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Motion analysis of the JHU-ISI Gesture and Skill Assessment Working Set using Robotics Video and Motion Assessment Software
Purpose The JIGSAWS dataset is a fixed dataset of robot-assisted surgery kinematic data used to develop predictive models of skill. The purpose of this study is to analyze the relationships of self-defined skill level with global rating scale scores and kinematic data (time, path length and movement...
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| Published in: | International journal for computer assisted radiology and surgery 2020-12, Vol.15 (12), p.2017-2025 |
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| Main Authors: | , , , |
| Format: | Article |
| Language: | English |
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| cites | cdi_FETCH-LOGICAL-c512t-9c4fcc0e603926e109c0dd72b37fdbdc44bea54729c7a14246536c957875e4dd3 |
| container_end_page | 2025 |
| container_issue | 12 |
| container_start_page | 2017 |
| container_title | International journal for computer assisted radiology and surgery |
| container_volume | 15 |
| creator | Lefor, Alan Kawarai Harada, Kanako Dosis, Aristotelis Mitsuishi, Mamoru |
| description | Purpose
The JIGSAWS dataset is a fixed dataset of robot-assisted surgery kinematic data used to develop predictive models of skill. The purpose of this study is to analyze the relationships of self-defined skill level with global rating scale scores and kinematic data (time, path length and movements) from three exercises (suturing, knot-tying and needle passing) (right and left hands) in the JIGSAWS dataset.
Methods
Global rating scale scores are reported in the JIGSAWS dataset and kinematic data were calculated using ROVIMAS software. Self-defined skill levels are in the dataset (novice, intermediate, expert). Correlation coefficients (global rating scale-skill level and global rating scale-kinematic parameters) were calculated. Kinematic parameters were compared among skill levels.
Results
Global rating scale scores correlated with skill in the knot-tying exercise (
r
= 0.55,
p
= 0.0005). In the suturing exercise, time, path length (left) and movements (left) were significantly different (
p |
| doi_str_mv | 10.1007/s11548-020-02259-z |
| format | article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7671974</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2449178228</sourcerecordid><originalsourceid>FETCH-LOGICAL-c512t-9c4fcc0e603926e109c0dd72b37fdbdc44bea54729c7a14246536c957875e4dd3</originalsourceid><addsrcrecordid>eNp9kUFv1DAQhS1ERUvhD3BAPnIJ2I4dxxekqoJ2UVEllsLRcuzJ1m02Lp4E1PLncbvLqlw4WB5p3nsz9kfIK87ecsb0O-RcybZigpUjlKnunpAD3ja8aqQwT3c1Z_vkOeIVY1LpWj0j-3XNhKqb5oD8_pymmEbqRjfcYkSaejpdAv10elEtlgt6AjjNGUo_0OV1HAZ6hAiIaxgn-j3l6ziu6BImOuN99SV1Jc8j_RYDpAfXdsAj2zL10y-X4QXZ692A8HJ7H5KLjx--Hp9WZ-cni-Ojs8orLqbKeNl7z6BhtRENcGY8C0GLrtZ96IKXsgOnpBbGa8elkE15mjdKt1qBDKE-JO83uTdzt4bgyw7ZDfYmx7XLtza5aP_tjPHSrtJPqxvNjZYl4M02IKcfc_kRu47oYRjcCGlGK6Q0XLdCtEUqNlKfE2KGfjeGM3tPzW6o2ULNPlCzd8X0-vGCO8tfTEVQbwRYWuMKsr1Kcy7I8H-xfwCROKWd</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2449178228</pqid></control><display><type>article</type><title>Motion analysis of the JHU-ISI Gesture and Skill Assessment Working Set using Robotics Video and Motion Assessment Software</title><source>Springer Nature</source><creator>Lefor, Alan Kawarai ; Harada, Kanako ; Dosis, Aristotelis ; Mitsuishi, Mamoru</creator><creatorcontrib>Lefor, Alan Kawarai ; Harada, Kanako ; Dosis, Aristotelis ; Mitsuishi, Mamoru</creatorcontrib><description>Purpose
The JIGSAWS dataset is a fixed dataset of robot-assisted surgery kinematic data used to develop predictive models of skill. The purpose of this study is to analyze the relationships of self-defined skill level with global rating scale scores and kinematic data (time, path length and movements) from three exercises (suturing, knot-tying and needle passing) (right and left hands) in the JIGSAWS dataset.
Methods
Global rating scale scores are reported in the JIGSAWS dataset and kinematic data were calculated using ROVIMAS software. Self-defined skill levels are in the dataset (novice, intermediate, expert). Correlation coefficients (global rating scale-skill level and global rating scale-kinematic parameters) were calculated. Kinematic parameters were compared among skill levels.
Results
Global rating scale scores correlated with skill in the knot-tying exercise (
r
= 0.55,
p
= 0.0005). In the suturing exercise, time, path length (left) and movements (left) were significantly different (
p
< 0.05) for novices and experts. For knot-tying, time, path length (right and left) and movements (right) differed significantly for novices and experts. For needle passing, no kinematic parameter was significantly different comparing novices and experts. The only kinematic parameter that correlated with global rating scale scores is time in the knot-tying exercise.
