Markov modeling of minimally invasive surgery based on tool/tissue interaction and force/torque signatures for evaluating surgical skills
Identifieur interne : 001400 ( PascalFrancis/Corpus ); précédent : 001399; suivant : 001401Markov modeling of minimally invasive surgery based on tool/tissue interaction and force/torque signatures for evaluating surgical skills
Auteurs : Jacob Rosen ; Blake Hannaford ; Christina G. Richards ; Mika N. SinananSource :
- IEEE transactions on biomedical engineering [ 0018-9294 ] ; 2001.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
The best method of training for laparoscopic surgical skills is controversial. Some advocate observation in the operating room, while others promote animal and simulated models or a combination of surgery-related tasks. A crucial process in surgical education is to evaluate the level of surgical skills. For laparoscopic surgery, skill evaluation is traditionally performed subjectively by experts grading a video of a procedure performed by a student. By its nature, this process uses fuzzy criteria. The objective of the current study was to develop and assess a skill scale using Markov models (MMs). Ten surgeons [five novice surgeons (NS); five expert surgeons (ES)] performed a cholecystectomy and Nissen fundopli-cation in a porcine model. An instrumented laparoscopic grasper equipped with a three-axis force/torque (F/T) sensor was used to measure the forces/torques at the hand/tool interface synchronized with a video of the tool operative maneuvers. A synthesis of frame-by-frame video analysis and a vector quantization algorithm, allowed to define F/T signatures associated with 14 different types of tool/tissue interactions. The magnitude of F/T applied by NS and ES were significantly different (p < 0.05) and varied based on the task being performed. High F/T magnitudes were applied by NS compared with ES while performing tissue manipulation and vise versa in tasks involved tissue dissection. From each step of the surgical procedures, two MMs were developed representing the performance of three surgeons out of the five in the ES and NS groups. The data obtained by the remaining two surgeons in each group were used for evaluating the performance scale. The final result was a surgical performance index which represented a ratio of statistical similarity between the examined surgeon's MM and the MM of NS and ES. The difference between the performance index value, for a surgeon under study, and the NS/ES boundary, indicated the level of expertise in the surgeon's own group. Using this index, 87.5% of the surgical procedures were correctly classified into the NS and ES groups. The 12.5% of the procedures that were misclassified were performed by the ES and classified as NS. However in these cases the performance index values were very dose to the NS/ES boundary. Preliminary data suggest that a performance index based on MM and F/T signatures provides an objective means of distinguishing NS from ES. In addition, this methodology can be further applied to evaluate haptic virtual reality surgical simulators for improving realism in surgical education.
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Pour connaître la documentation sur le format Inist Standard.
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Format Inist (serveur)
| NO : | PASCAL 01-0299375 INIST |
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| ET : | Markov modeling of minimally invasive surgery based on tool/tissue interaction and force/torque signatures for evaluating surgical skills |
| AU : | ROSEN (Jacob); HANNAFORD (Blake); RICHARDS (Christina G.); SINANAN (Mika N.) |
| AF : | Department of Electrical Engineering, University of Washington/Seattle, WA 98195/Etats-Unis (1 aut.); Department of Electrical Engineering, Box 352500, University of Washington/Seattle, WA 98195/Etats-Unis (2 aut.); Department of Surgery, University of Washington/Seattle, WA 98195/Etats-Unis (3 aut., 4 aut.) |
