cerebral aneurysm diagnosis: a study of 10,000 consecutive cases. Sci Rep 2023; 13:16202. [PMID: 37758849 PMCID: PMC10533861 DOI: 10.1038/s41598-023-43418-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 09/23/2023] [Indexed: 09/29/2023] Open
Abstract
Diagnostic image analysis for unruptured cerebral aneurysms using artificial intelligence has a very high sensitivity. However, further improvement is needed because of a relatively high number of false positives. This study aimed to confirm the clinical utility of tuning an artificial intelligence algorithm for cerebral aneurysm diagnosis. We extracted 10,000 magnetic resonance imaging scans of participants who underwent brain screening using the "Brain Dock" system. The sensitivity and false positives/case for aneurysm detection were compared before and after tuning the algorithm. The initial diagnosis included only cases for which feedback to the algorithm was provided. In the primary analysis, the sensitivity of aneurysm diagnosis decreased from 96.5 to 90% and the false positives/case improved from 2.06 to 0.99 after tuning the algorithm (P < 0.001). In the secondary analysis, the sensitivity of aneurysm diagnosis decreased from 98.8 to 94.6% and the false positives/case improved from 1.99 to 1.03 after tuning the algorithm (P < 0.001). The false positives/case reduced without a significant decrease in sensitivity. Using large clinical datasets, we demonstrated that by tuning the algorithm, we could significantly reduce false positives with a minimal decline in sensitivity.
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Affiliation(s)
- Masashi Kuwabara
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Fusao Ikawa
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan.
- Department of Neurosurgery, Shimane Prefectural Central Hospital, 4-1-1 Himebara, Izumo, Shimane, 693-8555, Japan.
| | - Shigeyuki Sakamoto
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Takahito Okazaki
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Daizo Ishii
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Masahiro Hosogai
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Yuyo Maeda
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Masaaki Chiku
- Department of Neurosurgery, Medical Check Studio, Tokyo Ginza Clinic, 1-2-4 Ginza, Chuo-ku, Tokyo, 104-0061, Japan
| | - Naoyuki Kitamura
- Department of Diagnostic Radiology, Kasumi Clinic, 1-2-27 Shinonomehommachi, Minami-ku, Hiroshima, Hiroshima, 734-0023, Japan
| | - Antoine Choppin
- LPIXEL Inc., 1-6-1 Otemachi, Chiyoda-ku, Tokyo, 100-0004, Japan
| | | | - Yuki Shimahara
- LPIXEL Inc., 1-6-1 Otemachi, Chiyoda-ku, Tokyo, 100-0004, Japan
| | - Takeo Nakayama
- Department of Health Informatics, School of Public Health, Graduate School of Medicine, Kyoto University, Yoshida-Konoe, Sakyo-ku, Kyoto, Kyoto, 606-8501, Japan
| | - Kaoru Kurisu
- Chugoku Rosai Hospital, 1-5-1 Hirotagaya, Kure, Hiroshima, 737-0193, Japan
| | - Nobutaka Horie
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan
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Detection of intracranial aneurysms using deep learning-based CAD system: usefulness of the scores of CNN's final layer for distinguishing between aneurysm and infundibular dilatation. Jpn J Radiol 2023; 41:131-141. [PMID: 36173510 PMCID: PMC9889446 DOI: 10.1007/s11604-022-01341-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 09/12/2022] [Indexed: 02/04/2023]
Abstract
PURPOSE We evaluated the diagnostic performance of a clinically available deep learning-based computer-assisted diagnosis software for detecting unruptured aneurysms (UANs) using magnetic resonance angiography and assessed the functionality of the convolutional neural network (CNN) final layer score for distinguishing between UAN and infundibular dilatation (ID). MATERIALS AND METHODS EIRL brain aneurysm (EIRL_BA) was used in this study. The subjects were 117 UAN and/or ID cases including 100 UAN lesions (average sizes of 2.56 ± 1.45 mm) and 40 ID lesions (average sizes of 1.75 ± 0.41 mm) in any of internal carotid artery, middle cerebral artery, and anterior communicating artery, and 123 normal controls. The sensitivity, specificity, and accuracy of EIRL_BA were determined for UAN and ID or UAN only. Furthermore, the relationship between the lesion category and score was examined using a linear regression analysis model, and the receiver operating characteristic (ROC) analysis was used to assess whether the scores represent UAN-like characteristics. RESULTS EIRL_BA showed a total of 203 candidates (an average of 1.73/case) in UAN and/or ID cases and 98 candidates (an average of 0.80/case) in normal controls. For diagnosing either UAN/ID, EIRL_BA showed an overall sensitivity of 80%, specificity of 84.2%, and accuracy of 83.7%, resulting in the positive likelihood ratio of 5.0. For diagnosing UAN only, the overall sensitivity of 89.0, specificity of 82.6%, and accuracy of 83.2% resulting in the positive likelihood ratio of 5.1. In a linear regression analysis, the scores significantly increased in the candidates' first and second ranks in UAN (p < 0.05) but not in ID. An ROC analysis using the score for diagnosing UAN showed an area under the curve of 0.836. CONCLUSION EIRL_BA is applicable for detecting small UAN, and the CNN's final layer scores may be an effective index for discriminating UAN and ID and representing the likelihood of UAN.
