Deep Learning Algorithm to Detect Cardiac Sarcoidosis From Echocardiographic Movies
Because the early diagnosis of subclinical cardiac sarcoidosis (CS) remains difficult, we developed a deep learning algorithm to distinguish CS patients from healthy subjects using echocardiographic movies. Among the patients who underwent echocardiography from January 2015 to December 2019, we chos...
Saved in:
| Published in: | Circulation journal : official journal of the Japanese Circulation Society Vol. 86; no. 1; p. 87 |
|---|---|
| Main Authors: | , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Japan
24.12.2021
|
| Subjects: | |
| ISSN: | 1347-4820, 1347-4820 |
| Online Access: | Get more information |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Because the early diagnosis of subclinical cardiac sarcoidosis (CS) remains difficult, we developed a deep learning algorithm to distinguish CS patients from healthy subjects using echocardiographic movies.
Among the patients who underwent echocardiography from January 2015 to December 2019, we chose 151 echocardiographic movies from 50 CS patients and 151 from 149 healthy subjects. We trained two 3D convolutional neural networks (3D-CNN) to identify CS patients using a dataset of 212 echocardiographic movies with and without a transfer learning method (Pretrained algorithm and Non-pretrained algorithm). On an independent set of 41 echocardiographic movies, the area under the receiver-operating characteristic curve (AUC) of the Pretrained algorithm was greater than that of Non-pretrained algorithm (0.842, 95% confidence interval (CI): 0.722-0.962 vs. 0.724, 95% CI: 0.566-0.882, P=0.253). The AUC from the interpretation of the same set of 41 echocardiographic movies by 5 cardiologists was not significantly different from that of the Pretrained algorithm (0.855, 95% CI: 0.735-0.975 vs. 0.842, 95% CI: 0.722-0.962, P=0.885). A sensitivity map demonstrated that the Pretrained algorithm focused on the area of the mitral valve.
A 3D-CNN with a transfer learning method may be a promising tool for detecting CS using an echocardiographic movie. |
|---|---|
| AbstractList | Because the early diagnosis of subclinical cardiac sarcoidosis (CS) remains difficult, we developed a deep learning algorithm to distinguish CS patients from healthy subjects using echocardiographic movies.
Among the patients who underwent echocardiography from January 2015 to December 2019, we chose 151 echocardiographic movies from 50 CS patients and 151 from 149 healthy subjects. We trained two 3D convolutional neural networks (3D-CNN) to identify CS patients using a dataset of 212 echocardiographic movies with and without a transfer learning method (Pretrained algorithm and Non-pretrained algorithm). On an independent set of 41 echocardiographic movies, the area under the receiver-operating characteristic curve (AUC) of the Pretrained algorithm was greater than that of Non-pretrained algorithm (0.842, 95% confidence interval (CI): 0.722-0.962 vs. 0.724, 95% CI: 0.566-0.882, P=0.253). The AUC from the interpretation of the same set of 41 echocardiographic movies by 5 cardiologists was not significantly different from that of the Pretrained algorithm (0.855, 95% CI: 0.735-0.975 vs. 0.842, 95% CI: 0.722-0.962, P=0.885). A sensitivity map demonstrated that the Pretrained algorithm focused on the area of the mitral valve.