| DT : | Publication en série; Niveau analytique |
| SO : | IEEE transactions on biomedical engineering; ISSN 0018-9294; Coden IEBEAX; Etats-Unis; Da. 2001; Vol. 48; No. 5; Pp. 579-591; Bibl. 48 ref. |
| LA : | Anglais |
| EA : | The best method of training for laparoscopic surgical skills is controversial. Some advocate observation in the operating room, while others promote animal and simulated models or a combination of surgery-related tasks. A crucial process in surgical education is to evaluate the level of surgical skills. For laparoscopic surgery, skill evaluation is traditionally performed subjectively by experts grading a video of a procedure performed by a student. By its nature, this process uses fuzzy criteria. The objective of the current study was to develop and assess a skill scale using Markov models (MMs). Ten surgeons [five novice surgeons (NS); five expert surgeons (ES)] performed a cholecystectomy and Nissen fundopli-cation in a porcine model. An instrumented laparoscopic grasper equipped with a three-axis force/torque (F/T) sensor was used to measure the forces/torques at the hand/tool interface synchronized with a video of the tool operative maneuvers. A synthesis of frame-by-frame video analysis and a vector quantization algorithm, allowed to define F/T signatures associated with 14 different types of tool/tissue interactions. The magnitude of F/T applied by NS and ES were significantly different (p < 0.05) and varied based on the task being performed. High F/T magnitudes were applied by NS compared with ES while performing tissue manipulation and vise versa in tasks involved tissue dissection. From each step of the surgical procedures, two MMs were developed representing the performance of three surgeons out of the five in the ES and NS groups. The data obtained by the remaining two surgeons in each group were used for evaluating the performance scale. The final result was a surgical performance index which represented a ratio of statistical similarity between the examined surgeon's MM and the MM of NS and ES. The difference between the performance index value, for a surgeon under study, and the NS/ES boundary, indicated the level of expertise in the surgeon's own group. Using this index, 87.5% of the surgical procedures were correctly classified into the NS and ES groups. The 12.5% of the procedures that were misclassified were performed by the ES and classified as NS. However in these cases the performance index values were very dose to the NS/ES boundary. Preliminary data suggest that a performance index based on MM and F/T signatures provides an objective means of distinguishing NS from ES. In addition, this methodology can be further applied to evaluate haptic virtual reality surgical simulators for improving realism in surgical education. |
| CC : | 002B25N; 002B30A09 |
| FD : | Chirurgie; Chirurgien; Formation professionnelle; Laparoscopie; Evaluation professionnelle; Echelle évaluation; Modèle Markov; Modèle animal; Animal; Cholécystectomie; Porc; Mesure couple; Mesure force; Préhension; Appareillage; Vésicule biliaire; Fundoplicature |
| FG : | Artiodactyla; Ungulata; Mammalia; Vertebrata; Génie biomédical |
| ED : | Surgery; Surgeon; Occupational training; Laparoscopy; Professional evaluation; Evaluation scale; Markov model; Animal model; Animal; Cholecystectomy; Pig; Torque measurement; Force measurement; Gripping; Instrumentation; Gallbladder |
| EG : | Artiodactyla; Ungulata; Mammalia; Vertebrata; Biomedical engineering |
| SD : | Cirugía; Cirujano; Formación profesional; Laparoscopia; Evaluación profesional; Escala evaluación; Modelo Markov; Modelo animal; Animal; Colecistectomía; Cerdo; Medición par; Medición esfuerzo; Prension; Instrumentación; Vesícula biliar |
| LO : | INIST-222E5.354000098847280090 |
| ID : | 01-0299375 |
