Development of a 3D Motion Tracking System for the Analysis of Skills in Microsurgery

Franco-González, Iván Tlacaélel; Pérez-Escamirosa, Fernando; Minor-Martínez, Arturo; Rosas-Barrientos, José Vicente; Hernández-Paredes, Tania Josefina

doi:10.1007/s10916-021-01787-8

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…6 In recent years, motion capture and tracking systems have been designed and introduced for the recording and analysis of motion paths and subsequent evaluation of microsurgical skills. 7  Implementation of an easily accessible training modality. Because laboratory microscopes are cumbersome and often inaccessible, This article is protected by copyright.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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Innovative Clinical Scenario Simulator for Step-by-Step Microsurgical Training

Cui,

Han,

Liu

et al. 2024

J Reconstr Microsurg

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Background Microsurgical training should be implemented with consideration of operative difficulties that occur in actual clinical situations. We evaluated the effectiveness of a novel clinical scenario simulator for step-by-step microsurgical training that progressed from conventional training to escalated training with additional obstacles. Methods A training device was designed according to multiple and intricate clinical microsurgery scenarios. Twenty surgical residents with no experience in microsurgery were randomly assigned to either the control group (conventional training curricula, n = 10) or the experimental group (step-by-step training courses, n = 10). After 4 weeks of laboratory practice, the participants were scheduled to perform their first microvascular anastomoses on patients in an operating room. The Global Rating Scale (GRS) scores and operative duration were used to compare microsurgical skills between the two groups. Results There were no significant differences in the participants’ baseline characteristics before microsurgical training between the groups with respect to age, sex, postgraduate year, surgical specialty, or mean GRS score (p < 0.05). There were also no significant differences in recipient sites between the two groups (p = 0.735). After training, the GRS scores in both groups were significantly improved (p = 0.000). However, in the actual microsurgical situations, the GRS scores were significantly higher in the experimental than control group (p < 0.05). There was no significant difference in the operative duration between the two groups (p < 0.13). Conclusion Compared with a traditional training program, this step-by-step microsurgical curriculum based on our clinical scenario simulator results in significant improvement in acquisition of microsurgical skills.

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Section: Accepted Manuscriptmentioning

confidence: 99%

mentioning

confidence: 99%

Innovative Clinical Scenario Simulator for Step-by-Step Microsurgical Training

Cui,

Han,

Liu

et al. 2024

J Reconstr Microsurg

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“…Franco-González et al [ 25 ] designed a stereoscopic system with two cameras that recorded images from different angles of surgical tweezers. The 3D motion tracking software was created using the C++ programming language and the OpenCV 3.4.11 library.…”

Section: Automatic Assessment Of the Level Of Microsurgical Skillsmentioning

confidence: 99%

Artificial Intelligence Technologies in the Microsurgical Operating Room (Review)

Bykanov¹,

Danilov²,

Kostumov³

et al. 2023

Sovrem Tehnol Med

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Surgery performed by a novice neurosurgeon under constant supervision of a senior surgeon with the experience of thousands of operations, able to handle any intraoperative complications and predict them in advance, and never getting tired, is currently an elusive dream, but can become a reality with the development of artificial intelligence methods.This paper has presented a review of the literature on the use of artificial intelligence technologies in the microsurgical operating room. Searching for sources was carried out in the PubMed text database of medical and biological publications. The key words used were "surgical procedures", "dexterity", "microsurgery" AND "artificial intelligence" OR "machine learning" OR "neural networks". Articles in English and Russian were considered with no limitation to publication date. The main directions of research on the use of artificial intelligence technologies in the microsurgical operating room have been highlighted.Despite the fact that in recent years machine learning has been increasingly introduced into the medical field, a small number of studies related to the problem of interest have been published, and their results have not proved to be of practical use yet. However, the social significance of this direction is an important argument for its development.

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“…While step recognition algorithms are entirely based on automated feature extraction from whole pictures or selected image portions, the extracted features generally pose interpretation problems. Only the incorporation of more contextualized tasks (e.g., object delineation 15 and motion tracking 16 ) will facilitate proper assessment of the intrinsic discriminative ability of an algorithmic system, with the ultimate goals of improving its accuracy and addressing potential pitfalls. By comparing the instrument-handling abilities of trainees versus those of experienced surgeons, information from object delineation and motion tracking will also be paramount in the field of surgical training.…”

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confidence: 99%