OC05.01: Prospective validation of an end‐to‐end machine learning‐based model for the classification of adnexal masses using ultrasonography
Gespeichert in:
| Veröffentlicht in: | Ultrasound in obstetrics & gynecology 2024-09, Vol.64 (S1), p.12-12 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 12 |
|---|---|
| container_issue | S1 |
| container_start_page | 12 |
| container_title | Ultrasound in obstetrics & gynecology |
| container_volume | 64 |
| creator | Barcroft, J. Linton‐Reid, K. Munaretto, M. Fantauzzi, M. Kim, J. Murugesu, S. Parker, N. Kyriacou, C. Novak, A.M. Pikovsky, M. Cooper, N. Lee, S. Savelli, L. Thomson, A.R. Yazbek, J. Stalder, C. Bharwani, N. Posma, J. Timmerman, D. Al‐Memar, M. Landolfo, C. Saso, S. Aboagye, E. Bourne, T. |
| description | |
| doi_str_mv | 10.1002/uog.27748 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3102049360</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3102049360</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1028-65910644839bca17beac24ba5bafc241d5f812c0acbab85c5f6eefda267dc6573</originalsourceid><addsrcrecordid>eNp1kM9u2zAMxoWhBZa2O-wNBOzUgxPKf2R7tyLo0gEB0kNzNmiZShQ4UibZbXPbIxR9xD7JtGVAT72QBPnjR_Bj7KuAqQBIZ6PbTNOyzKtPbCJyWSdQQnHGJlBLSEpZp5_ZRQg7AJB5JifsdTWHYgriO7_3LhxIDeaR-CP2psPBOMud5mg52e7t98vgYogl36PaGku8J_TW2E1stxgoDlxHPdfO82FLXPUYgtFGvUt1lp6xjwIhUOBjiMt87AePwVm38XjYHq_YucY-0Jf_-ZKtf9w-zO-S5Wrxc36zTJSAtEpkUYv4RF5ldatQlC2hSvMWixZ1LERX6EqkClC12FaFKrQk0h2msuyULMrskn076R68-zVSGJqdG72NJ5ssXoC8ziRE6vpEqehP8KSbgzd79MdGQPPX8iZa3vyzPLKzE_tkejp-DDbr1eK08QcBjoiV</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3102049360</pqid></control><display><type>article</type><title>OC05.01: Prospective validation of an end‐to‐end machine learning‐based model for the classification of adnexal masses using ultrasonography</title><source>Wiley Online Library Journals Frontfile Complete</source><creator>Barcroft, J. ; Linton‐Reid, K. ; Munaretto, M. ; Fantauzzi, M. ; Kim, J. ; Murugesu, S. ; Parker, N. ; Kyriacou, C. ; Novak, A.M. ; Pikovsky, M. ; Cooper, N. ; Lee, S. ; Savelli, L. ; Thomson, A.R. ; Yazbek, J. ; Stalder, C. ; Bharwani, N. ; Posma, J. ; Timmerman, D. ; Al‐Memar, M. ; Landolfo, C. ; Saso, S. ; Aboagye, E. ; Bourne, T.</creator><creatorcontrib>Barcroft, J. ; Linton‐Reid, K. ; Munaretto, M. ; Fantauzzi, M. ; Kim, J. ; Murugesu, S. ; Parker, N. ; Kyriacou, C. ; Novak, A.M. ; Pikovsky, M. ; Cooper, N. ; Lee, S. ; Savelli, L. ; Thomson, A.R. ; Yazbek, J. ; Stalder, C. ; Bharwani, N. ; Posma, J. ; Timmerman, D. ; Al‐Memar, M. ; Landolfo, C. ; Saso, S. ; Aboagye, E. ; Bourne, T.</creatorcontrib><identifier>ISSN: 0960-7692</identifier><identifier>EISSN: 1469-0705</identifier><identifier>DOI: 10.1002/uog.27748</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Machine learning</subject><ispartof>Ultrasound in obstetrics & gynecology, 2024-09, Vol.64 (S1), p.12-12</ispartof><rights>The Authors 2024. © Ultrasound in Obstetrics & Gynecology</rights><rights>Copyright © 2024 ISUOG. Published by John Wiley & Sons Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fuog.27748$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fuog.27748$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids></links><search><creatorcontrib>Barcroft, J.</creatorcontrib><creatorcontrib>Linton‐Reid, K.</creatorcontrib><creatorcontrib>Munaretto, M.