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Richards</name><affiliation><inist:fA14 i1="03"><s1>Department of Surgery, University of Washington</s1><s2>Seattle, WA 98195</s2><s3>USA</s3><sZ>3 aut.</sZ><sZ>4 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Sinanan, Mika N" sort="Sinanan, Mika N" uniqKey="Sinanan M" first="Mika N." last="Sinanan">Mika N. Sinanan</name><affiliation><inist:fA14 i1="03"><s1>Department of Surgery, University of Washington</s1><s2>Seattle, WA 98195</s2><s3>USA</s3><sZ>3 aut.</sZ><sZ>4 aut.</sZ></inist:fA14></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">INIST</idno><idno type="inist">01-0299375</idno><date when="2001">2001</date><idno type="stanalyst">PASCAL 01-0299375 INIST</idno><idno type="RBID">Pascal:01-0299375</idno><idno type="wicri:Area/PascalFrancis/Corpus">001400</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Markov modeling of minimally invasive surgery based on tool/tissue interaction and force/torque signatures for evaluating surgical skills</title><author><name sortKey="Rosen, Jacob" sort="Rosen, Jacob" uniqKey="Rosen J" first="Jacob" last="Rosen">Jacob Rosen</name><affiliation><inist:fA14 i1="01"><s1>Department of Electrical Engineering, University of Washington</s1><s2>Seattle, WA 98195</s2><s3>USA</s3><sZ>1 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Hannaford, Blake" sort="Hannaford, Blake" uniqKey="Hannaford B" first="Blake" last="Hannaford">Blake Hannaford</name><affiliation><inist:fA14 i1="02"><s1>Department of Electrical Engineering, Box 352500, University of Washington</s1><s2>Seattle, WA 98195</s2><s3>USA</s3><sZ>2 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Richards, Christina G" sort="Richards, Christina G" uniqKey="Richards C" first="Christina G." last="Richards">Christina G. Richards</name><affiliation><inist:fA14 i1="03"><s1>Department of Surgery, University of Washington</s1><s2>Seattle, WA 98195</s2><s3>USA</s3><sZ>3 aut.</sZ><sZ>4 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Sinanan, Mika N" sort="Sinanan, Mika N" uniqKey="Sinanan M" first="Mika N." last="Sinanan">Mika N. Sinanan</name><affiliation><inist:fA14 i1="03"><s1>Department of Surgery, University of Washington</s1><s2>Seattle, WA 98195</s2><s3>USA</s3><sZ>3 aut.</sZ><sZ>4 aut.</sZ></inist:fA14></affiliation></author></analytic><series><title level="j" type="main">IEEE transactions on biomedical engineering</title><title level="j" type="abbreviated">IEEE trans. biomed. eng.</title><idno type="ISSN">0018-9294</idno><imprint><date when="2001">2001</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">IEEE transactions on biomedical engineering</title><title level="j" type="abbreviated">IEEE trans. biomed. eng.</title><idno type="ISSN">0018-9294</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Animal</term><term>Animal model</term><term>Cholecystectomy</term><term>Evaluation scale</term><term>Force measurement</term><term>Gallbladder</term><term>Gripping</term><term>Instrumentation</term><term>Laparoscopy</term><term>Markov model</term><term>Occupational training</term><term>Pig</term><term>Professional evaluation</term><term>Surgeon</term><term>Surgery</term><term>Torque measurement</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Chirurgie</term><term>Chirurgien</term><term>Formation professionnelle</term><term>Laparoscopie</term><term>Evaluation professionnelle</term><term>Echelle évaluation</term><term>Modèle Markov</term><term>Modèle animal</term><term>Animal</term><term>Cholécystectomie</term><term>Porc</term><term>Mesure couple</term><term>Mesure force</term><term>Préhension</term><term>Appareillage</term><term>Vésicule biliaire</term><term>Fundoplicature</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The best method of training for laparoscopic surgical skills is controversial. Some advocate observation in the operating room, while others promote