Video analysis in basic skills training: a way to expand the value and use of BlackBox training?
Background Basic skills training in laparoscopic high-fidelity simulators (LHFS) improves laparoscopic skills. However, since LHFS are expensive, their availability is limited. The aim of this study was to assess whether automated video analysis of low-cost BlackBox laparoscopic training could provi...
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| Veröffentlicht in: | Surgical endoscopy 2018-01, Vol.32 (1), p.87-95 |
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| creator | Oussi, Ninos Loukas, Constantinos Kjellin, Ann Lahanas, Vasileios Georgiou, Konstantinos Henningsohn, Lars Felländer-Tsai, Li Georgiou, Evangelos Enochsson, Lars |
| description | Background
Basic skills training in laparoscopic high-fidelity simulators (LHFS) improves laparoscopic skills. However, since LHFS are expensive, their availability is limited. The aim of this study was to assess whether automated video analysis of low-cost BlackBox laparoscopic training could provide an alternative to LHFS in basic skills training.
Methods
Medical students volunteered to participate during their surgical semester at the Karolinska University Hospital. After written informed consent, they performed two laparoscopic tasks (PEG-transfer and precision-cutting) on a BlackBox trainer. All tasks were videotaped and sent to MPLSC for automated video analysis, generating two parameters (Pl and Prtcl_tot) that assess the total motion activity. The students then carried out final tests on the MIST-VR simulator. This study was a European collaboration among two simulation centers, located in Sweden and Greece, within the framework of ACS-AEI.
Results
31 students (19 females and 12 males), mean age of 26.2 ± 0.8 years, participated in the study. However, since two of the students completed only one of the three MIST-VR tasks, they were excluded. The three MIST-VR scores showed significant positive correlations to both the Pl variable in the automated video analysis of the PEG-transfer (RSquare 0.48,
P
|
| doi_str_mv | 10.1007/s00464-017-5641-7 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_swepu</sourceid><recordid>TN_cdi_swepub_primary_oai_swepub_ki_se_495728</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1915342297</sourcerecordid><originalsourceid>FETCH-LOGICAL-c583t-8e44e4b295b162fc486a16a1e92e9576162a1e9b116a09e21071f66f515bf1c23</originalsourceid><addsrcrecordid>eNp9kk1vEzEQhi0EoqHwA7ggS1w4sOBvrzkUteVTqsQFejXezWzqxlkHe7dt_j1eJYQWqUiW7Bk_89oevwg9p-QNJUS_zYQIJSpCdSWVoJV-gGZUcFYxRuuHaEYMJxXTRhygJzlfkoIbKh-jA1YrJQSXM_Tz3M8hYte7sMk-Y9_jxmXf4rz0IWQ8JOd73y_eYYev3QYPEcPN2vVzPFwAvnJhBDxFYwYcO3wSXLs8iTf7uvdP0aPOhQzPdvMh-vHp4_fTL9XZt89fT4_PqlbWfKhqEAJEw4xsqGJdK2rlaBlgGBipVUlOQUNLkhhglGjaKdVJKpuOtowfomqrm69hPTZ2nfzKpY2NzttdallWYEWRY3XhX9_Lf_DnxzamhR1HyzlTRvxX_i--Gi0VwihT-KMtX-AVzFvoS0fCnbK7O72_sIt4ZaXWRJLpfq92Ain-GiEPduVzCyG4HuKYLS0_yQVjRhf05T_oZRxT-dGJMqzWXIqpQXRLtSnmnKDbX4YSO9nJbu1ki53sZCc7Kb-4_Yp9xR__FIDt2lK2-gWkW0ffq_obhnTVkA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1992873542</pqid></control><display><type>article</type><title>Video analysis in basic skills training: a way to expand the value and use of BlackBox training?</title><source>MEDLINE</source><source>SWEPUB Freely available online</source><source>SpringerLink Journals - AutoHoldings</source><creator>Oussi, Ninos ; Loukas, Constantinos ; Kjellin, Ann ; Lahanas, Vasileios ; Georgiou, Konstantinos ; Henningsohn, Lars ; Felländer-Tsai, Li ; Georgiou, Evangelos ; Enochsson, Lars</creator><creatorcontrib>Oussi, Ninos ; Loukas, Constantinos ; Kjellin, Ann ; Lahanas, Vasileios ; Georgiou, Konstantinos ; Henningsohn, Lars ; Felländer-Tsai, Li ; Georgiou, Evangelos ; Enochsson, Lars</creatorcontrib><description>Background
Basic skills training in laparoscopic high-fidelity simulators (LHFS) improves laparoscopic skills. However, since LHFS are expensive, their availability is limited. The aim of this study was to assess whether automated video analysis of low-cost BlackBox laparoscopic training could provide an alternative to LHFS in basic skills training.
