Anatomical recognition of dissection layers, nerves, vas deferens, and microvessels using artificial intelligence during transabdominal preperitoneal inguinal hernia repair
Purpose In laparoscopic inguinal hernia surgery, proper recognition of loose connective tissue, nerves, vas deferens, and microvessels is important to prevent postoperative complications, such as recurrence, pain, sexual dysfunction, and bleeding. EUREKA (Anaut Inc., Tokyo, Japan) is a system that u...
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| Veröffentlicht in: | Hernia : the journal of hernias and abdominal wall surgery 2024-12, Vol.29 (1), p.52, Article 52 |
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| container_title | Hernia : the journal of hernias and abdominal wall surgery |
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| creator | Mita, Kazuhito Kobayashi, Nao Takahashi, Kunihiko Sakai, Takashi Shimaguchi, Mayu Kouno, Michitaka Toyota, Naoyuki Hatano, Minoru Toyota, Tsuyoshi Sasaki, Junichi |
| description | Purpose
In laparoscopic inguinal hernia surgery, proper recognition of loose connective tissue, nerves, vas deferens, and microvessels is important to prevent postoperative complications, such as recurrence, pain, sexual dysfunction, and bleeding. EUREKA (Anaut Inc., Tokyo, Japan) is a system that uses artificial intelligence (AI) for anatomical recognition. This system can intraoperatively confirm the aforementioned anatomical landmarks. In this study, we validated the accuracy of EUREKA in recognizing dissection layers, nerves, vas deferens, and microvessels during transabdominal preperitoneal inguinal hernia repair (TAPP).
Methods
We used TAPP videos to compare EUREKA’s recognition of loose connective tissue, nerves, vas deferens, and microvessels with the original surgical video and examined whether EUREKA accurately identified these structures. Intersection over Union (IoU) and F1/Dice scores were calculated to quantitively evaluate AI predictive images.
Results
The mean IoU and F1/Dice scores were 0.33 and 0.50 for connective tissue, 0.24 and 0.38 for nerves, 0.50 and 0.66 for the vas deferens, and 0.30 and 0.45 for microvessels, respectively. Compared with the images without EUREKA visualization, dissection layers were very clearly recognized and displayed when appropriate tension was applied. |
| doi_str_mv | 10.1007/s10029-024-03223-5 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_11671561</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3149540658</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2275-26413b52754654d051741fff1e73b0b791edb9323dfe57620510d7adf05976e73</originalsourceid><addsrcrecordid>eNp9ks1u1DAQxyMEoqXwAhxQJC4cCPgzXp9QVfElVeqlnC0nHqeusnawk5X6Tn1IZjellB568Xg8v_nP2J6qekvJJ0qI-lxwZbohTDSEM8Yb-aw6pkxsGs2IeP5gf1S9KuWaELIR7eZldcS1YkJofVzdnkY7p23o7Vhn6NMQwxxSrJOvXSgF-oM32hvI5WMdIe8A7c6W2oGHDBE9G12NCjlhrMBY6qWEONQ2z8GHPqByiDOMYxgg9lC7Je_Dc7ax2M5h8YjIlGGCHOYU4ZAwLIfjK8gxWGxtsiG_rl54OxZ4c2dPql_fvl6e_WjOL77_PDs9b3rGlGxYKyjvJG5FK4UjkipBvfcUFO9IpzQF12nOuPMgVcsQIE5Z54nUqkXopPqy6k5LtwXXQ8RmRzPlsLX5xiQbzP-RGK7MkHaG0lZR2VJU-HCnkNPvBcpstqH0-AY2QlqK4VRoKUgrN4i-f4RepyXj3VeKM6r5XpCtFD5zKRn8fTeUmP00mHUaDE6DOUyDkZj07uE97lP-fj8CfAXKtP8TyP9qPyH7BxTkxJk</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3149321931</pqid></control><display><type>article</type><title>Anatomical recognition of dissection layers, nerves, vas deferens, and microvessels using artificial intelligence during transabdominal preperitoneal inguinal hernia repair</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Mita, Kazuhito ; Kobayashi, Nao ; Takahashi, Kunihiko ; Sakai, Takashi ; Shimaguchi, Mayu ; Kouno, Michitaka ; Toyota, Naoyuki ; Hatano, Minoru ; Toyota, Tsuyoshi ; Sasaki, Junichi</creator><creatorcontrib>Mita, Kazuhito ; Kobayashi, Nao ; Takahashi, Kunihiko ; Sakai, Takashi ; Shimaguchi, Mayu ; Kouno, Michitaka ; Toyota, Naoyuki ; Hatano, Minoru ; Toyota, Tsuyoshi ; Sasaki, Junichi</creatorcontrib><description>Purpose
In laparoscopic inguinal hernia surgery, proper recognition of loose connective tissue, nerves, vas deferens, and microvessels is important to prevent postoperative complications, such as recurrence, pain, sexual dysfunction, and bleeding. EUREKA (Anaut Inc., Tokyo, Japan) is a system that uses artificial intelligence (AI) for anatomical recognition. This system can intraoperatively confirm the aforementioned anatomical landmarks. In this study, we validated the accuracy of EUREKA in recognizing dissection layers, nerves, vas deferens, and microvessels during transabdominal preperitoneal inguinal hernia repair (TAPP).