Conclusion
Global rating scale scores weakly correlate with skill level and kinematic parameters. The ability of kinematic parameters to differentiate among self-defined skill levels is inconsistent. Additional data are needed to enhance the dataset and facilitate subset analyses and future model development.</description><identifier>ISSN: 1861-6410</identifier><identifier>EISSN: 1861-6429</identifier><identifier>DOI: 10.1007/s11548-020-02259-z</identifier><identifier>PMID: 33025366</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Biomechanical Phenomena ; Clinical Competence ; Computer Imaging ; Computer Science ; Gestures ; Health Informatics ; Humans ; Imaging ; Laparoscopy - education ; Medicine ; Medicine & Public Health ; Motion ; Original ; Original Article ; Pattern Recognition and Graphics ; Radiology ; Robotic Surgical Procedures ; Simulation Training ; Software ; Surgery ; Suture Techniques - education ; Sutures ; Vision</subject><ispartof>International journal for computer assisted radiology and surgery, 2020-12, Vol.15 (12), p.2017-2025</ispartof><rights>The Author(s) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c512t-9c4fcc0e603926e109c0dd72b37fdbdc44bea54729c7a14246536c957875e4dd3</citedby><cites>FETCH-LOGICAL-c512t-9c4fcc0e603926e109c0dd72b37fdbdc44bea54729c7a14246536c957875e4dd3</cites><orcidid>0000-0001-6673-5630</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33025366$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lefor, Alan Kawarai</creatorcontrib><creatorcontrib>Harada, Kanako</creatorcontrib><creatorcontrib>Dosis, Aristotelis</creatorcontrib><creatorcontrib>Mitsuishi, Mamoru</creatorcontrib><title>Motion analysis of the JHU-ISI Gesture and Skill Assessment Working Set using Robotics Video and Motion Assessment Software</title><title>International journal for computer assisted radiology and surgery</title><addtitle>Int J CARS</addtitle><addtitle>Int J Comput Assist Radiol Surg</addtitle><description>Purpose
The JIGSAWS dataset is a fixed dataset of robot-assisted surgery kinematic data used to develop predictive models of skill. The purpose of this study is to analyze the relationships of self-defined skill level with global rating scale scores and kinematic data (time, path length and movements) from three exercises (suturing, knot-tying and needle passing) (right and left hands) in the JIGSAWS dataset.
Methods
Global rating scale scores are reported in the JIGSAWS dataset and kinematic data were calculated using ROVIMAS software. Self-defined skill levels are in the dataset (novice, intermediate, expert). Correlation coefficients (global rating scale-skill level and global rating scale-kinematic parameters) were calculated. Kinematic parameters were compared among skill levels.
Results
Global rating scale scores correlated with skill in the knot-tying exercise (
r
= 0.55,
p
= 0.0005). In the suturing exercise, time, path length (left) and movements (left) were significantly different (
p
< 0.05) for novices and experts. For knot-tying, time, path length (right and left) and movements (right) differed significantly for novices and experts. For needle passing, no kinematic parameter was significantly different comparing novices and experts. The only kinematic parameter that correlated with global rating scale scores is time in the knot-tying exercise.
Conclusion
Global rating scale scores weakly correlate with skill level and kinematic parameters. The ability of kinematic parameters to differentiate among self-defined skill levels is inconsistent. Additional data are needed to enhance the dataset and facilitate subset analyses and future model development.</description><subject>Biomechanical Phenomena</subject><subject>Clinical Competence</subject><subject>Computer Imaging</subject><subject>Computer Science</subject><subject>Gestures</subject><subject>Health Informatics</subject><subject>Humans</subject><subject>Imaging</subject><subject>Laparoscopy - education</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Motion</subject><subject>Original</subject><subject>Original Article</subject><subject>Pattern Recognition and Graphics</subject><subject>Radiology</subject><subject>Robotic Surgical Procedures</subject><subject>Simulation Training</subject><subject>Software</subject><subject>Surgery</subject><subject>Suture Techniques - education</subject><subject>Sutures</subject><subject>Vision</subject><issn>1861-6410</issn><issn>1861-6429</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kUFv1DAQhS1ERUvhD3BAPnIJ2I4dxxekqoJ2UVEllsLRcuzJ1m02Lp4E1PLncbvLqlw4WB5p3nsz9kfIK87ecsb0O-RcybZigpUjlKnunpAD3ja8aqQwT3c1Z_vkOeIVY1LpWj0j-3XNhKqb5oD8_pymmEbqRjfcYkSaejpdAv10elEtlgt6AjjNGUo_0OV1HAZ6hAiIaxgn-j3l6ziu6BImOuN99SV1Jc8j_RYDpAfXdsAj2zL10y-X4QXZ692A8HJ7H5KLjx--Hp9WZ-cni-Ojs8orLqbKeNl7z6BhtRENcGY8C0GLrtZ96IKXsgOnpBbGa8elkE15mjdKt1qBDKE-JO83uTdzt4bgyw7ZDfYmx7XLtza5aP_tjPHSrtJPqxvNjZYl4M02IKcfc_kRu47oYRjcCGlGK6Q0XLdCtEUqNlKfE2KGfjeGM3tPzW6o2ULNPlCzd8X0-vGCO8tfTEVQbwRYWuMKsr1Kcy7I8H-xfwCROKWd</recordid><startdate>20201201</startdate><enddate>20201201</enddate><creator>Lefor, Alan