Methods
Medical students volunteered to participate during their surgical semester at the Karolinska University Hospital. After written informed consent, they performed two laparoscopic tasks (PEG-transfer and precision-cutting) on a BlackBox trainer. All tasks were videotaped and sent to MPLSC for automated video analysis, generating two parameters (Pl and Prtcl_tot) that assess the total motion activity. The students then carried out final tests on the MIST-VR simulator. This study was a European collaboration among two simulation centers, located in Sweden and Greece, within the framework of ACS-AEI.
Results
31 students (19 females and 12 males), mean age of 26.2 ± 0.8 years, participated in the study. However, since two of the students completed only one of the three MIST-VR tasks, they were excluded. The three MIST-VR scores showed significant positive correlations to both the Pl variable in the automated video analysis of the PEG-transfer (RSquare 0.48,
P
< 0.0001; 0.34,
P
= 0.0009; 0.45,
P
< 0.0001, respectively) as well as to the Prtcl_tot variable in that same exercise (RSquare 0.42,
P
= 0.0002; 0.29,
P
= 0.0024; 0.45,
P
< 0.0001). However, the correlations were exclusively shown in the group with less PC gaming experience as well as in the female group.
Conclusions
Automated video analysis provides accurate results in line with those of the validated MIST-VR. We believe that a more frequent use of automated video analysis could provide an extended value to cost-efficient laparoscopic BlackBox training. However, since there are gender-specific as well as PC gaming experience differences, this should be taken in account regarding the value of automated video analysis.</description><identifier>ISSN: 0930-2794</identifier><identifier>ISSN: 1432-2218</identifier><identifier>EISSN: 1432-2218</identifier><identifier>DOI: 10.1007/s00464-017-5641-7</identifier><identifier>PMID: 28664435</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Abdominal Surgery ; Adult ; Automation ; BlackBox trainer ; Clinical Competence - statistics & numerical data ; Computer Simulation - statistics & numerical data ; Education, Medical, Undergraduate - methods ; Female ; Frogs ; Gastroenterology ; Gynecology ; Hepatology ; Humans ; Laparoscopy ; Laparoscopy - education ; Male ; Medical students ; Medicine ; Medicine & Public Health ; MIST-VR simulation ; Proctology ; Skill development ; Students ; Surgery ; Training ; Video analysis ; Video Recording - methods ; Virtual reality</subject><ispartof>Surgical endoscopy, 2018-01, Vol.32 (1), p.87-95</ispartof><rights>The Author(s) 2017</rights><rights>Surgical Endoscopy is a copyright of Springer, (2017). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c583t-8e44e4b295b162fc486a16a1e92e9576162a1e9b116a09e21071f66f515bf1c23</citedby><cites>FETCH-LOGICAL-c583t-8e44e4b295b162fc486a16a1e92e9576162a1e9b116a09e21071f66f515bf1c23</cites><orcidid>0000-0002-1930-654X</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/s00464-017-5641-7$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00464-017-5641-7$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,552,778,782,883,27907,27908,41471,42540,51302</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28664435$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-144969$$DView record from Swedish Publication Index$$Hfree_for_read</backlink><backlink>$$Uhttps://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-332694$$DView record from Swedish Publication Index$$Hfree_for_read</backlink><backlink>$$Uhttp://kipublications.ki.se/Default.aspx?queryparsed=id:137547811$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><creatorcontrib>Oussi, Ninos</creatorcontrib><creatorcontrib>Loukas, Constantinos</creatorcontrib><creatorcontrib>Kjellin, Ann</creatorcontrib><creatorcontrib>Lahanas, Vasileios</creatorcontrib><creatorcontrib>Georgiou, Konstantinos</creatorcontrib><creatorcontrib>Henningsohn, Lars</creatorcontrib><creatorcontrib>Felländer-Tsai, Li</creatorcontrib><creatorcontrib>Georgiou, Evangelos</creatorcontrib><creatorcontrib>Enochsson, Lars</creatorcontrib><title>Video analysis in basic skills training: a way to expand the value and use of BlackBox training?</title><title>Surgical endoscopy</title><addtitle>Surg Endosc</addtitle><addtitle>Surg Endosc</addtitle><description>Background