A 3D-CNN with a transfer learning method may be a promising tool for detecting CS using an echocardiographic movie. Because the early diagnosis of subclinical cardiac sarcoidosis (CS) remains difficult, we developed a deep learning algorithm to distinguish CS patients from healthy subjects using echocardiographic movies.BACKGROUNDBecause the early diagnosis of subclinical cardiac sarcoidosis (CS) remains difficult, we developed a deep learning algorithm to distinguish CS patients from healthy subjects using echocardiographic movies.Among the patients who underwent echocardiography from January 2015 to December 2019, we chose 151 echocardiographic movies from 50 CS patients and 151 from 149 healthy subjects. We trained two 3D convolutional neural networks (3D-CNN) to identify CS patients using a dataset of 212 echocardiographic movies with and without a transfer learning method (Pretrained algorithm and Non-pretrained algorithm). On an independent set of 41 echocardiographic movies, the area under the receiver-operating characteristic curve (AUC) of the Pretrained algorithm was greater than that of Non-pretrained algorithm (0.842, 95% confidence interval (CI): 0.722-0.962 vs. 0.724, 95% CI: 0.566-0.882, P=0.253). The AUC from the interpretation of the same set of 41 echocardiographic movies by 5 cardiologists was not significantly different from that of the Pretrained algorithm (0.855, 95% CI: 0.735-0.975 vs. 0.842, 95% CI: 0.722-0.962, P=0.885). A sensitivity map demonstrated that the Pretrained algorithm focused on the area of the mitral valve.METHODS AND RESULTSAmong the patients who underwent echocardiography from January 2015 to December 2019, we chose 151 echocardiographic movies from 50 CS patients and 151 from 149 healthy subjects. We trained two 3D convolutional neural networks (3D-CNN) to identify CS patients using a dataset of 212 echocardiographic movies with and without a transfer learning method (Pretrained algorithm and Non-pretrained algorithm). On an independent set of 41 echocardiographic movies, the area under the receiver-operating characteristic curve (AUC) of the Pretrained algorithm was greater than that of Non-pretrained algorithm (0.842, 95% confidence interval (CI): 0.722-0.962 vs. 0.724, 95% CI: 0.566-0.882, P=0.253). The AUC from the interpretation of the same set of 41 echocardiographic movies by 5 cardiologists was not significantly different from that of the Pretrained algorithm (0.855, 95% CI: 0.735-0.975 vs. 0.842, 95% CI: 0.722-0.962, P=0.885). A sensitivity map demonstrated that the Pretrained algorithm focused on the area of the mitral valve.A 3D-CNN with a transfer learning method may be a promising tool for detecting CS using an echocardiographic movie.CONCLUSIONSA 3D-CNN with a transfer learning method may be a promising tool for detecting CS using an echocardiographic movie. |
| Author | Uehara, Masae Higashikuni, Yasutomi Akazawa, Hiroshi Ieki, Hirotaka Nakao, Tomoko Ninomiya, Kota Morita, Hiroyuki Matsuoka, Ryo Daimon, Masao Kodera, Satoshi Komuro, Issei Katsushika, Susumu Nakamoto, Mitsuhiko Takeda, Norifumi Kakuda, Nobutaka Shinohara, Hiroki Fujiu, Katsuhito Ando, Jiro Nakanishi, Koki |
| Author_xml | – sequence: 1 givenname: Susumu surname: Katsushika fullname: Katsushika, Susumu organization: Department of Cardiovascular Medicine, The University of Tokyo Hospital – sequence: 2 givenname: Satoshi surname: Kodera fullname: Kodera, Satoshi organization: Department of Cardiovascular Medicine, The University of Tokyo Hospital – sequence: 3 givenname: Mitsuhiko surname: Nakamoto fullname: Nakamoto, Mitsuhiko organization: Department of Cardiovascular Medicine, The University of Tokyo Hospital – sequence: 4 givenname: Kota surname: Ninomiya fullname: Ninomiya, Kota organization: Department of Cardiovascular Medicine, The University of Tokyo Hospital – sequence: 5 givenname: Nobutaka surname: Kakuda fullname: Kakuda, Nobutaka