Methods
We used TAPP videos to compare EUREKA’s recognition of loose connective tissue, nerves, vas deferens, and microvessels with the original surgical video and examined whether EUREKA accurately identified these structures. Intersection over Union (IoU) and F1/Dice scores were calculated to quantitively evaluate AI predictive images.
Results
The mean IoU and F1/Dice scores were 0.33 and 0.50 for connective tissue, 0.24 and 0.38 for nerves, 0.50 and 0.66 for the vas deferens, and 0.30 and 0.45 for microvessels, respectively. Compared with the images without EUREKA visualization, dissection layers were very clearly recognized and displayed when appropriate tension was applied.</description><identifier>ISSN: 1248-9204</identifier><identifier>ISSN: 1265-4906</identifier><identifier>EISSN: 1248-9204</identifier><identifier>DOI: 10.1007/s10029-024-03223-5</identifier><identifier>PMID: 39724499</identifier><language>eng</language><publisher>Paris: Springer Paris</publisher><subject>Abdominal Surgery ; Anatomic Landmarks ; Artificial Intelligence ; Complications ; Connective tissue ; Connective Tissue - anatomy & histology ; Connective tissues ; Dissection ; Dissection - methods ; Hernia ; Hernia, Inguinal - surgery ; Hernias ; Herniorrhaphy - methods ; How-I-Do-It ; Humans ; Laparoscopy ; Laparoscopy - methods ; Male ; Medicine ; Medicine & Public Health ; Microvessels - anatomy & histology ; Nerves ; Postoperative ; Vas deferens ; Vas Deferens - anatomy & histology ; Vas Deferens - surgery</subject><ispartof>Hernia : the journal of hernias and abdominal wall surgery, 2024-12, Vol.29 (1), p.52, Article 52</ispartof><rights>The Author(s) 2024</rights><rights>2024. The Author(s).</rights><rights>Copyright Springer Nature B.V. Dec 2025</rights><rights>The Author(s) 2024 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2275-26413b52754654d051741fff1e73b0b791edb9323dfe57620510d7adf05976e73</cites><orcidid>0000-0002-1516-8442</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/s10029-024-03223-5$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10029-024-03223-5$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,27923,27924,41487,42556,51318</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39724499$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Mita, Kazuhito</creatorcontrib><creatorcontrib>Kobayashi, Nao</creatorcontrib><creatorcontrib>Takahashi, Kunihiko</creatorcontrib><creatorcontrib>Sakai, Takashi</creatorcontrib><creatorcontrib>Shimaguchi, Mayu</creatorcontrib><creatorcontrib>Kouno, Michitaka</creatorcontrib><creatorcontrib>Toyota, Naoyuki</creatorcontrib><creatorcontrib>Hatano, Minoru</creatorcontrib><creatorcontrib>Toyota, Tsuyoshi</creatorcontrib><creatorcontrib>Sasaki, Junichi</creatorcontrib><title>Anatomical recognition of dissection layers, nerves, vas deferens, and microvessels using artificial intelligence during transabdominal preperitoneal inguinal hernia repair</title><title>Hernia : the journal of hernias and abdominal wall surgery</title><addtitle>Hernia</addtitle><addtitle>Hernia</addtitle><description>Purpose
In laparoscopic inguinal hernia surgery, proper recognition of loose connective tissue, nerves, vas deferens, and microvessels is important to prevent postoperative complications, such as recurrence, pain, sexual dysfunction, and bleeding. EUREKA (Anaut Inc., Tokyo, Japan) is a system that uses artificial intelligence (AI) for anatomical recognition. This system can intraoperatively confirm the aforementioned anatomical landmarks. In this study, we validated the accuracy of EUREKA in recognizing dissection layers, nerves, vas deferens, and microvessels during transabdominal preperitoneal inguinal hernia repair (TAPP).
