Deep learning algorithm for detection of aortic dissection on non-contrast-enhanced CT
Objectives To develop a deep learning–based algorithm to detect aortic dissection (AD) and evaluate the diagnostic ability of the algorithm compared with those of radiologists. Methods Included in the study were 170 patients (85 with AD and 85 without AD). An AD detection algorithm was developed usi...
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| Veröffentlicht in: | European radiology 2021-02, Vol.31 (2), p.1151-1159 |
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| creator | Hata, Akinori Yanagawa, Masahiro Yamagata, Kazuki Suzuki, Yuuki Kido, Shoji Kawata, Atsushi Doi, Shuhei Yoshida, Yuriko Miyata, Tomo Tsubamoto, Mitsuko Kikuchi, Noriko Tomiyama, Noriyuki |
| description | Objectives
To develop a deep learning–based algorithm to detect aortic dissection (AD) and evaluate the diagnostic ability of the algorithm compared with those of radiologists.
Methods
Included in the study were 170 patients (85 with AD and 85 without AD). An AD detection algorithm was developed using a convolutional neural network with Xception architecture. Of the patient data, 80% were used for training and validation and 20% were used for testing. Fivefold cross-validation was performed to evaluate the method. An average of 6688 non-contrast-enhanced CT images (slice thickness, 5 mm) were used for training. A radiologist reviewed both contrast-enhanced and non-contrast-enhanced images and identified the slices of AD. The identified slices were used as ground truth. Receiver operating characteristic curve and area under the curve (AUC) analysis was performed. Five radiologists independently evaluated the images. The accuracy, sensitivity, and specificity of the algorithm and those of the radiologists were compared.
Results
The AUC of the developed algorithm was 0.940, and a cutoff value of 0.400 provided accuracy of 90.0%, sensitivity of 91.8%, and specificity of 88.2%. For the radiologists, median (range) accuracy, sensitivity, and specificity were 88.8 (83.5–94.1)%, 90.6 (83.5–94.1)%, and 94.1 (72.9–97.6)%, respectively. There was no significant difference in performance in terms of accuracy, sensitivity, or specificity between the algorithm and the average performance of the radiologists (
p
> 0.05).
Conclusions
The developed algorithm showed comparable diagnostic performance to radiologists for detecting AD, which suggests the potential of the proposed method to support clinical practice by reducing missed ADs.
Key Points
• A deep learning–based algorithm for detecting aortic dissection was developed using the non-contrast-enhanced CT images of 170 patients.
• The algorithm had an AUC of 0.940 for detecting aortic dissection.
• The accuracy, sensitivity, and specificity of the algorithm were comparable to those of radiologists. |
| doi_str_mv | 10.1007/s00330-020-07213-w |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2438684198</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2478669913</sourcerecordid><originalsourceid>FETCH-LOGICAL-c441t-65837dc359d04b1e4cb445a10a5da5e14ef606a60cb2e32ad3f46748d2aa7e843</originalsourceid><addsrcrecordid>eNp9kM1KJDEUhYM4aPvzAi6GgJvZxLn5qUpqKa2jgjAbdRvSya22pDppk2rEt5-MrSO4mEUIJN859_IRcsLhjAPonwVASmAg6tGCS_ayQ2ZcScE4GLVLZtBJw3TXqX1yUMoTAHRc6T2yL4VptAA5Iw8XiGs6ostxiEvqxmXKw_S4on3KNOCEfhpSpKmnLuVp8DQMpXw8RhpTZD7FKbsyMYyPLnoMdH53RL71bix4_H4fkvtfl3fza3b7--pmfn7LvFJ8Ym1jpA5eNl0AteCo_EKpxnFwTXANcoV9C61rwS8ESuGC7FWrlQnCOY1GyUPyY9u7zul5g2Wyq6F4HEcXMW2KFUqa1ijemYqefkGf0ibHul2ltGnbruOyUmJL-ZxKydjbdR5WLr9aDvavdbu1bqt1-2bdvtTQ9_fqzWKF4V_kQ3MF5BYo9SsuMX_O_k_tH2q3jRo</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2478669913</pqid></control><display><type>article</type><title>Deep learning algorithm for detection of aortic dissection on non-contrast-enhanced CT</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Hata, Akinori ; Yanagawa, Masahiro ; Yamagata, Kazuki ; Suzuki, Yuuki ; Kido, Shoji ; Kawata, Atsushi ; Doi, Shuhei ; Yoshida, Yuriko ; Miyata, Tomo ; Tsubamoto, Mitsuko ; Kikuchi, Noriko ; Tomiyama, Noriyuki</creator><creatorcontrib>Hata, Akinori ; Yanagawa, Masahiro ; Yamagata, Kazuki ; Suzuki, Yuuki ; Kido, Shoji ; Kawata, Atsushi ; Doi, Shuhei ; Yoshida, Yuriko ; Miyata, Tomo ; Tsubamoto, Mitsuko ; Kikuchi, Noriko ; Tomiyama, Noriyuki</creatorcontrib><description>Objectives
To develop a deep learning–based algorithm to detect aortic dissection (AD) and evaluate the diagnostic ability of the algorithm compared with those of radiologists.
