Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA.
Eur J Cardiothorac Surg. 2022 Jan 24;61(2):239-248. doi: 10.1093/ejcts/ezab422.
Machine learning (ML) has great potential, but there are few examples of its implementation improving outcomes. The thoracic surgeon must be aware of pertinent ML literature and how to evaluate this field for the safe translation to patient care. This scoping review provides an introduction to ML applications specific to the thoracic surgeon. We review current applications, limitations and future directions.
A search of the PubMed database was conducted with inclusion requirements being the use of an ML algorithm to analyse patient information relevant to a thoracic surgeon and contain sufficient details on the data used, ML methods and results. Twenty-two papers met the criteria and were reviewed using a methodological quality rubric.
ML demonstrated enhanced preoperative test accuracy, earlier pathological diagnosis, therapies to maximize survival and predictions of adverse events and survival after surgery. However, only 4 performed external validation. One demonstrated improved patient outcomes, nearly all failed to perform model calibration and one addressed fairness and bias with most not generalizable to different populations. There was a considerable variation to allow for reproducibility.
There is promise but also challenges for ML in thoracic surgery. The transparency of data and algorithm design and the systemic bias on which models are dependent remain issues to be addressed. Although there has yet to be widespread use in thoracic surgery, it is essential thoracic surgeons be at the forefront of the eventual safe introduction of ML to the clinic and operating room.
机器学习（ML）具有巨大的潜力，但将其应用于改善结果的例子却很少。胸外科医生必须了解相关的 ML 文献，以及如何评估该领域，以确保安全地将其应用于患者护理。本范围综述专门介绍了与胸外科医生相关的 ML 应用。我们回顾了当前的应用、局限性和未来方向。
对 PubMed 数据库进行了搜索，纳入标准为使用 ML 算法分析与胸外科医生相关的患者信息，并包含足够详细的数据使用、ML 方法和结果信息。有 22 篇论文符合标准，并使用方法学质量评估表进行了审查。
ML 提高了术前检测的准确性、更早的病理诊断、最大化生存的治疗方法以及对术后不良事件和生存的预测。然而，只有 4 项进行了外部验证。其中一项证明了改善了患者的预后，几乎所有的模型都未能进行校准，其中一项涉及公平性和偏见问题，而大多数模型不能推广到不同的人群。存在很大的差异，难以进行重现。
ML 在胸外科领域有一定的应用前景，但也存在挑战。数据和算法设计的透明度以及模型所依赖的系统偏差仍然是需要解决的问题。尽管 ML 在胸外科的应用尚未广泛，但胸外科医生必须站在最终安全地将 ML 引入临床和手术室的前沿。