animal and simulated models or a combination of surgery-related tasks. A crucial process in surgical education is to evaluate the level of surgical skills. For laparoscopic surgery, skill evaluation is traditionally performed subjectively by experts grading a video of a procedure performed by a student. By its nature, this process uses fuzzy criteria. The objective of the current study was to develop and assess a skill scale using Markov models (MMs). Ten surgeons [five novice surgeons (NS); five expert surgeons (ES)] performed a cholecystectomy and Nissen fundopli-cation in a porcine model. An instrumented laparoscopic grasper equipped with a three-axis force/torque (F/T) sensor was used to measure the forces/torques at the hand/tool interface synchronized with a video of the tool operative maneuvers. A synthesis of frame-by-frame video analysis and a vector quantization algorithm, allowed to define F/T signatures associated with 14 different types of tool/tissue interactions. The magnitude of F/T applied by NS and ES were significantly different (p < 0.05) and varied based on the task being performed. High F/T magnitudes were applied by NS compared with ES while performing tissue manipulation and vise versa in tasks involved tissue dissection. From each step of the surgical procedures, two MMs were developed representing the performance of three surgeons out of the five in the ES and NS groups. The data obtained by the remaining two surgeons in each group were used for evaluating the performance scale. The final result was a surgical performance index which represented a ratio of statistical similarity between the examined surgeon's MM and the MM of NS and ES. The difference between the performance index value, for a surgeon under study, and the NS/ES boundary, indicated the level of expertise in the surgeon's own group. Using this index, 87.5% of the surgical procedures were correctly classified into the NS and ES groups. The 12.5% of the procedures that were misclassified were performed by the ES and classified as NS. However in these cases the performance index values were very dose to the NS/ES boundary. Preliminary data suggest that a performance index based on MM and F/T signatures provides an objective means of distinguishing NS from ES. In addition, this methodology can be further applied to evaluate haptic virtual reality surgical simulators for improving realism in surgical education.</div></front></TEI><inist><standard h6="B"><pA><fA01 i1="01" i2="1"><s0>0018-9294</s0></fA01><fA02 i1="01"><s0>IEBEAX</s0></fA02><fA03 i2="1"><s0>IEEE trans. biomed. eng.</s0></fA03><fA05><s2>48</s2></fA05><fA06><s2>5</s2></fA06><fA08 i1="01" i2="1" l="ENG"><s1>Markov modeling of minimally invasive surgery based on tool/tissue interaction and force/torque signatures for evaluating surgical skills</s1></fA08><fA11 i1="01" i2="1"><s1>ROSEN (Jacob)</s1></fA11><fA11 i1="02" i2="1"><s1>HANNAFORD (Blake)</s1></fA11><fA11 i1="03" i2="1"><s1>RICHARDS (Christina G.)</s1></fA11><fA11 i1="04" i2="1"><s1>SINANAN (Mika N.)</s1></fA11><fA14 i1="01"><s1>Department of Electrical Engineering, University of Washington</s1><s2>Seattle, WA 98195</s2><s3>USA</s3><sZ>1 aut.</sZ></fA14><fA14 i1="02"><s1>Department of Electrical Engineering, Box 352500, University of Washington</s1><s2>Seattle, WA 98195</s2><s3>USA</s3><sZ>2 aut.</sZ></fA14><fA14 i1="03"><s1>Department of Surgery, University of Washington</s1><s2>Seattle, WA 98195</s2><s3>USA</s3><sZ>3 aut.</sZ><sZ>4 aut.</sZ></fA14><fA20><s1>579-591</s1></fA20><fA21><s1>2001</s1></fA21><fA23 i1="01"><s0>ENG</s0></fA23><fA43 i1="01"><s1>INIST</s1><s2>222E5</s2><s5>354000098847280090</s5></fA43><fA44><s0>0000</s0><s1>© 2001 INIST-CNRS. All rights reserved.</s1></fA44><fA45><s0>48 ref.