Basic skills training in laparoscopic high-fidelity simulators (LHFS) improves laparoscopic skills. However, since LHFS are expensive, their availability is limited. The aim of this study was to assess whether automated video analysis of low-cost BlackBox laparoscopic training could provide an alternative to LHFS in basic skills training.
Methods
Medical students volunteered to participate during their surgical semester at the Karolinska University Hospital. After written informed consent, they performed two laparoscopic tasks (PEG-transfer and precision-cutting) on a BlackBox trainer. All tasks were videotaped and sent to MPLSC for automated video analysis, generating two parameters (Pl and Prtcl_tot) that assess the total motion activity. The students then carried out final tests on the MIST-VR simulator. This study was a European collaboration among two simulation centers, located in Sweden and Greece, within the framework of ACS-AEI.
Results
31 students (19 females and 12 males), mean age of 26.2 ± 0.8 years, participated in the study. However, since two of the students completed only one of the three MIST-VR tasks, they were excluded. The three MIST-VR scores showed significant positive correlations to both the Pl variable in the automated video analysis of the PEG-transfer (RSquare 0.48,
P
< 0.0001; 0.34,
P
= 0.0009; 0.45,
P
< 0.0001, respectively) as well as to the Prtcl_tot variable in that same exercise (RSquare 0.42,
P
= 0.0002; 0.29,
P
= 0.0024; 0.45,
P
< 0.0001). However, the correlations were exclusively shown in the group with less PC gaming experience as well as in the female group.
Conclusions
Automated video analysis provides accurate results in line with those of the validated MIST-VR. We believe that a more frequent use of automated video analysis could provide an extended value to cost-efficient laparoscopic BlackBox training. However, since there are gender-specific as well as PC gaming experience differences, this should be taken in account regarding the value of automated video analysis.</description><subject>Abdominal Surgery</subject><subject>Adult</subject><subject>Automation</subject><subject>BlackBox trainer</subject><subject>Clinical Competence - statistics & numerical data</subject><subject>Computer Simulation - statistics & numerical data</subject><subject>Education, Medical, Undergraduate - methods</subject><subject>Female</subject><subject>Frogs</subject><subject>Gastroenterology</subject><subject>Gynecology</subject><subject>Hepatology</subject><subject>Humans</subject><subject>Laparoscopy</subject><subject>Laparoscopy - education</subject><subject>Male</subject><subject>Medical students</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>MIST-VR simulation</subject><subject>Proctology</subject><subject>Skill development</subject><subject>Students</subject><subject>Surgery</subject><subject>Training</subject><subject>Video analysis</subject><subject>Video Recording - methods</subject><subject>Virtual