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Shimada Y, Tanimoto T, Nishimori M, Choppin A, Meir A, Ozaki A, Higuchi A, Kosaka M, Shimahara Y, Kitamura N. Incidental cerebral aneurysms detected by a computer-assisted detection system based on artificial intelligence: A case series. Medicine (Baltimore) 2020; 99:e21518. [PMID: 33120725 PMCID: PMC7581039 DOI: 10.1097/md.0000000000021518] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
RATIONALE Computer-assisted detection (CAD) systems based on artificial intelligence (AI) using convolutional neural network (CNN) have been successfully used for the diagnosis of unruptured cerebral aneurysms in experimental situations. However, it is yet unclear whether CAD systems can detect cerebral aneurysms effectively in real-life clinical situations. This paper describes the diagnostic efficacy of CAD systems for cerebral aneurysms and the types of cerebral aneurysms that they can detect. PATIENT CONCERNS From March 7, 2017 to August 26, 2018 we performed brain magnetic resonance imaging (MRI) scans for 1623 subjects, to rule out intracranial diseases. We retrospectively reviewed the medical records including the history and images for each patient. DIAGNOSES, INTERVENTIONS AND OUTCOMES Among them, we encountered 5 cases in whom the cerebral aneurysms had been overlooked in the first and second round of imaging, and were detected for the first time by CAD. All missed aneurysms were less than 2 mm in diameter. Of the 5 aneurysms, 2 were internal carotid artery (ICA) paraclinoid aneurysms, 2 were Internal carotid-posterior communicating artery (IC-PC) aneurysms and 1 was a distal middle cerebral artery (MCA) aneurysm. LESSONS Our CAD system can detect very small aneurysms masked by the surrounding arteries and difficult for radiologists to detect. In the future, CAD systems might pave the way to substitute the workload of diagnostic radiologists and reduce the cost of human labor.
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Affiliation(s)
- Yuki Shimada
- MNES Inc, Hiroshima
- Minamisoma Municipal General Hospital, Minamisoma
- Department of Neurosurgery, Fukushima Medical University, Fukushima
- Medical Governance Research Institute, Tokyo
- Kasumi Clinic, Hiroshima
| | - Tetsuya Tanimoto
- MNES Inc, Hiroshima
- Medical Governance Research Institute, Tokyo
- Kasumi Clinic, Hiroshima
| | | | | | - Arie Meir
- Google Inc, Mountain View, California, USA
- Department of Breast Surgery, Jyoban Hospital of Tokiwa Foundation, Fukushima, Japan
| | - Akihiko Ozaki
- MNES Inc, Hiroshima
- Medical Governance Research Institute, Tokyo
- Kasumi Clinic, Hiroshima
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Ueda D, Yamamoto A, Nishimori M, Shimono T, Doishita S, Shimazaki A, Katayama Y, Fukumoto S, Choppin A, Shimahara Y, Miki Y. Deep Learning for MR Angiography: Automated Detection of Cerebral Aneurysms. Radiology 2018; 290:187-194. [PMID: 30351253 DOI: 10.1148/radiol.2018180901] [Citation(s) in RCA: 108] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Purpose To develop and evaluate a supportive algorithm using deep learning for detecting cerebral aneurysms at time-of-flight MR angiography to provide a second assessment of images already interpreted by radiologists. Materials and Methods MR images reported by radiologists to contain aneurysms were extracted from four institutions for the period from November 2006 through October 2017. The images were divided into three data sets: training data set, internal test data set, and external test data set. The algorithm was constructed by deep learning with the training data set, and its sensitivity to detect aneurysms in the test data sets was evaluated. To find aneurysms that had been overlooked in the initial reports, two radiologists independently performed a blinded interpretation of aneurysm candidates detected by the algorithm. When there was disagreement, the final diagnosis was made in consensus. The number of newly detected aneurysms was also evaluated. Results The training data set, which provided training and validation data, included 748 aneurysms (mean size, 3.1 mm ± 2.0 [standard deviation]) from 683 examinations; 318 of these examinations were on male patients (mean age, 63 years ± 13) and 365 were on female patients (mean age, 64 years ± 13). Test data were provided by the internal test data set (649 aneurysms [mean size, 4.1 mm ± 3.2] in 521 examinations, including 177 male patients and 344 female patients with mean age of 66 years ± 12 and 67 years ± 13, respectively) and the external test data set (80 aneurysms [mean size, 4.1 mm ± 2.1] in 67 examinations, including 19 male patients and 48 female patients with mean age of 63 years ± 12 and 68 years ± 12, respectively). The sensitivity was 91% (592 of 649) and 93% (74 of 80) for the internal and external test data sets, respectively. The algorithm improved aneurysm detection in the internal and external test data sets by 4.8% (31 of 649) and 13% (10 of 80), respectively, compared with the initial reports. Conclusion A deep learning algorithm detected cerebral aneurysms in radiologic reports with high sensitivity and improved aneurysm detection compared with the initial reports. © RSNA, 2018 See also the editorial by Flanders in this issue.