Kawarai</creator><creator>Harada, Kanako</creator><creator>Dosis, Aristotelis</creator><creator>Mitsuishi, Mamoru</creator><general>Springer International Publishing</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>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-6673-5630</orcidid></search><sort><creationdate>20201201</creationdate><title>Motion analysis of the JHU-ISI Gesture and Skill Assessment Working Set using Robotics Video and Motion Assessment Software</title><author>Lefor, Alan Kawarai ; Harada, Kanako ; Dosis, Aristotelis ; Mitsuishi, Mamoru</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c512t-9c4fcc0e603926e109c0dd72b37fdbdc44bea54729c7a14246536c957875e4dd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Biomechanical Phenomena</topic><topic>Clinical Competence</topic><topic>Computer Imaging</topic><topic>Computer Science</topic><topic>Gestures</topic><topic>Health Informatics</topic><topic>Humans</topic><topic>Imaging</topic><topic>Laparoscopy - education</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Motion</topic><topic>Original</topic><topic>Original Article</topic><topic>Pattern Recognition and Graphics</topic><topic>Radiology</topic><topic>Robotic Surgical Procedures</topic><topic>Simulation Training</topic><topic>Software</topic><topic>Surgery</topic><topic>Suture Techniques - education</topic><topic>Sutures</topic><topic>Vision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lefor, Alan Kawarai</creatorcontrib><creatorcontrib>Harada, Kanako</creatorcontrib><creatorcontrib>Dosis, Aristotelis</creatorcontrib><creatorcontrib>Mitsuishi, Mamoru</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>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>International journal for computer assisted radiology and surgery</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lefor, Alan Kawarai</au><au>Harada, Kanako</au><au>Dosis, Aristotelis</au><au>Mitsuishi, Mamoru</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Motion analysis of the JHU-ISI Gesture and Skill Assessment Working Set using Robotics Video and Motion Assessment Software</atitle><jtitle>International journal for computer assisted radiology and surgery</jtitle><stitle>Int J CARS</stitle><addtitle>Int J Comput Assist Radiol Surg</addtitle><date>2020-12-01</date><risdate>2020</risdate><volume>15</volume><issue>12</issue><spage>2017</spage><epage>2025</epage><pages>2017-2025</pages><issn>1861-6410</issn><eissn>1861-6429</eissn><abstract>Purpose
The JIGSAWS dataset is a fixed dataset of robot-assisted surgery kinematic data used to develop predictive models of skill. The purpose of this study is to analyze the relationships of self-defined skill level with global rating scale scores and kinematic data (time, path length and movements) from three exercises (suturing, knot-tying and needle passing) (right and left hands) in the JIGSAWS dataset.
Methods
Global rating scale scores are reported in the JIGSAWS dataset and kinematic data were calculated using ROVIMAS software. Self-defined skill levels are in the dataset (novice, intermediate, expert). Correlation coefficients (global rating scale-skill level and global rating scale-kinematic parameters) were calculated. Kinematic parameters were compared among skill levels.
Results
Global rating scale scores correlated with skill in the knot-tying exercise (
r
= 0.55,
p
= 0.0005). In the suturing exercise, time, path length (left) and movements (left) were significantly different (
p
< 0.05) for novices and experts. For knot-tying, time, path length (right and left) and movements (right) differed significantly for novices and experts. For needle passing, no kinematic parameter was significantly different comparing novices and experts. The only kinematic parameter that correlated with global rating scale scores is time in the knot-tying exercise.
Conclusion
Global rating scale scores weakly correlate with skill level and kinematic parameters. The ability of kinematic parameters to differentiate among self-defined skill levels is inconsistent. Additional data are needed to enhance the dataset and facilitate subset analyses and future model development.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>33025366</pmid><doi>10.1007/s11548-020-02259-z</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0001-6673-5630</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | International journal for computer assisted radiology and surgery, 2020-12, Vol.15 (12), p.2017-2025 |
| issn | 1861-6410 1861-6429 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7671974 |
| source | Springer Nature |
| subjects | Biomechanical Phenomena Clinical Competence Computer Imaging Computer Science Gestures Health Informatics Humans Imaging Laparoscopy - education Medicine Medicine & Public Health Motion Original Original Article Pattern Recognition and Graphics Radiology Robotic Surgical Procedures Simulation Training Software Surgery Suture Techniques - education Sutures Vision |
| title | Motion analysis of the JHU-ISI Gesture and Skill Assessment Working Set using Robotics Video and Motion Assessment Software |
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