</s0></fA45><fA47 i1="01" i2="1"><s0>01-0299375</s0></fA47><fA60><s1>P</s1></fA60><fA61><s0>A</s0></fA61><fA64 i1="01" i2="1"><s0>IEEE transactions on biomedical engineering</s0></fA64><fA66 i1="01"><s0>USA</s0></fA66><fC01 i1="01" l="ENG"><s0>The best method of training for laparoscopic surgical skills is controversial. Some advocate observation in the operating room, while others promote animal and simulated models or a combination of surgery-related tasks. A crucial process in surgical education is to evaluate the level of surgical skills. For laparoscopic surgery, skill evaluation is traditionally performed subjectively by experts grading a video of a procedure performed by a student. By its nature, this process uses fuzzy criteria. The objective of the current study was to develop and assess a skill scale using Markov models (MMs). Ten surgeons [five novice surgeons (NS); five expert surgeons (ES)] performed a cholecystectomy and Nissen fundopli-cation in a porcine model. An instrumented laparoscopic grasper equipped with a three-axis force/torque (F/T) sensor was used to measure the forces/torques at the hand/tool interface synchronized with a video of the tool operative maneuvers. A synthesis of frame-by-frame video analysis and a vector quantization algorithm, allowed to define F/T signatures associated with 14 different types of tool/tissue interactions. The magnitude of F/T applied by NS and ES were significantly different (p < 0.05) and varied based on the task being performed. High F/T magnitudes were applied by NS compared with ES while performing tissue manipulation and vise versa in tasks involved tissue dissection. From each step of the surgical procedures, two MMs were developed representing the performance of three surgeons out of the five in the ES and NS groups. The data obtained by the remaining two surgeons in each group were used for evaluating the performance scale. The final result was a surgical performance index which represented a ratio of statistical similarity between the examined surgeon's MM and the MM of NS and ES. The difference between the performance index value, for a surgeon under study, and the NS/ES boundary, indicated the level of expertise in the surgeon's own group. Using this index, 87.5% of the surgical procedures were correctly classified into the NS and ES groups. The 12.5% of the procedures that were misclassified were performed by the ES and classified as NS. However in these cases the performance index values were very dose to the NS/ES boundary. Preliminary data suggest that a performance index based on MM and F/T signatures provides an objective means of distinguishing NS from ES. In addition, this methodology can be further applied to evaluate haptic virtual reality surgical simulators for improving realism in surgical education.</s0></fC01><fC02 i1="01" i2="X"><s0>002B25N</s0></fC02><fC02 i1="02" i2="X"><s0>002B30A09</s0></fC02><fC03 i1="01" i2="X" l="FRE"><s0>Chirurgie</s0><s5>01</s5></fC03><fC03 i1="01" i2="X" l="ENG"><s0>Surgery</s0><s5>01</s5></fC03><fC03 i1="01" i2="X" l="SPA"><s0>Cirugía</s0><s5>01</s5></fC03><fC03 i1="02" i2="X" l="FRE"><s0>Chirurgien</s0><s5>02</s5></fC03><fC03 i1="02" i2="X" l="ENG"><s0>Surgeon</s0><s5>02</s5></fC03><fC03 i1="02" i2="X" l="SPA"><s0>Cirujano</s0><s5>02</s5></fC03><fC03 i1="03" i2="X" l="FRE"><s0>Formation professionnelle</s0><s5>03</s5></fC03><fC03 i1="03" i2="X" l="ENG"><s0>Occupational training</s0><s5>03</s5></fC03><fC03 i1="03" i2="X" l="SPA"><s0>Formación profesional</s0><s5>03</s5></fC03><fC03 i1="04" i2="X" l="FRE"><s0>Laparoscopie</s0><s5>04</s5></fC03><fC03 i1="04" i2="X" l="ENG"><s0>Laparoscopy</s0><s5>04</s5></fC03><fC03 i1="04" i2="X" l="SPA"><s0>Laparoscopia</s0><s5>04</s5></fC03><fC03 i1="05" i2="X" l="FRE"><s0>Evaluation professionnelle</s0><s5>05</s5></fC03><fC03 i1="05" i2="X" l="ENG"><s0>Professional evaluation</s0><s5>05</s5></fC03><fC03 i1="05" i2="X" l="SPA"><s0>Evaluación profesional</s0><s5>05</s5></fC03><fC03 i1="06" i2="X" l="FRE"><s0>Echelle évaluation</s0><s5>06</s5></fC03><fC03 