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Affiliation(s)
- Daiju Ueda
- From the Department of Diagnostic and Interventional Radiology (D.U., A.Y., T.S., S.D., A.S., Y.M.) and Department of Premier Preventive Medicine (S.F.), Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan; LPixel, Tokyo, Japan (M.N., A.C., Y.S.); and Department of Radiology, Osaka City University Hospital, Osaka, Japan (Y.K.)
| | - Akira Yamamoto
- From the Department of Diagnostic and Interventional Radiology (D.U., A.Y., T.S., S.D., A.S., Y.M.) and Department of Premier Preventive Medicine (S.F.), Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan; LPixel, Tokyo, Japan (M.N., A.C., Y.S.); and Department of Radiology, Osaka City University Hospital, Osaka, Japan (Y.K.)
| | - Masataka Nishimori
- From the Department of Diagnostic and Interventional Radiology (D.U., A.Y., T.S., S.D., A.S., Y.M.) and Department of Premier Preventive Medicine (S.F.), Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan; LPixel, Tokyo, Japan (M.N., A.C., Y.S.); and Department of Radiology, Osaka City University Hospital, Osaka, Japan (Y.K.)
| | - Taro Shimono
- From the Department of Diagnostic and Interventional Radiology (D.U., A.Y., T.S., S.D., A.S., Y.M.) and Department of Premier Preventive Medicine (S.F.), Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan; LPixel, Tokyo, Japan (M.N., A.C., Y.S.); and Department of Radiology, Osaka City University Hospital, Osaka, Japan (Y.K.)
| | - Satoshi Doishita
- From the Department of Diagnostic and Interventional Radiology (D.U., A.Y., T.S., S.D., A.S., Y.M.) and Department of Premier Preventive Medicine (S.F.), Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan; LPixel, Tokyo, Japan (M.N., A.C., Y.S.); and Department of Radiology, Osaka City University Hospital, Osaka, Japan (Y.K.)
| | - Akitoshi Shimazaki
- From the Department of Diagnostic and Interventional Radiology (D.U., A.Y., T.S., S.D., A.S., Y.M.) and Department of Premier Preventive Medicine (S.F.), Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan; LPixel, Tokyo, Japan (M.N., A.C., Y.S.); and Department of Radiology, Osaka City University Hospital, Osaka, Japan (Y.K.)
| | - Yutaka Katayama
- From the Department of Diagnostic and Interventional Radiology (D.U., A.Y., T.S., S.D., A.S., Y.M.) and Department of Premier Preventive Medicine (S.F.), Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan; LPixel, Tokyo, Japan (M.N., A.C., Y.S.); and Department of Radiology, Osaka City University Hospital, Osaka, Japan (Y.K.)
| | - Shinya Fukumoto
- From the Department of Diagnostic and Interventional Radiology (D.U., A.Y., T.S., S.D., A.S., Y.M.) and Department of Premier Preventive Medicine (S.F.), Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan; LPixel, Tokyo, Japan (M.N., A.C., Y.S.); and Department of Radiology, Osaka City University Hospital, Osaka, Japan (Y.K.)
| | - Antoine Choppin
- From the Department of Diagnostic and Interventional Radiology (D.U., A.Y., T.S., S.D., A.S., Y.M.) and Department of Premier Preventive Medicine (S.F.), Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan; LPixel, Tokyo, Japan (M.N., A.C., Y.S.); and Department of Radiology, Osaka City University Hospital, Osaka, Japan (Y.K.)
| | - Yuki Shimahara
- From the Department of Diagnostic and Interventional Radiology (D.U., A.Y., T.S., S.D., A.S., Y.M.) and Department of Premier Preventive Medicine (S.F.), Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan; LPixel, Tokyo, Japan (M.N., A.C., Y.S.); and Department of Radiology, Osaka City University Hospital, Osaka, Japan (Y.K.)
| | - Yukio Miki
- From the Department of Diagnostic and Interventional Radiology (D.U., A.Y., T.S., S.D., A.S., Y.M.) and Department of Premier Preventive Medicine (S.F.), Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan; LPixel, Tokyo, Japan (M.N., A.C., Y.S.); and Department of Radiology, Osaka City University Hospital, Osaka, Japan (Y.K.)
