OnetoMAP Data Analytics and Machine Learning, Department of General Surgery, University of South Florida Morsani College of Medicine, Tampa, FL.
OnetoMAP Data Analytics and Machine Learning, Department of General Surgery, University of South Florida Morsani College of Medicine, Tampa, FL.
Surgery. 2021 May;169(5):1250-1252. doi: 10.1016/j.surg.2020.09.040. Epub 2020 Dec 3.
Surgical training has undergone substantial change in the last few decades. As technology and patient complexity continues to increase, demands for novel approaches to ensure competency have arisen. Virtual reality systems augmented with machine learning represents one such approach. The ability to offer on-demand training, integrate checklists, and provide personalized, surgeon-specific feedback is paving the way to a new era of surgical training. Machine learning algorithms that improve over time as they acquire more data will continue to refine the education they provide. Further, fully immersive simulated environments coupled with machine learning analytics provide real-world training opportunities in a safe atmosphere away from the potential to harm patients. Careful implementation of these technologies has the potential to increase access and improve quality of surgical training and patient care and are poised to change the landscape of current surgical training. Herein, we describe the current state of virtual reality coupled with machine learning for surgical training, future directions, and existing limitations of this technology.
在过去的几十年里，外科培训经历了重大变革。随着技术和患者复杂性的不断增加，需要采用新的方法来确保胜任能力。虚拟现实系统与机器学习的结合代表了这样一种方法。提供按需培训、整合检查表以及提供个性化、特定于外科医生的反馈的能力正在开辟外科培训的新时代。随着时间的推移，随着机器学习算法获取更多数据，它们将继续改进所提供的教育。此外，完全沉浸式模拟环境与机器学习分析相结合，为外科医生提供了在安全的环境中进行实际操作的机会，避免了对患者造成伤害的可能性。这些技术的谨慎实施有可能增加外科培训和患者护理的机会，并提高其质量，同时有望改变当前外科培训的格局。在此，我们描述了虚拟现实与机器学习相结合用于外科培训的现状、未来方向以及该技术的现有局限性。