i1="06" i2="X" l="ENG"><s0>Evaluation scale</s0><s5>06</s5></fC03><fC03 i1="06" i2="X" l="SPA"><s0>Escala evaluación</s0><s5>06</s5></fC03><fC03 i1="07" i2="X" l="FRE"><s0>Modèle Markov</s0><s5>07</s5></fC03><fC03 i1="07" i2="X" l="ENG"><s0>Markov model</s0><s5>07</s5></fC03><fC03 i1="07" i2="X" l="SPA"><s0>Modelo Markov</s0><s5>07</s5></fC03><fC03 i1="08" i2="X" l="FRE"><s0>Modèle animal</s0><s5>08</s5></fC03><fC03 i1="08" i2="X" l="ENG"><s0>Animal model</s0><s5>08</s5></fC03><fC03 i1="08" i2="X" l="SPA"><s0>Modelo animal</s0><s5>08</s5></fC03><fC03 i1="09" i2="X" l="FRE"><s0>Animal</s0><s5>09</s5></fC03><fC03 i1="09" i2="X" l="ENG"><s0>Animal</s0><s5>09</s5></fC03><fC03 i1="09" i2="X" l="SPA"><s0>Animal</s0><s5>09</s5></fC03><fC03 i1="10" i2="X" l="FRE"><s0>Cholécystectomie</s0><s5>10</s5></fC03><fC03 i1="10" i2="X" l="ENG"><s0>Cholecystectomy</s0><s5>10</s5></fC03><fC03 i1="10" i2="X" l="SPA"><s0>Colecistectomía</s0><s5>10</s5></fC03><fC03 i1="11" i2="X" l="FRE"><s0>Porc</s0><s5>11</s5></fC03><fC03 i1="11" i2="X" l="ENG"><s0>Pig</s0><s5>11</s5></fC03><fC03 i1="11" i2="X" l="SPA"><s0>Cerdo</s0><s5>11</s5></fC03><fC03 i1="12" i2="X" l="FRE"><s0>Mesure couple</s0><s5>12</s5></fC03><fC03 i1="12" i2="X" l="ENG"><s0>Torque measurement</s0><s5>12</s5></fC03><fC03 i1="12" i2="X" l="SPA"><s0>Medición par</s0><s5>12</s5></fC03><fC03 i1="13" i2="X" l="FRE"><s0>Mesure force</s0><s5>13</s5></fC03><fC03 i1="13" i2="X" l="ENG"><s0>Force measurement</s0><s5>13</s5></fC03><fC03 i1="13" i2="X" l="SPA"><s0>Medición esfuerzo</s0><s5>13</s5></fC03><fC03 i1="14" i2="X" l="FRE"><s0>Préhension</s0><s5>14</s5></fC03><fC03 i1="14" i2="X" l="ENG"><s0>Gripping</s0><s5>14</s5></fC03><fC03 i1="14" i2="X" l="SPA"><s0>Prension</s0><s5>14</s5></fC03><fC03 i1="15" i2="X" l="FRE"><s0>Appareillage</s0><s5>15</s5></fC03><fC03 i1="15" i2="X" l="ENG"><s0>Instrumentation</s0><s5>15</s5></fC03><fC03 i1="15" i2="X" l="SPA"><s0>Instrumentación</s0><s5>15</s5></fC03><fC03 i1="16" i2="X" l="FRE"><s0>Vésicule biliaire</s0><s5>31</s5></fC03><fC03 i1="16" i2="X" l="ENG"><s0>Gallbladder</s0><s5>31</s5></fC03><fC03 i1="16" i2="X" l="SPA"><s0>Vesícula biliar</s0><s5>31</s5></fC03><fC03 i1="17" i2="X" l="FRE"><s0>Fundoplicature</s0><s4>INC</s4><s5>86</s5></fC03><fC07 i1="01" i2="X" l="FRE"><s0>Artiodactyla</s0><s2>NS</s2></fC07><fC07 i1="01" i2="X" l="ENG"><s0>Artiodactyla</s0><s2>NS</s2></fC07><fC07 i1="01" i2="X" l="SPA"><s0>Artiodactyla</s0><s2>NS</s2></fC07><fC07 i1="02" i2="X" l="FRE"><s0>Ungulata</s0><s2>NS</s2></fC07><fC07 i1="02" i2="X" l="ENG"><s0>Ungulata</s0><s2>NS</s2></fC07><fC07 i1="02" i2="X" l="SPA"><s0>Ungulata</s0><s2>NS</s2></fC07><fC07 i1="03" i2="X" l="FRE"><s0>Mammalia</s0><s2>NS</s2></fC07><fC07 i1="03" i2="X" l="ENG"><s0>Mammalia</s0><s2>NS</s2></fC07><fC07 i1="03" i2="X" l="SPA"><s0>Mammalia</s0><s2>NS</s2></fC07><fC07 i1="04" i2="X" l="FRE"><s0>Vertebrata</s0><s2>NS</s2></fC07><fC07 i1="04" i2="X" l="ENG"><s0>Vertebrata</s0><s2>NS</s2></fC07><fC07 i1="04" i2="X" l="SPA"><s0>Vertebrata</s0><s2>NS</s2></fC07><fC07 i1="05" i2="X" l="FRE"><s0>Génie biomédical</s0><s5>37</s5></fC07><fC07 i1="05" i2="X" l="ENG"><s0>Biomedical engineering</s0><s5>37</s5></fC07><fC07 i1="05" i2="X" l="SPA"><s0>Ingeniería biomédica</s0><s5>37</s5></fC07><fN21><s1>204</s1></fN21></pA></standard><server><NO>PASCAL 01-0299375 INIST</NO><ET>Markov modeling of minimally invasive surgery based on tool/tissue interaction and force/torque signatures for evaluating surgical skills</ET><AU>ROSEN (Jacob); HANNAFORD (Blake); RICHARDS (Christina G.); SINANAN (Mika N.)</AU><AF>Department of Electrical Engineering, University of Washington/Seattle, WA 98195/Etats-Unis (1 aut.); Department of Electrical Engineering, Box 352500, University of Washington/Seattle, WA 98195/Etats-Unis (2 aut.); Department of Surgery, University of Washington/Seattle, WA 98195/Etats-Unis (3 aut., 4 aut.)