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Miki S, Hayashi N, Masutani Y, Nomura Y, Yoshikawa T, Hanaoka S, Nemoto M, Ohtomo K. Computer-Assisted Detection of Cerebral Aneurysms in MR Angiography in a Routine Image-Reading Environment: Effects on Diagnosis by Radiologists. AJNR Am J Neuroradiol 2016; 37:1038-43. [PMID: 26892988 DOI: 10.3174/ajnr.a4671] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2015] [Accepted: 11/19/2015] [Indexed: 12/19/2022]
Abstract
BACKGROUND AND PURPOSE Experiences with computer-assisted detection of cerebral aneurysms in diagnosis by radiologists in real-life clinical environments have not been reported. The purpose of this study was to evaluate the usefulness of computer-assisted detection in a routine reading environment. MATERIALS AND METHODS During 39 months in a routine clinical practice environment, 2701 MR angiograms were each read by 2 radiologists by using a computer-assisted detection system. Initial interpretation was independently made without using the detection system, followed by a possible alteration of diagnosis after referring to the lesion candidate output from the system. We used the final consensus of the 2 radiologists as the reference standard. The sensitivity and specificity of radiologists before and after seeing the lesion candidates were evaluated by aneurysm- and patient-based analyses. RESULTS The use of the computer-assisted detection system increased the number of detected aneurysms by 9.3% (from 258 to 282). Aneurysm-based analysis revealed that the apparent sensitivity of the radiologists' diagnoses made without and with the detection system was 64% and 69%, respectively. The detection system presented 82% of the aneurysms. The detection system more frequently benefited radiologists than being detrimental. CONCLUSIONS Routine integration of computer-assisted detection with MR angiography for cerebral aneurysms is feasible, and radiologists can detect a number of additional cerebral aneurysms by using the detection system without a substantial decrease in their specificity. The low confidence of radiologists in the system may limit its usefulness.
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Affiliation(s)
- S Miki
- From the Departments of Computational Diagnostic Radiology and Preventive Medicine (S.M., N.H., Y.N., T.Y., M.N.)
| | - N Hayashi
- From the Departments of Computational Diagnostic Radiology and Preventive Medicine (S.M., N.H., Y.N., T.Y., M.N.)
| | - Y Masutani
- Faculty of Information Sciences and Graduate School of Information Sciences (Y.M.), Hiroshima City University, Hiroshima, Japan
| | - Y Nomura
- From the Departments of Computational Diagnostic Radiology and Preventive Medicine (S.M., N.H., Y.N., T.Y., M.N.)
| | - T Yoshikawa
- From the Departments of Computational Diagnostic Radiology and Preventive Medicine (S.M., N.H., Y.N., T.Y., M.N.)
| | - S Hanaoka
- Radiology (S.H., K.O.), The University of Tokyo Hospital, Tokyo, Japan
| | - M Nemoto
- From the Departments of Computational Diagnostic Radiology and Preventive Medicine (S.M., N.H., Y.N., T.Y., M.N.)