Methods
Included in the study were 170 patients (85 with AD and 85 without AD). An AD detection algorithm was developed using a convolutional neural network with Xception architecture. Of the patient data, 80% were used for training and validation and 20% were used for testing. Fivefold cross-validation was performed to evaluate the method. An average of 6688 non-contrast-enhanced CT images (slice thickness, 5 mm) were used for training. A radiologist reviewed both contrast-enhanced and non-contrast-enhanced images and identified the slices of AD. The identified slices were used as ground truth. Receiver operating characteristic curve and area under the curve (AUC) analysis was performed. Five radiologists independently evaluated the images. The accuracy, sensitivity, and specificity of the algorithm and those of the radiologists were compared.
Results
The AUC of the developed algorithm was 0.940, and a cutoff value of 0.400 provided accuracy of 90.0%, sensitivity of 91.8%, and specificity of 88.2%. For the radiologists, median (range) accuracy, sensitivity, and specificity were 88.8 (83.5–94.1)%, 90.6 (83.5–94.1)%, and 94.1 (72.9–97.6)%, respectively. There was no significant difference in performance in terms of accuracy, sensitivity, or specificity between the algorithm and the average performance of the radiologists (
p
> 0.05).
Conclusions
The developed algorithm showed comparable diagnostic performance to radiologists for detecting AD, which suggests the potential of the proposed method to support clinical practice by reducing missed ADs.
Key Points
• A deep learning–based algorithm for detecting aortic dissection was developed using the non-contrast-enhanced CT images of 170 patients.
• The algorithm had an AUC of 0.940 for detecting aortic dissection.
• The accuracy, sensitivity, and specificity of the algorithm were comparable to those of radiologists.</description><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-020-07213-w</identifier><identifier>PMID: 32857203</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Accuracy ; Algorithms ; Aneurysm, Dissecting - diagnostic imaging ; Aorta ; Aortic dissection ; Artificial intelligence ; Artificial neural networks ; Cardiac ; Computed tomography ; Deep Learning ; Diagnostic Radiology ; Diagnostic systems ; Dissection ; Ground truth ; Humans ; Image contrast ; Image enhancement ; Imaging ; Internal Medicine ; Interventional Radiology ; Machine learning ; Medical imaging ; Medicine ; Medicine & Public Health ; Neural networks ; Neuroradiology ; Radiologists ; Radiology ; Tomography, X-Ray Computed ; Training ; Ultrasound</subject><ispartof>European radiology, 2021-02, Vol.31 (2), p.1151-1159</ispartof><rights>European Society of Radiology 2020</rights><rights>European Society of Radiology 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c441t-65837dc359d04b1e4cb445a10a5da5e14ef606a60cb2e32ad3f46748d2aa7e843</citedby><cites>FETCH-LOGICAL-c441t-65837dc359d04b1e4cb445a10a5da5e14ef606a60cb2e32ad3f46748d2aa7e843</cites><orcidid>0000-0003-1149-7094</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00330-020-07213-w$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-020-07213-w$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32857203$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hata, Akinori</creatorcontrib><creatorcontrib>Yanagawa, Masahiro</creatorcontrib><creatorcontrib>Yamagata, Kazuki</creatorcontrib><creatorcontrib>Suzuki, Yuuki</creatorcontrib><creatorcontrib>Kido, Shoji</creatorcontrib><creatorcontrib>Kawata, Atsushi</creatorcontrib><creatorcontrib>Doi, Shuhei</creatorcontrib><creatorcontrib>Yoshida, Yuriko</creatorcontrib><creatorcontrib>Miyata, Tomo</creatorcontrib><creatorcontrib>Tsubamoto, Mitsuko</creatorcontrib><creatorcontrib>Kikuchi, Noriko</creatorcontrib><creatorcontrib>Tomiyama, Noriyuki</creatorcontrib><title>Deep learning algorithm for detection of aortic dissection on non-contrast-enhanced CT</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objectives
To develop a deep learning–based algorithm to detect aortic dissection (AD) and evaluate the diagnostic ability of the algorithm compared with those of radiologists.
