Can Microinstrument Motion Metrics of Distance, Speed, and Acceleration Indicate Surgical Task Complexity? An AI-Driven Study...International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH) (Virtual), December 13-15, 2024.
Objectifying the quality of microsurgical technique is both crucial and challenging. The aim of this study was to evaluate whether microinstrument motion metricscan reflect the complexity of microsurgical tasks. The laboratory experiment involved 13 right-handed neurosurgeons tasked with using micro...
| Publicado en: | Studies in Health Technology & Informatics Vol. 323; pp. 111 - 116 |
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| Autores principales: | , , , , , , , , , |
| Formato: | equations & formulas pictorial proceedings research Journal Article |
| Publicado: |
Sage Publications Inc.
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184463107&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184463107 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2025 vid: 323 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 184463107 184463107 184463107 10.3233/SHTI250059 184463107 ppf: 111 ppct: 5 formats: tig: atl: Can Microinstrument Motion Metrics of Distance, Speed, and Acceleration Indicate Surgical Task Complexity? An AI-Driven Study...International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH) (Virtual), December 13-15, 2024. aug: au: DANILOV, Gleb KOSTYUMOV, Vasiliy PILIPENKO, Oleg TRUBETSKOY, Sergey NUTFULLIN, Bulat TITOV, Oleg ILYUSHIN, Eugeniy PITSKHELAURI, David PANTELEEV, Andrey BYKANOV, Andrey affil: Laboratory of Biomedical Informatics and Artificial Intelligence, Lomonosov Moscow State University, Moscow, Russian Federation sug: subj: Microsurgery Equipment and Supplies Microsurgery Methods Task Performance and Analysis Congresses and Conferences Human Benchmarking Robotic Surgical Procedures Artificial Intelligence Surgery, Operative Education ab: Objectifying the quality of microsurgical technique is both crucial and challenging. The aim of this study was to evaluate whether microinstrument motion metricscan reflect the complexity of microsurgical tasks. The laboratory experiment involved 13 right-handed neurosurgeons tasked with using microsurgical scissors to cut a white thread at a spot marked by a purple dot under the microscope. Each participant completed the task under four consecutive conditions: with or without wrist stabilization on a support, both before and after muscle load. Using the promptable transformer model, we segmented microsurgical instruments from video recordings and extracted their skeletons and centers of mass. From the time series of the center of mass X and Y coordinates, we derived seven additional time series for velocity, acceleration, and the jerk along the X and Y axes, as well as the smoothness metric. We generated thirty-three statistical features for each time series using the feasts R package. These motion features were then compared pairwise across various tasks. Of the 1782 tests conducted, 164 (or 9.2%) revealed statistically significant differences in 66 motion features. Our results provide a proof-of-concept, showing that AI-derived microsurgical motion features can reflect the complexity of conditions encountered by the microsurgeon during surgery. pubtype: Academic Journal doctype: equations & formulas pictorial proceedings research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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