organization: Department of Cardiovascular Medicine, The University of Tokyo Hospital – sequence: 6 givenname: Hiroki surname: Shinohara fullname: Shinohara, Hiroki organization: Department of Cardiovascular Medicine, The University of Tokyo Hospital – sequence: 7 givenname: Ryo surname: Matsuoka fullname: Matsuoka, Ryo organization: Department of Cardiovascular Medicine, The University of Tokyo Hospital – sequence: 8 givenname: Hirotaka surname: Ieki fullname: Ieki, Hirotaka organization: Department of Cardiovascular Medicine, The University of Tokyo Hospital – sequence: 9 givenname: Masae surname: Uehara fullname: Uehara, Masae organization: Department of Cardiovascular Medicine, The University of Tokyo Hospital – sequence: 10 givenname: Yasutomi surname: Higashikuni fullname: Higashikuni, Yasutomi organization: Department of Cardiovascular Medicine, The University of Tokyo Hospital – sequence: 11 givenname: Koki surname: Nakanishi fullname: Nakanishi, Koki organization: Department of Cardiovascular Medicine, The University of Tokyo Hospital – sequence: 12 givenname: Tomoko surname: Nakao fullname: Nakao, Tomoko organization: Department of Clinical Laboratory, The University of Tokyo Hospital – sequence: 13 givenname: Norifumi surname: Takeda fullname: Takeda, Norifumi organization: Department of Cardiovascular Medicine, The University of Tokyo Hospital – sequence: 14 givenname: Katsuhito surname: Fujiu fullname: Fujiu, Katsuhito organization: Department of Advanced Cardiology, The University of Tokyo – sequence: 15 givenname: Masao surname: Daimon fullname: Daimon, Masao organization: Department of Clinical Laboratory, The University of Tokyo Hospital – sequence: 16 givenname: Jiro surname: Ando fullname: Ando, Jiro organization: Department of Cardiovascular Medicine, The University of Tokyo Hospital – sequence: 17 givenname: Hiroshi surname: Akazawa fullname: Akazawa, Hiroshi organization: Department of Cardiovascular Medicine, The University of Tokyo Hospital – sequence: 18 givenname: Hiroyuki surname: Morita fullname: Morita, Hiroyuki organization: Department of Cardiovascular Medicine, The University of Tokyo Hospital – sequence: 19 givenname: Issei surname: Komuro fullname: Komuro, Issei organization: Department of Cardiovascular Medicine, The University of Tokyo Hospital |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34176867$$D View this record in MEDLINE/PubMed |
| BookMark | eNpNUDtPwzAYtFARfcDOhDyypNiOndhjlbY8VMRQmKMvjtO6SuJgJ0j8e1pRJKY76U6nu5uiUetag9AtJXPKRPygrdeHefYSMRoRlogLNKExTyMuGRn942M0DeFACFNEqCs0jjlNE5mkE7RdGtPhjQHf2naHF_XOedvvG9w7vDS90T3OwJcWNN6C186WLtiA1941eKX3Tp9Et_PQ7a3Gr-7LmnCNLiuog7k54wx9rFfv2VO0eXt8zhabSAsl-0hVpWSSCkYI0QBE0jI2MUClSBUTI2XMeXVsCkYVpeIVS4AUjArOGS9AEDZD97-5nXefgwl93tigTV1Da9wQcia4UKfF6dF6d7YORWPKvPO2Af-d_x3BfgBOCGHH |
| CitedBy_id | crossref_primary_10_1038_s44325_025_00064_8 crossref_primary_10_1007_s40336_023_00595_z crossref_primary_10_1016_j_autrev_2025_103916 crossref_primary_10_1016_j_jjcc_2021_10_016 crossref_primary_10_1093_eurheartj_ehae356 crossref_primary_10_1097_MCP_0000000000000902 crossref_primary_10_3390_cells11010059 crossref_primary_10_3390_jimaging9020050 crossref_primary_10_3390_life13081653 crossref_primary_10_1007_s00408_023_00641_7 crossref_primary_10_1111_echo_15417 crossref_primary_10_1007_s12410_025_09607_0 crossref_primary_10_1007_s11886_024_02159_7 crossref_primary_10_1016_j_rdc_2022_07_004 crossref_primary_10_1007_s11886_025_02250_7 crossref_primary_10_1097_MCP_0000000000001193 crossref_primary_10_3390_diagnostics13142426 crossref_primary_10_1053_j_semnuclmed_2024_02_004 crossref_primary_10_21518_ms2025_040 crossref_primary_10_1007_s11886_024_02088_5 crossref_primary_10_1007_s42452_025_06504_5 |
| ContentType | Journal Article |
| DBID | CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1253/circj.CJ-21-0265 |