Methods
Included in the study were 170 patients (85 with AD and 85 without AD). An AD detection algorithm was developed using a convolutional neural network with Xception architecture. Of the patient data, 80% were used for training and validation and 20% were used for testing. Fivefold cross-validation was performed to evaluate the method. An average of 6688 non-contrast-enhanced CT images (slice thickness, 5 mm) were used for training. A radiologist reviewed both contrast-enhanced and non-contrast-enhanced images and identified the slices of AD. The identified slices were used as ground truth. Receiver operating characteristic curve and area under the curve (AUC) analysis was performed. Five radiologists independently evaluated the images. The accuracy, sensitivity, and specificity of the algorithm and those of the radiologists were compared.
Results
The AUC of the developed algorithm was 0.940, and a cutoff value of 0.400 provided accuracy of 90.0%, sensitivity of 91.8%, and specificity of 88.2%. For the radiologists, median (range) accuracy, sensitivity, and specificity were 88.8 (83.5–94.1)%, 90.6 (83.5–94.1)%, and 94.1 (72.9–97.6)%, respectively. There was no significant difference in performance in terms of accuracy, sensitivity, or specificity between the algorithm and the average performance of the radiologists (
p
> 0.05).
Conclusions
The developed algorithm showed comparable diagnostic performance to radiologists for detecting AD, which suggests the potential of the proposed method to support clinical practice by reducing missed ADs.
Key Points
• A deep learning–based algorithm for detecting aortic dissection was developed using the non-contrast-enhanced CT images of 170 patients.
• The algorithm had an AUC of 0.940 for detecting aortic dissection.
• The accuracy, sensitivity, and specificity of the algorithm were comparable to those of radiologists.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Aneurysm, Dissecting - diagnostic imaging</subject><subject>Aorta</subject><subject>Aortic dissection</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Cardiac</subject><subject>Computed tomography</subject><subject>Deep Learning</subject><subject>Diagnostic Radiology</subject><subject>Diagnostic systems</subject><subject>Dissection</subject><subject>Ground truth</subject><subject>Humans</subject><subject>Image contrast</subject><subject>Image enhancement</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Machine learning</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neural networks</subject><subject>Neuroradiology</subject><subject>Radiologists</subject><subject>Radiology</subject><subject>Tomography, X-Ray Computed</subject><subject>Training</subject><subject>Ultrasound</subject><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kM1KJDEUhYM4aPvzAi6GgJvZxLn5qUpqKa2jgjAbdRvSya22pDppk2rEt5-MrSO4mEUIJN859_IRcsLhjAPonwVASmAg6tGCS_ayQ2ZcScE4GLVLZtBJw3TXqX1yUMoTAHRc6T2yL4VptAA5Iw8XiGs6ostxiEvqxmXKw_S4on3KNOCEfhpSpKmnLuVp8DQMpXw8RhpTZD7FKbsyMYyPLnoMdH53RL71bix4_H4fkvtfl3fza3b7--pmfn7LvFJ8Ym1jpA5eNl0AteCo_EKpxnFwTXANcoV9C61rwS8ESuGC7FWrlQnCOY1GyUPyY9u7zul5g2Wyq6F4HEcXMW2KFUqa1ijemYqefkGf0ibHul2ltGnbruOyUmJL-ZxKydjbdR5WLr9aDvavdbu1bqt1-2bdvtTQ9_fqzWKF4V_kQ3MF5BYo9SsuMX_O_k_tH2q3jRo</recordid><startdate>20210201</startdate><enddate>20210201</enddate><creator>Hata, Akinori</creator><creator>Yanagawa, Masahiro</creator><creator>Yamagata, Kazuki</creator><creator>Suzuki, Yuuki</creator><creator>Kido, Shoji</creator><creator>Kawata, Atsushi</creator><creator>Doi, Shuhei</creator><creator>Yoshida, Yuriko</creator><creator>Miyata, Tomo</creator><creator>Tsubamoto, Mitsuko</creator><creator>Kikuchi, Noriko</creator><creator>Tomiyama, Noriyuki</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QO</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-1149-7094</orcidid></search><sort><creationdate>20210201</creationdate><title>Deep learning algorithm for detection of aortic dissection on non-contrast-enhanced CT</title><author>Hata, Akinori ; Yanagawa, Masahiro ; Yamagata, Kazuki ; Suzuki, Yuuki ; Kido, Shoji ; Kawata, Atsushi ; Doi, Shuhei ; Yoshida, Yuriko ; Miyata, Tomo ; Tsubamoto, Mitsuko ; Kikuchi, Noriko ; Tomiyama, Noriyuki</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c441t-65837dc359d04b1e4cb445a10a5da5e14ef606a60cb2e32ad3f46748d2aa7e843</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Aneurysm, Dissecting - diagnostic imaging</topic><topic>Aorta</topic><topic>Aortic dissection</topic><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Cardiac</topic><topic>Computed tomography</topic><topic>Deep Learning</topic><topic>Diagnostic Radiology</topic><topic>Diagnostic systems</topic><topic>Dissection</topic><topic>Ground truth</topic><topic>Humans</topic><topic>Image contrast</topic><topic>Image enhancement</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Machine