Methods
We used TAPP videos to compare EUREKA’s recognition of loose connective tissue, nerves, vas deferens, and microvessels with the original surgical video and examined whether EUREKA accurately identified these structures. Intersection over Union (IoU) and F1/Dice scores were calculated to quantitively evaluate AI predictive images.
Results
The mean IoU and F1/Dice scores were 0.33 and 0.50 for connective tissue, 0.24 and 0.38 for nerves, 0.50 and 0.66 for the vas deferens, and 0.30 and 0.45 for microvessels, respectively. Compared with the images without EUREKA visualization, dissection layers were very clearly recognized and displayed when appropriate tension was applied.</description><subject>Abdominal Surgery</subject><subject>Anatomic Landmarks</subject><subject>Artificial Intelligence</subject><subject>Complications</subject><subject>Connective tissue</subject><subject>Connective Tissue - anatomy & histology</subject><subject>Connective tissues</subject><subject>Dissection</subject><subject>Dissection - methods</subject><subject>Hernia</subject><subject>Hernia, Inguinal - surgery</subject><subject>Hernias</subject><subject>Herniorrhaphy - methods</subject><subject>How-I-Do-It</subject><subject>Humans</subject><subject>Laparoscopy</subject><subject>Laparoscopy - methods</subject><subject>Male</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Microvessels - anatomy & histology</subject><subject>Nerves</subject><subject>Postoperative</subject><subject>Vas deferens</subject><subject>Vas Deferens - anatomy & histology</subject><subject>Vas Deferens - surgery</subject><issn>1248-9204</issn><issn>1265-4906</issn><issn>1248-9204</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><recordid>eNp9ks1u1DAQxyMEoqXwAhxQJC4cCPgzXp9QVfElVeqlnC0nHqeusnawk5X6Tn1IZjellB568Xg8v_nP2J6qekvJJ0qI-lxwZbohTDSEM8Yb-aw6pkxsGs2IeP5gf1S9KuWaELIR7eZldcS1YkJofVzdnkY7p23o7Vhn6NMQwxxSrJOvXSgF-oM32hvI5WMdIe8A7c6W2oGHDBE9G12NCjlhrMBY6qWEONQ2z8GHPqByiDOMYxgg9lC7Je_Dc7ax2M5h8YjIlGGCHOYU4ZAwLIfjK8gxWGxtsiG_rl54OxZ4c2dPql_fvl6e_WjOL77_PDs9b3rGlGxYKyjvJG5FK4UjkipBvfcUFO9IpzQF12nOuPMgVcsQIE5Z54nUqkXopPqy6k5LtwXXQ8RmRzPlsLX5xiQbzP-RGK7MkHaG0lZR2VJU-HCnkNPvBcpstqH0-AY2QlqK4VRoKUgrN4i-f4RepyXj3VeKM6r5XpCtFD5zKRn8fTeUmP00mHUaDE6DOUyDkZj07uE97lP-fj8CfAXKtP8TyP9qPyH7BxTkxJk</recordid><startdate>20241226</startdate><enddate>20241226</enddate><creator>Mita, Kazuhito</creator><creator>Kobayashi, Nao</creator><creator>Takahashi, Kunihiko</creator><creator>Sakai, Takashi</creator><creator>Shimaguchi, Mayu</creator><creator>Kouno, Michitaka</creator><creator>Toyota, Naoyuki</creator><creator>Hatano, Minoru</creator><creator>Toyota, Tsuyoshi</creator><creator>Sasaki, Junichi</creator><general>Springer Paris</general><general>Springer Nature B.V</general><scope>C6C</scope><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>7T5</scope><scope>H94</scope><scope>K9.</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-1516-8442</orcidid></search><sort><creationdate>20241226</creationdate><title>Anatomical recognition of dissection layers, nerves, vas deferens, and microvessels using artificial intelligence during transabdominal preperitoneal inguinal hernia repair</title><author>Mita, Kazuhito ; Kobayashi, Nao ; Takahashi, Kunihiko ; Sakai, Takashi ; Shimaguchi, Mayu ; Kouno, Michitaka ; Toyota, Naoyuki ; Hatano, Minoru ; Toyota, Tsuyoshi ; Sasaki, Junichi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2275-26413b52754654d051741fff1e73b0b791edb9323dfe57620510d7adf05976e73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Abdominal Surgery</topic><topic>Anatomic Landmarks</topic><topic>Artificial Intelligence</topic><topic>Complications</topic><topic>Connective tissue</topic><topic>Connective Tissue - anatomy & histology</topic><topic>Connective tissues</topic><topic>Dissection</topic><topic>Dissection - methods</topic><topic>Hernia</topic><topic>Hernia, Inguinal - surgery</topic><topic>Hernias</topic><topic>Herniorrhaphy - methods</topic><topic>How-I-Do-It</topic><topic>Humans</topic><topic>Laparoscopy</topic><topic>Laparoscopy - methods</topic><topic>Male</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Microvessels - anatomy & histology</topic><topic>Nerves</topic><topic>Postoperative</topic><topic>Vas deferens</topic><topic>Vas Deferens - anatomy & histology</topic><topic>Vas Deferens - surgery</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mita, Kazuhito</creatorcontrib><creatorcontrib>Kobayashi, Nao</creatorcontrib><creatorcontrib>Takahashi, Kunihiko</creatorcontrib><creatorcontrib>Sakai, Takashi</creatorcontrib><creatorcontrib>Shimaguchi, Mayu</creatorcontrib><creatorcontrib>Kouno, Michitaka</creatorcontrib><creatorcontrib>Toyota, Naoyuki</creatorcontrib><creatorcontrib>Hatano, Minoru</creatorcontrib><creatorcontrib>Toyota, Tsuyoshi</creatorcontrib><creatorcontrib>Sasaki, Junichi</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Immunology Abstracts</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Hernia : the journal of hernias and abdominal wall surgery</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mita, Kazuhito</au><au>Kobayashi, Nao</au><au>Takahashi, Kunihiko</au><au>Sakai, Takashi</au><au>Shimaguchi, Mayu</au><au>Kouno, Michitaka</au><au>Toyota, Naoyuki</au><au>Hatano, Minoru</au><au>Toyota, Tsuyoshi</au><au>Sasaki, Junichi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Anatomical recognition of dissection layers, nerves, vas deferens, and microvessels using artificial intelligence during transabdominal preperitoneal inguinal hernia repair</atitle><jtitle>Hernia : the journal of hernias and abdominal wall surgery</jtitle><stitle>Hernia</stitle><addtitle>Hernia</addtitle><date>2024-12-26</date><risdate>2024</risdate><volume>29</volume><issue>1</issue><spage>52</spage><pages>52-</pages><artnum>52</artnum><issn>1248-9204</issn><issn>1265-4906</issn><eissn>1248-9204</eissn><abstract>Purpose
In laparoscopic inguinal hernia surgery, proper recognition of loose connective tissue, nerves, vas deferens, and microvessels is important to prevent postoperative complications, such as recurrence, pain, sexual dysfunction, and bleeding. EUREKA (Anaut Inc., Tokyo, Japan) is a system that uses artificial intelligence (AI) for anatomical recognition. This system can intraoperatively confirm the aforementioned anatomical landmarks. In this study, we validated the accuracy of EUREKA in recognizing dissection layers, nerves, vas deferens, and microvessels during transabdominal preperitoneal inguinal hernia repair (TAPP).
Methods
We used TAPP videos to compare EUREKA’s recognition of loose connective tissue, nerves, vas deferens, and microvessels with the original surgical video and examined whether EUREKA accurately identified these structures. Intersection over Union (IoU) and F1/Dice scores were calculated to quantitively evaluate AI predictive images.
Results
The mean IoU and F1/Dice scores were 0.33 and 0.50 for connective tissue, 0.24 and 0.38 for nerves, 0.50 and 0.66 for the vas deferens, and 0.30 and 0.45 for microvessels, respectively. Compared with the images without EUREKA visualization, dissection layers were very clearly recognized and displayed when appropriate tension was applied.</abstract><cop>Paris</cop><pub>Springer Paris</pub><pmid>39724499</pmid><doi>10.1007/s10029-024-03223-5</doi><orcidid>https://orcid.org/0000-0002-1516-8442</orcidid><oa>free_for_read</oa></addata></record> |
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| language | eng |
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| source | MEDLINE; SpringerLink Journals - AutoHoldings |
| subjects | Abdominal Surgery Anatomic Landmarks Artificial Intelligence Complications Connective tissue Connective Tissue - anatomy & histology Connective tissues Dissection Dissection - methods Hernia Hernia, Inguinal - surgery Hernias Herniorrhaphy - methods How-I-Do-It Humans Laparoscopy Laparoscopy - methods Male Medicine Medicine & Public Health Microvessels - anatomy & histology Nerves Postoperative Vas deferens Vas Deferens - anatomy & histology Vas Deferens - surgery |
| title | Anatomical recognition of dissection layers, nerves, vas deferens, and microvessels using artificial intelligence during transabdominal preperitoneal inguinal hernia repair |
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