Detection of hand motion during cadaveric mastoidectomy dissections: a technical note.

IF 1.6 4区 医学 Q2 SURGERY
Frontiers in Surgery Pub Date : 2024-10-03 eCollection Date: 2024-01-01 DOI:10.3389/fsurg.2024.1441346
Thomas J On, Yuan Xu, Nicolas I Gonzalez-Romo, Gerardo Gomez-Castro, Oscar Alcantar-Garibay, Marco Santello, Michael T Lawton, Mark C Preul
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引用次数: 0

Abstract

Background: Surgical approaches that access the posterior temporal bone require careful drilling motions to achieve adequate exposure while avoiding injury to critical structures.

Objective: We assessed a deep learning hand motion detector to potentially refine hand motion and precision during power drill use in a cadaveric mastoidectomy procedure.

Methods: A deep-learning hand motion detector tracked the movement of a surgeon's hands during three cadaveric mastoidectomy procedures. The model provided horizontal and vertical coordinates of 21 landmarks on both hands, which were used to create vertical and horizontal plane tracking plots. Preliminary surgical performance metrics were calculated from the motion detections.

Results: 1,948,837 landmark detections were collected, with an overall 85.9% performance. There was similar detection of the dominant hand (48.2%) compared to the non-dominant hand (51.7%). A loss of tracking occurred due to the increased brightness caused by the microscope light at the center of the field and by movements of the hand outside the field of view of the camera. The mean (SD) time spent (seconds) during instrument changes was 21.5 (12.4) and 4.4 (5.7) during adjustments of the microscope.

Conclusion: A deep-learning hand motion detector can measure surgical motion without physical sensors attached to the hands during mastoidectomy simulations on cadavers. While preliminary metrics were developed to assess hand motion during mastoidectomy, further studies are needed to expand and validate these metrics for potential use in guiding and evaluating surgical training.

尸体乳突切除术解剖过程中的手部运动检测:技术说明。
背景:进入颞骨后部的手术方法需要谨慎的钻孔动作,以实现充分暴露,同时避免损伤关键结构:我们对深度学习手部运动检测器进行了评估,以改进在尸体乳突切除术中使用电钻时的手部运动和精确度:深度学习手部运动检测器跟踪了外科医生在三次乳突切除术中的手部运动。该模型提供了双手 21 个地标的水平和垂直坐标,用于绘制垂直和水平平面追踪图。根据运动检测结果计算出初步的手术性能指标:共收集到 1,948,837 次地标检测,总体性能达到 85.9%。优势手(48.2%)与非优势手(51.7%)的检测结果相似。由于视野中心的显微镜光线导致亮度增加,以及手在摄像机视野外的移动,出现了跟踪损失。仪器更换时的平均(标清)耗时(秒)为 21.5(12.4),显微镜调整时的平均(标清)耗时(秒)为 4.4(5.7):结论:在尸体上模拟乳突切除术时,深度学习手部运动检测器可以测量手术运动,而无需在手上安装物理传感器。虽然已开发出初步指标来评估乳突切除术中的手部运动,但还需要进一步研究来扩展和验证这些指标,以便用于指导和评估手术培训。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Frontiers in Surgery
Frontiers in Surgery Medicine-Surgery
CiteScore
1.90
自引率
11.10%
发文量
1872
审稿时长
12 weeks
期刊介绍: Evidence of surgical interventions go back to prehistoric times. Since then, the field of surgery has developed into a complex array of specialties and procedures, particularly with the advent of microsurgery, lasers and minimally invasive techniques. The advanced skills now required from surgeons has led to ever increasing specialization, though these still share important fundamental principles. Frontiers in Surgery is the umbrella journal representing the publication interests of all surgical specialties. It is divided into several “Specialty Sections” listed below. All these sections have their own Specialty Chief Editor, Editorial Board and homepage, but all articles carry the citation Frontiers in Surgery. Frontiers in Surgery calls upon medical professionals and scientists from all surgical specialties to publish their experimental and clinical studies in this journal. By assembling all surgical specialties, which nonetheless retain their independence, under the common umbrella of Frontiers in Surgery, a powerful publication venue is created. Since there is often overlap and common ground between the different surgical specialties, assembly of all surgical disciplines into a single journal will foster a collaborative dialogue amongst the surgical community. This means that publications, which are also of interest to other surgical specialties, will reach a wider audience and have greater impact. The aim of this multidisciplinary journal is to create a discussion and knowledge platform of advances and research findings in surgical practice today to continuously improve clinical management of patients and foster innovation in this field.
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