learning</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neural networks</topic><topic>Neuroradiology</topic><topic>Radiologists</topic><topic>Radiology</topic><topic>Tomography, X-Ray Computed</topic><topic>Training</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hata, Akinori</creatorcontrib><creatorcontrib>Yanagawa, Masahiro</creatorcontrib><creatorcontrib>Yamagata, Kazuki</creatorcontrib><creatorcontrib>Suzuki, Yuuki</creatorcontrib><creatorcontrib>Kido, Shoji</creatorcontrib><creatorcontrib>Kawata, Atsushi</creatorcontrib><creatorcontrib>Doi, Shuhei</creatorcontrib><creatorcontrib>Yoshida, Yuriko</creatorcontrib><creatorcontrib>Miyata, Tomo</creatorcontrib><creatorcontrib>Tsubamoto, Mitsuko</creatorcontrib><creatorcontrib>Kikuchi, Noriko</creatorcontrib><creatorcontrib>Tomiyama, Noriyuki</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><jtitle>European radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hata, Akinori</au><au>Yanagawa, Masahiro</au><au>Yamagata, Kazuki</au><au>Suzuki, Yuuki</au><au>Kido, Shoji</au><au>Kawata, Atsushi</au><au>Doi, Shuhei</au><au>Yoshida, Yuriko</au><au>Miyata, Tomo</au><au>Tsubamoto, Mitsuko</au><au>Kikuchi, Noriko</au><au>Tomiyama, Noriyuki</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep learning algorithm for detection of aortic dissection on non-contrast-enhanced CT</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2021-02-01</date><risdate>2021</risdate><volume>31</volume><issue>2</issue><spage>1151</spage><epage>1159</epage><pages>1151-1159</pages><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objectives
To develop a deep learning–based algorithm to detect aortic dissection (AD) and evaluate the diagnostic ability of the algorithm compared with those of radiologists.
Methods
Included in the study were 170 patients (85 with AD and 85 without AD). An AD detection algorithm was developed using a convolutional neural network with Xception architecture. Of the patient data, 80% were used for training and validation and 20% were used for testing. Fivefold cross-validation was performed to evaluate the method. An average of 6688 non-contrast-enhanced CT images (slice thickness, 5 mm) were used for training. A radiologist reviewed both contrast-enhanced and non-contrast-enhanced images and identified the slices of AD. The identified slices were used as ground truth. Receiver operating characteristic curve and area under the curve (AUC) analysis was performed. Five radiologists independently evaluated the images. The accuracy, sensitivity, and specificity of the algorithm and those of the radiologists were compared.
Results
The AUC of the developed algorithm was 0.940, and a cutoff value of 0.400 provided accuracy of 90.0%, sensitivity of 91.8%, and specificity of 88.2%. For the radiologists, median (range) accuracy, sensitivity, and specificity were 88.8 (83.5–94.1)%, 90.6 (83.5–94.1)%, and 94.1 (72.9–97.6)%, respectively. There was no significant difference in performance in terms of accuracy, sensitivity, or specificity between the algorithm and the average performance of the radiologists (
p
> 0.05).
Conclusions
The developed algorithm showed comparable diagnostic performance to radiologists for detecting AD, which suggests the potential of the proposed method to support clinical practice by reducing missed ADs.
Key Points
• A deep learning–based algorithm for detecting aortic dissection was developed using the non-contrast-enhanced CT images of 170 patients.
• The algorithm had an AUC of 0.940 for detecting aortic dissection.
• The accuracy, sensitivity, and specificity of the algorithm were comparable to those of radiologists.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>32857203</pmid><doi>10.1007/s00330-020-07213-w</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-1149-7094</orcidid></addata></record> |
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| ispartof | European radiology, 2021-02, Vol.31 (2), p.1151-1159 |
| issn | 0938-7994 1432-1084 |
| language | eng |
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| source | MEDLINE; SpringerLink Journals - AutoHoldings |
| subjects | Accuracy Algorithms Aneurysm, Dissecting - diagnostic imaging Aorta Aortic dissection Artificial intelligence Artificial neural networks Cardiac Computed tomography Deep Learning Diagnostic Radiology Diagnostic systems Dissection Ground truth Humans Image contrast Image enhancement Imaging Internal Medicine Interventional Radiology Machine learning Medical imaging Medicine Medicine & Public Health Neural networks Neuroradiology Radiologists Radiology Tomography, X-Ray Computed Training Ultrasound |
| title | Deep learning algorithm for detection of aortic dissection on non-contrast-enhanced CT |
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