| | - K Ohtomo
- Radiology (S.H., K.O.), The University of Tokyo Hospital, Tokyo, Japan
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Fukuoka D. [Computer-aided diagnosis on the field of head and neck: development and current trends]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2013; 69:1313-1319. [PMID: 24256657 DOI: 10.6009/jjrt.2013_jsrt_69.11.1313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
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Uchiyama Y, Asano T, Kato H, Hara T, Kanematsu M, Hoshi H, Iwama T, Fujita H. Computer-aided diagnosis for detection of lacunar infarcts on MR images: ROC analysis of radiologists' performance. J Digit Imaging 2012; 25:497-503. [PMID: 22215250 DOI: 10.1007/s10278-011-9444-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
The purpose of this study was to retrospectively evaluate radiologist performance in detection of lacunar infarcts on T1- and T2-weighted images, without and with the use of a computer-aided diagnosis (CAD) scheme. Thirty T1-weighted and 30 T2-weighted MR images obtained from 30 patients were used for assessing observer performance. These images were acquired using the fast spin-echo sequence with a 1.5-T MR imaging scanner. The group included 15 patients (age range, 48-83 years; mean age, 67.2 years; 10 men and five women) with a lacunar infarct and 15 patients (age range, 39-76 years; mean age, 64.0 years; eight men and seven women) without lacunar infarcts. Nine radiologists participated in the study. The radiologists initially interpreted the T1- and T2-weighted images without and then with the use of CAD, which indicated their confidence levels regarding the presence (or absence) of lacunar infarcts and the most likely position of a lesion on each MR scan. The observers' performance without and with the computer output was evaluated by performing receiver operating characteristic analysis. For the nine radiologists, the mean area under the best-fit binormal receiver operating characteristic curve plotted for unit square values of radiologists who interpreted the images without and with the scheme were 0.891 and 0.937, respectively. The performance of the radiologists improved significantly when they used the computer output (p=0.032). The CAD scheme has potential to improve the accuracy of radiologists' performance in detection of lacunar infarcts.
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Affiliation(s)
- Yoshikazu Uchiyama
- Department of Computer and Control Engineering, Oita National College of Technology, 1666 Maki, Oita City, 870-0512, Japan.
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Arimura H, Tokunaga C, Yamashita Y, Kuwazuru J. Magnetic Resonance Image Analysis for Brain CAD Systems with Machine Learning. MACHINE LEARNING IN COMPUTER-AIDED DIAGNOSIS 2012. [DOI: 10.4018/978-1-4666-0059-1.ch013] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
This chapter describes the image analysis for brain Computer-Aided Diagnosis (CAD) systems with machine learning techniques, which could assist radiologists in the detection of such brain diseases as asymptomatic unruptured aneurysms, Alzheimer’s Disease (AD), vascular dementia, and Multiple Sclerosis (MS) by magnetic resonance imaging. Image analysis in CAD systems consists of image enhancement, initial detection, and image feature extraction, including segmentation. In addition, the authors review the classification of true and false positives using machine learning techniques, as well as the evaluation methods and development cycle for CAD systems.
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Computer-Aided Diagnosis Systems for Brain Diseases in Magnetic Resonance Images. ALGORITHMS 2009. [DOI: 10.3390/a2030925] [Citation(s) in RCA: 54] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Uchiyama Y, Yokoyama R, Ando H, Asano T, Kato H, Yamakawa H, Yamakawa H, Hara T, Iwama T, Hoshi H, Fujita H. Computer-aided diagnosis scheme for detection of lacunar infarcts on MR images. Acad Radiol 2007; 14:1554-61. [PMID: 18035284 DOI: 10.1016/j.acra.2007.09.012] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2006] [Revised: 09/07/2007] [Accepted: 09/13/2007] [Indexed: 11/19/2022]
Abstract
RATIONALE AND OBJECTIVES The detection and management of asymptomatic lacunar infarcts on magnetic resonance (MR) images are important tasks for radiologists to ensure the prevention of severe cerebral infarctions. However, accurate identification of the lacunar infarcts on MR images is a difficult task for the radiologists. Therefore the purpose of this study was to develop a computer-aided diagnosis scheme for the detection of lacunar infarcts to assist radiologists' interpretation as a "second opinion." MATERIALS AND METHODS Our database comprised 1,143 T1- and 1,143 T2-weighted images obtained from 132 patients. The locations of the lacunar infarcts were determined by experienced neuroradiologists. We first segmented the cerebral region in a T1-weighted image by using a region growing technique for restricting the search area of lacunar infarcts. For identifying the initial lacunar infarcts candidates, a top-hat transform and multiple-phase binarization were then applied to the T2-weighted image within the segmented cerebral region. For eliminating the false positives (FPs), we determined 12 features--the locations x and y, signal intensity differences in the T1- and T2-weighted images, nodular components from a scale of 1 to 4, and nodular and linear components from a scale of 1 to 4. The nodular components and the linear components were obtained using a filter bank technique. The rule-based schemes and a support vector machine with 12 features were applied to the regions of the initial candidates for distinguishing between lacunar infarcts and FPs. RESULTS Our computerized scheme was evaluated by using a holdout method. The sensitivity of the detection of lacunar infarcts was 96.8% (90/93) with 0.76 FP per image. CONCLUSIONS Our computerized scheme would be useful in assisting radiologists for identifying lacunar infarcts in MR images.
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Affiliation(s)
- Yoshikazu Uchiyama
- Department of Intelligent Image Information, Graduate School of Medicine, Gifu University, Japan.
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