reality</subject><issn>0930-2794</issn><issn>1432-2218</issn><issn>1432-2218</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>D8T</sourceid><recordid>eNp9kk1vEzEQhi0EoqHwA7ggS1w4sOBvrzkUteVTqsQFejXezWzqxlkHe7dt_j1eJYQWqUiW7Bk_89oevwg9p-QNJUS_zYQIJSpCdSWVoJV-gGZUcFYxRuuHaEYMJxXTRhygJzlfkoIbKh-jA1YrJQSXM_Tz3M8hYte7sMk-Y9_jxmXf4rz0IWQ8JOd73y_eYYev3QYPEcPN2vVzPFwAvnJhBDxFYwYcO3wSXLs8iTf7uvdP0aPOhQzPdvMh-vHp4_fTL9XZt89fT4_PqlbWfKhqEAJEw4xsqGJdK2rlaBlgGBipVUlOQUNLkhhglGjaKdVJKpuOtowfomqrm69hPTZ2nfzKpY2NzttdallWYEWRY3XhX9_Lf_DnxzamhR1HyzlTRvxX_i--Gi0VwihT-KMtX-AVzFvoS0fCnbK7O72_sIt4ZaXWRJLpfq92Ain-GiEPduVzCyG4HuKYLS0_yQVjRhf05T_oZRxT-dGJMqzWXIqpQXRLtSnmnKDbX4YSO9nJbu1ki53sZCc7Kb-4_Yp9xR__FIDt2lK2-gWkW0ffq_obhnTVkA</recordid><startdate>20180101</startdate><enddate>20180101</enddate><creator>Oussi, Ninos</creator><creator>Loukas, Constantinos</creator><creator>Kjellin, Ann</creator><creator>Lahanas, Vasileios</creator><creator>Georgiou, Konstantinos</creator><creator>Henningsohn, Lars</creator><creator>Felländer-Tsai, Li</creator><creator>Georgiou, Evangelos</creator><creator>Enochsson, Lars</creator><general>Springer US</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>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M1P</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>ADHXS</scope><scope>ADTPV</scope><scope>AOWAS</scope><scope>D8T</scope><scope>D93</scope><scope>ZZAVC</scope><scope>ACNBI</scope><scope>DF2</scope><orcidid>https://orcid.org/0000-0002-1930-654X</orcidid></search><sort><creationdate>20180101</creationdate><title>Video analysis in basic skills training: a way to expand the value and use of BlackBox training?</title><author>Oussi, Ninos ; Loukas, Constantinos ; Kjellin, Ann ; Lahanas, Vasileios ; Georgiou, Konstantinos ; Henningsohn, Lars ; Felländer-Tsai, Li ; Georgiou, Evangelos ; Enochsson, Lars</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c583t-8e44e4b295b162fc486a16a1e92e9576162a1e9b116a09e21071f66f515bf1c23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Abdominal Surgery</topic><topic>Adult</topic><topic>Automation</topic><topic>BlackBox trainer</topic><topic>Clinical Competence - statistics & numerical data</topic><topic>Computer Simulation - statistics & numerical data</topic><topic>Education, Medical, Undergraduate - methods</topic><topic>Female</topic><topic>Frogs</topic><topic>Gastroenterology</topic><topic>Gynecology</topic><topic>Hepatology</topic><topic>Humans</topic><topic>Laparoscopy</topic><topic>Laparoscopy - education</topic><topic>Male</topic><topic>Medical students</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>MIST-VR simulation</topic><topic>Proctology</topic><topic>Skill development</topic><topic>Students</topic><topic>Surgery</topic><topic>Training</topic><topic>Video analysis</topic><topic>Video Recording - methods</topic><topic>Virtual reality</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Oussi, Ninos</creatorcontrib><creatorcontrib>Loukas, Constantinos</creatorcontrib><creatorcontrib>Kjellin, Ann</creatorcontrib><creatorcontrib>Lahanas, Vasileios</creatorcontrib><creatorcontrib>Georgiou, Konstantinos</creatorcontrib><creatorcontrib>Henningsohn, Lars</creatorcontrib><creatorcontrib>Felländer-Tsai, Li</creatorcontrib><creatorcontrib>Georgiou, Evangelos</creatorcontrib><creatorcontrib>Enochsson, Lars</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>ProQuest Central (Corporate)</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>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>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>SWEPUB Umeå universitet full text</collection><collection>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Freely available online</collection><collection>SWEPUB Umeå universitet</collection><collection>SwePub Articles full text</collection><collection>SWEPUB Uppsala universitet full text</collection><collection>SWEPUB Uppsala universitet</collection><jtitle>Surgical endoscopy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Oussi, Ninos</au><au>Loukas, Constantinos</au><au>Kjellin, Ann</au><au>Lahanas, Vasileios</au><au>Georgiou, Konstantinos</au><au>Henningsohn, Lars</au><au>Felländer-Tsai, Li</au><au>Georgiou, Evangelos</au><au>Enochsson, Lars</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Video analysis in basic skills training: a way to expand the value and use of BlackBox training?