| DatabaseName | Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | no_fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1347-4820 |
| ExternalDocumentID | 34176867 |
| Genre | Research Support, Non-U.S. Gov't Journal Article |
| GroupedDBID | --- .55 29B 2WC 53G 5GY 5RE 6J9 ACGFO ADBBV AENEX ALMA_UNASSIGNED_HOLDINGS BAWUL CGR CS3 CUY CVF DIK DU5 E3Z EBS ECM EIF EJD F5P GX1 JSF JSH KQ8 M~E NPM OK1 P2P RJT RNS RZJ TR2 W2D X7M XSB ZXP 7X8 OVT |
| ID | FETCH-LOGICAL-c598t-9fd828152000caa081d3e3aaf90f30e88344f341ae9bd94f26a0b2154424ba502 |
| IEDL.DBID | 7X8 |
| ISICitedReferencesCount | 24 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000735434500017&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1347-4820 |
| IngestDate | Thu Jul 10 23:55:16 EDT 2025 Thu Jan 02 22:37:52 EST 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Deep learning Echocardiography Transfer learning Artificial intelligence Cardiac sarcoidosis |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c598t-9fd828152000caa081d3e3aaf90f30e88344f341ae9bd94f26a0b2154424ba502 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://www.jstage.jst.go.jp/article/circj/86/1/86_CJ-21-0265/_article/-char/en |
| PMID | 34176867 |
| PQID | 2545990597 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_2545990597 pubmed_primary_34176867 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-12-24 |
| PublicationDateYYYYMMDD | 2021-12-24 |
| PublicationDate_xml | – month: 12 year: 2021 text: 2021-12-24 day: 24 |
| PublicationDecade | 2020 |
| PublicationPlace | Japan |
| PublicationPlace_xml | – name: Japan |
| PublicationTitle | Circulation journal : official journal of the Japanese Circulation Society |
| PublicationTitleAlternate | Circ J |
| PublicationYear | 2021 |
| References | 34471070 - Circ J. 2021 Dec 24;86(1):96-97. doi: 10.1253/circj.CJ-21-0663 |
| References_xml | – reference: 34471070 - Circ J. 2021 Dec 24;86(1):96-97. doi: 10.1253/circj.CJ-21-0663 |
| SSID | ssj0029059 |
| Score | 2.4656377 |
| Snippet | Because the early diagnosis of subclinical cardiac sarcoidosis (CS) remains difficult, we developed a deep learning algorithm to distinguish CS patients from... |
| SourceID | proquest pubmed |
| SourceType | Aggregation Database Index Database |
| StartPage | 87 |
| SubjectTerms | Algorithms Deep Learning Echocardiography Humans Motion Pictures Myocarditis Sarcoidosis - diagnostic imaging |
| Title | Deep Learning Algorithm to Detect Cardiac Sarcoidosis From Echocardiographic Movies |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/34176867 https://www.proquest.com/docview/2545990597 |
| Volume | 86 |
| WOSCitedRecordID | wos000735434500017&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1JS8NAFB7UinhxX-rGCF7HJrNkOUnpgogthSr0Viaz1IhNalP9_b5JUz0JgpecMsnw5i3fm_f4HkI3QieWQn5MqAEj54xxEgVakIBpwPdMx5EueWYfw34_Go3iQXXhVlRtlSufWDpqnSt3R96AREaA5wT8ezd7J25qlKuuViM01lGNAZRxWh2OvqsI1L1eJlw8JBxCXVWmpII1VDpXr7etB0Ihm6aB-B1gloGmu_vfLe6hnQpi4uZSJ_bRmskO0FavKqIfomHbmBmuiFUnuPk2gY8sXqZ4keO2cUUF3Cr1RuEhmEGe6rxIC9yd51PcAW-pyhbWkuk6VbiXw66KI_Tc7Ty17kk1W4EoEUcLElt3Fr4jXfKUlAAMNDNMSht7lnkmcuM3LEQ4aeJEx9zSQHoJddQ9lCdSePQYbWR5Zk4RpkkUGQrLpW851wrcZxJILULftwIUpI6uV-Iag-66goTMTP5RjH8EVkcnS5mPZ0uSjTH8GzKhIDz7w-pztE1dq4lPCeUXqGbBcs0l2lSfi7SYX5VKAc_-oPcFsn_A1Q |
| linkProvider | ProQuest |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Deep+Learning+Algorithm+to+Detect+Cardiac+Sarcoidosis+From+Echocardiographic+Movies&rft.jtitle=Circulation+journal+%3A+official+journal+of+the+Japanese+Circulation+Society&rft.au=Katsushika%2C+Susumu&rft.au=Kodera%2C+Satoshi&rft.au=Nakamoto%2C+Mitsuhiko&rft.au=Ninomiya%2C+Kota&rft.date=2021-12-24&rft.issn=1347-4820&rft.eissn=1347-4820&rft.volume=86&rft.issue=1&rft.spage=87&rft_id=info:doi/10.1253%2Fcircj.CJ-21-0265&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1347-4820&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1347-4820&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1347-4820&client=summon |