</creatorcontrib><creatorcontrib>Fantauzzi, M.</creatorcontrib><creatorcontrib>Kim, J.</creatorcontrib><creatorcontrib>Murugesu, S.</creatorcontrib><creatorcontrib>Parker, N.</creatorcontrib><creatorcontrib>Kyriacou, C.</creatorcontrib><creatorcontrib>Novak, A.M.</creatorcontrib><creatorcontrib>Pikovsky, M.</creatorcontrib><creatorcontrib>Cooper, N.</creatorcontrib><creatorcontrib>Lee, S.</creatorcontrib><creatorcontrib>Savelli, L.</creatorcontrib><creatorcontrib>Thomson, A.R.</creatorcontrib><creatorcontrib>Yazbek, J.</creatorcontrib><creatorcontrib>Stalder, C.</creatorcontrib><creatorcontrib>Bharwani, N.</creatorcontrib><creatorcontrib>Posma, J.</creatorcontrib><creatorcontrib>Timmerman, D.</creatorcontrib><creatorcontrib>Al‐Memar, M.</creatorcontrib><creatorcontrib>Landolfo, C.</creatorcontrib><creatorcontrib>Saso, S.</creatorcontrib><creatorcontrib>Aboagye, E.</creatorcontrib><creatorcontrib>Bourne, T.</creatorcontrib><title>OC05.01: Prospective validation of an end‐to‐end machine learning‐based model for the classification of adnexal masses using ultrasonography</title><title>Ultrasound in obstetrics & gynecology</title><subject>Machine learning</subject><issn>0960-7692</issn><issn>1469-0705</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp1kM9u2zAMxoWhBZa2O-wNBOzUgxPKf2R7tyLo0gEB0kNzNmiZShQ4UibZbXPbIxR9xD7JtGVAT72QBPnjR_Bj7KuAqQBIZ6PbTNOyzKtPbCJyWSdQQnHGJlBLSEpZp5_ZRQg7AJB5JifsdTWHYgriO7_3LhxIDeaR-CP2psPBOMud5mg52e7t98vgYogl36PaGku8J_TW2E1stxgoDlxHPdfO82FLXPUYgtFGvUt1lp6xjwIhUOBjiMt87AePwVm38XjYHq_YucY-0Jf_-ZKtf9w-zO-S5Wrxc36zTJSAtEpkUYv4RF5ldatQlC2hSvMWixZ1LERX6EqkClC12FaFKrQk0h2msuyULMrskn076R68-zVSGJqdG72NJ5ssXoC8ziRE6vpEqehP8KSbgzd79MdGQPPX8iZa3vyzPLKzE_tkejp-DDbr1eK08QcBjoiV</recordid><startdate>202409</startdate><enddate>202409</enddate><creator>Barcroft, J.</creator><creator>Linton‐Reid, K.</creator><creator>Munaretto, M.</creator><creator>Fantauzzi, M.</creator><creator>Kim, J.</creator><creator>Murugesu, S.</creator><creator>Parker, N.</creator><creator>Kyriacou, C.</creator><creator>Novak, A.M.</creator><creator>Pikovsky, M.</creator><creator>Cooper, N.</creator><creator>Lee, S.</creator><creator>Savelli, L.</creator><creator>Thomson, A.R.</creator><creator>Yazbek, J.</creator><creator>Stalder, C.</creator><creator>Bharwani, N.</creator><creator>Posma, J.</creator><creator>Timmerman, D.</creator><creator>Al‐Memar, M.</creator><creator>Landolfo, C.</creator><creator>Saso, S.</creator><creator>Aboagye, E.</creator><creator>Bourne, T.</creator><general>John Wiley & Sons, Ltd</general><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope></search><sort><creationdate>202409</creationdate><title>OC05.01: Prospective validation of an end‐to‐end machine learning‐based model for the classification of adnexal masses using ultrasonography</title><author>Barcroft, J. ; Linton‐Reid, K. ; Munaretto, M. ; Fantauzzi, M. ; Kim, J. ; Murugesu, S. ; Parker, N. ; Kyriacou, C. ; Novak, A.M. ; Pikovsky, M. ; Cooper, N. ; Lee, S. ; Savelli, L. ; Thomson, A.R. ; Yazbek, J. ; Stalder, C. ; Bharwani, N. ; Posma, J. ; Timmerman, D. ; Al‐Memar, M. ; Landolfo, C. ; Saso, S. ; Aboagye, E. ; Bourne, T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1028-65910644839bca17beac24ba5bafc241d5f812c0acbab85c5f6eefda267dc6573</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Machine learning</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Barcroft, J.