</AF><DT>Publication en série; Niveau analytique</DT><SO>IEEE transactions on biomedical engineering; ISSN 0018-9294; Coden IEBEAX; Etats-Unis; Da. 2001; Vol. 48; No. 5; Pp. 579-591; Bibl. 48 ref.</SO><LA>Anglais</LA><EA>The best method of training for laparoscopic surgical skills is controversial. Some advocate observation in the operating room, while others promote animal and simulated models or a combination of surgery-related tasks. A crucial process in surgical education is to evaluate the level of surgical skills. For laparoscopic surgery, skill evaluation is traditionally performed subjectively by experts grading a video of a procedure performed by a student. By its nature, this process uses fuzzy criteria. The objective of the current study was to develop and assess a skill scale using Markov models (MMs). Ten surgeons [five novice surgeons (NS); five expert surgeons (ES)] performed a cholecystectomy and Nissen fundopli-cation in a porcine model. An instrumented laparoscopic grasper equipped with a three-axis force/torque (F/T) sensor was used to measure the forces/torques at the hand/tool interface synchronized with a video of the tool operative maneuvers. A synthesis of frame-by-frame video analysis and a vector quantization algorithm, allowed to define F/T signatures associated with 14 different types of tool/tissue interactions. The magnitude of F/T applied by NS and ES were significantly different (p < 0.05) and varied based on the task being performed. High F/T magnitudes were applied by NS compared with ES while performing tissue manipulation and vise versa in tasks involved tissue dissection. From each step of the surgical procedures, two MMs were developed representing the performance of three surgeons out of the five in the ES and NS groups. The data obtained by the remaining two surgeons in each group were used for evaluating the performance scale. The final result was a surgical performance index which represented a ratio of statistical similarity between the examined surgeon's MM and the MM of NS and ES. The difference between the performance index value, for a surgeon under study, and the NS/ES boundary, indicated the level of expertise in the surgeon's own group. Using this index, 87.5% of the surgical procedures were correctly classified into the NS and ES groups. The 12.5% of the procedures that were misclassified were performed by the ES and classified as NS. However in these cases the performance index values were very dose to the NS/ES boundary. Preliminary data suggest that a performance index based on MM and F/T signatures provides an objective means of distinguishing NS from ES. In addition, this methodology can be further applied to evaluate haptic virtual reality surgical simulators for improving realism in surgical education.</EA><CC>002B25N; 002B30A09</CC><FD>Chirurgie; Chirurgien; Formation professionnelle; Laparoscopie; Evaluation professionnelle; Echelle évaluation; Modèle Markov; Modèle animal; Animal; Cholécystectomie; Porc; Mesure couple; Mesure force; Préhension; Appareillage; Vésicule biliaire; Fundoplicature</FD><FG>Artiodactyla; Ungulata; Mammalia; Vertebrata; Génie biomédical</FG><ED>Surgery; Surgeon; Occupational training; Laparoscopy; Professional evaluation; Evaluation scale; Markov model; Animal model; Animal; Cholecystectomy; Pig; Torque measurement; Force measurement; Gripping; Instrumentation; Gallbladder</ED><EG>Artiodactyla; Ungulata; Mammalia; Vertebrata; Biomedical engineering</EG><SD>Cirugía; Cirujano; Formación profesional; Laparoscopia; Evaluación profesional; Escala evaluación; Modelo Markov; Modelo animal; Animal; Colecistectomía; Cerdo; Medición par; Medición esfuerzo; Prension; Instrumentación; Vesícula biliar</SD><LO>INIST-222E5.354000098847280090</LO><ID>01-0299375</ID></server></inist></record>Pour manipuler ce document sous Unix (Dilib)
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