基于人工智能的微血管吻合一致性评估深度学习模型。

IF 3.6 2区 医学 Q1 CLINICAL NEUROLOGY
Jiuxu Chen, Thomas J On, Yuan Xu, Jonathan A Tangsrivimol, Kivanc Yangi, Rokuya Tanikawa, Michael T Lawton, Marco Santello, Baoxin Li, Mark C Preul
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引用次数: 0

摘要

目的:评价微吻合术的一致性和准确性是神经外科训练的关键。传统的方法依赖于专家观察,这可能是主观的和耗时的。本研究的目的是开发和验证使用长短期记忆(LSTM)架构的深度学习模型,通过预测和比较缝合执行来客观评估微吻合性能。方法:建立基于lstm的神经网络,对微血管吻合模拟中的手部运动进行建模和预测。2位神经外科专家进行显微吻合2次,间隔1年(第1期和第2期)。外科医生1进行中断缝合,外科医生2进行连续缝合。此外,一名具有最小显微外科经验的受训者进行了一次中断缝合手术。利用Kullback-Leibler (KL)散度比较预测和实际缝合执行情况,定量评估模型性能。并对其经济性和运动性进行了分析。结果:基于lstm的模型能准确预测缝合动作。外科医生1的KL散度值为0.00063(第1节)和0.00061(第2节),外科医生2的值为0.00082(第1节)和0.00016(第2节)。受训者表现出较高的KL差异(0.00196),反映出较不一致的表现。评估了运动经济性,显示外科医生1的平均欧氏距离为7.41 mm(第1阶段)和5.85 mm(第2阶段),外科医生2的平均欧氏距离为10.53 mm(第1阶段)和14.46 mm(第2阶段),受训者为10.50 mm。运动流分析显示,外科医生1的缝线间隔中位数为31.96秒(第1次)和29.57秒(第2次),外科医生2的缝线间隔中位数为21.53秒(第1次)和21.50秒(第2次),受术者的缝线间隔中位数为101.23秒。结论:基于lstm的模型客观地评估了微吻合性能,捕获了一致性和效率。进一步验证了运动指标的经济性和流通性。未来的研究将扩展模型的应用到更多的外科医生,并完善对性能指标的解释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial intelligence-based deep learning model for evaluating procedural consistency in microvascular anastomosis.

Objective: Assessing the consistency and precision of microanastomosis performance is crucial in neurosurgical training. Traditional methods rely on expert observation, which can be subjective and time-consuming. The aim of this study was to develop and validate a deep learning model using long short-term memory (LSTM) architecture for objective evaluation of microanastomosis performance by predicting and comparing suturing executions.

Methods: An LSTM-based neural network was developed to model and predict hand movements during microvascular anastomosis simulation. Video data were collected from 2 expert neurosurgeons performing microanastomosis twice, 1 year apart (sessions 1 and 2). Surgeon 1 performed interrupted suturing, and surgeon 2 performed continuous suturing. Additionally, a trainee with minimal microsurgical experience performed the interrupted suturing procedure once. Model performance was quantitatively assessed by comparing predicted and actual suturing executions using Kullback-Leibler (KL) divergence. Economy and flow of motion were also analyzed.

Results: The LSTM-based model accurately predicted suturing movements. Surgeon 1 demonstrated KL divergence values of 0.00063 (session 1) and 0.00061 (session 2), and surgeon 2 had values of 0.00082 (session 1) and 0.00016 (session 2). The trainee exhibited higher KL divergence (0.00196), reflecting less consistent performance. The economy of motion was assessed, showing mean Euclidean distances of 7.41 mm (session 1) and 5.85 mm (session 2) for surgeon 1, 10.53 mm (session 1) and 14.46 mm (session 2) for surgeon 2, and 10.50 mm for the trainee. The flow of motion analysis indicated median time intervals between sutures of 31.96 seconds (session 1) and 29.57 seconds (session 2) for surgeon 1, 21.53 seconds (session 1) and 21.50 seconds (session 2) for surgeon 2, and 101.23 seconds for the trainee.

Conclusions: The LSTM-based model objectively assessed microanastomosis performance, capturing consistency and efficiency. Economy and flow of motion metrics were further validated. Future studies will extend the model's application to more surgeons and refine interpretation of the performance metrics.

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来源期刊
Journal of neurosurgery
Journal of neurosurgery 医学-临床神经学
CiteScore
7.20
自引率
7.30%
发文量
1003
审稿时长
1 months
期刊介绍: The Journal of Neurosurgery, Journal of Neurosurgery: Spine, Journal of Neurosurgery: Pediatrics, and Neurosurgical Focus are devoted to the publication of original works relating primarily to neurosurgery, including studies in clinical neurophysiology, organic neurology, ophthalmology, radiology, pathology, and molecular biology. The Editors and Editorial Boards encourage submission of clinical and laboratory studies. Other manuscripts accepted for review include technical notes on instruments or equipment that are innovative or useful to clinicians and researchers in the field of neuroscience; papers describing unusual cases; manuscripts on historical persons or events related to neurosurgery; and in Neurosurgical Focus, occasional reviews. Letters to the Editor commenting on articles recently published in the Journal of Neurosurgery, Journal of Neurosurgery: Spine, and Journal of Neurosurgery: Pediatrics are welcome.
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