</atitle><jtitle>Surgical endoscopy</jtitle><stitle>Surg Endosc</stitle><addtitle>Surg Endosc</addtitle><date>2018-01-01</date><risdate>2018</risdate><volume>32</volume><issue>1</issue><spage>87</spage><epage>95</epage><pages>87-95</pages><issn>0930-2794</issn><issn>1432-2218</issn><eissn>1432-2218</eissn><abstract>Background
Basic skills training in laparoscopic high-fidelity simulators (LHFS) improves laparoscopic skills. However, since LHFS are expensive, their availability is limited. The aim of this study was to assess whether automated video analysis of low-cost BlackBox laparoscopic training could provide an alternative to LHFS in basic skills training.
Methods
Medical students volunteered to participate during their surgical semester at the Karolinska University Hospital. After written informed consent, they performed two laparoscopic tasks (PEG-transfer and precision-cutting) on a BlackBox trainer. All tasks were videotaped and sent to MPLSC for automated video analysis, generating two parameters (Pl and Prtcl_tot) that assess the total motion activity. The students then carried out final tests on the MIST-VR simulator. This study was a European collaboration among two simulation centers, located in Sweden and Greece, within the framework of ACS-AEI.
Results
31 students (19 females and 12 males), mean age of 26.2 ± 0.8 years, participated in the study. However, since two of the students completed only one of the three MIST-VR tasks, they were excluded. The three MIST-VR scores showed significant positive correlations to both the Pl variable in the automated video analysis of the PEG-transfer (RSquare 0.48,
P
< 0.0001; 0.34,
P
= 0.0009; 0.45,
P
< 0.0001, respectively) as well as to the Prtcl_tot variable in that same exercise (RSquare 0.42,
P
= 0.0002; 0.29,
P
= 0.0024; 0.45,
P
< 0.0001). However, the correlations were exclusively shown in the group with less PC gaming experience as well as in the female group.
Conclusions
Automated video analysis provides accurate results in line with those of the validated MIST-VR. We believe that a more frequent use of automated video analysis could provide an extended value to cost-efficient laparoscopic BlackBox training. However, since there are gender-specific as well as PC gaming experience differences, this should be taken in account regarding the value of automated video analysis.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>28664435</pmid><doi>10.1007/s00464-017-5641-7</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-1930-654X</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Surgical endoscopy, 2018-01, Vol.32 (1), p.87-95 |
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| language | eng |
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| source | MEDLINE; SWEPUB Freely available online; SpringerLink Journals - AutoHoldings |
| subjects | Abdominal Surgery Adult Automation BlackBox trainer Clinical Competence - statistics & numerical data Computer Simulation - statistics & numerical data Education, Medical, Undergraduate - methods Female Frogs Gastroenterology Gynecology Hepatology Humans Laparoscopy Laparoscopy - education Male Medical students Medicine Medicine & Public Health MIST-VR simulation Proctology Skill development Students Surgery Training Video analysis Video Recording - methods Virtual reality |
| title | Video analysis in basic skills training: a way to expand the value and use of BlackBox training? |
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