</creatorcontrib><creatorcontrib>Linton‐Reid, K.</creatorcontrib><creatorcontrib>Munaretto, M.</creatorcontrib><creatorcontrib>Fantauzzi, M.</creatorcontrib><creatorcontrib>Kim, J.</creatorcontrib><creatorcontrib>Murugesu, S.</creatorcontrib><creatorcontrib>Parker, N.</creatorcontrib><creatorcontrib>Kyriacou, C.</creatorcontrib><creatorcontrib>Novak, A.M.</creatorcontrib><creatorcontrib>Pikovsky, M.</creatorcontrib><creatorcontrib>Cooper, N.</creatorcontrib><creatorcontrib>Lee, S.</creatorcontrib><creatorcontrib>Savelli, L.</creatorcontrib><creatorcontrib>Thomson, A.R.</creatorcontrib><creatorcontrib>Yazbek, J.</creatorcontrib><creatorcontrib>Stalder, C.</creatorcontrib><creatorcontrib>Bharwani, N.</creatorcontrib><creatorcontrib>Posma, J.</creatorcontrib><creatorcontrib>Timmerman, D.</creatorcontrib><creatorcontrib>Al‐Memar, M.</creatorcontrib><creatorcontrib>Landolfo, C.</creatorcontrib><creatorcontrib>Saso, S.</creatorcontrib><creatorcontrib>Aboagye, E.</creatorcontrib><creatorcontrib>Bourne, T.</creatorcontrib><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Ultrasound in obstetrics & gynecology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Barcroft, J.</au><au>Linton‐Reid, K.</au><au>Munaretto, M.</au><au>Fantauzzi, M.</au><au>Kim, J.</au><au>Murugesu, S.</au><au>Parker, N.</au><au>Kyriacou, C.</au><au>Novak, A.M.</au><au>Pikovsky, M.</au><au>Cooper, N.</au><au>Lee, S.</au><au>Savelli, L.</au><au>Thomson, A.R.</au><au>Yazbek, J.</au><au>Stalder, C.</au><au>Bharwani, N.</au><au>Posma, J.</au><au>Timmerman, D.</au><au>Al‐Memar, M.</au><au>Landolfo, C.</au><au>Saso, S.</au><au>Aboagye, E.</au><au>Bourne, T.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>OC05.01: Prospective validation of an end‐to‐end machine learning‐based model for the classification of adnexal masses using ultrasonography</atitle><jtitle>Ultrasound in obstetrics & gynecology</jtitle><date>2024-09</date><risdate>2024</risdate><volume>64</volume><issue>S1</issue><spage>12</spage><epage>12</epage><pages>12-12</pages><issn>0960-7692</issn><eissn>1469-0705</eissn><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/uog.27748</doi><tpages>1</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0960-7692 |
| ispartof | Ultrasound in obstetrics & gynecology, 2024-09, Vol.64 (S1), p.12-12 |
| issn | 0960-7692 1469-0705 |
| language | eng |
| recordid | cdi_proquest_journals_3102049360 |
| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Machine learning |
| title | OC05.01: Prospective validation of an end‐to‐end machine learning‐based model for the classification of adnexal masses using ultrasonography |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T06%3A52%3A46IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=OC05.01:%20Prospective%20validation%20of%20an%20end%E2%80%90to%E2%80%90end%20machine%20learning%E2%80%90based%20model%20for%20the%20classification%20of%20adnexal%20masses%20using%20ultrasonography&rft.jtitle=Ultrasound%20in%20obstetrics%20&%20gynecology&rft.au=Barcroft,%20J.&rft.date=2024-09&rft.volume=64&rft.issue=S1&rft.spage=12&rft.epage=12&rft.pages=12-12&rft.issn=0960-7692&rft.eissn=1469-0705&rft_id=info:doi/10.1002/uog.27748&rft_dat=%3Cproquest_cross%3E3102049360%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3102049360&rft_id=info:pmid/&rfr_iscdi=true |