UCD Centre for Precision Surgery, University College Dublin, Dublin D07 Y9AW, Ireland.
World J Gastroenterol. 2021 Nov 14;27(42):7240-7246. doi: 10.3748/wjg.v27.i42.7240.
Advances in machine learning, computer vision and artificial intelligence methods, in combination with those in processing and cloud computing capability, portend the advent of true decision support during interventions in real-time and soon perhaps in automated surgical steps. Such capability, deployed alongside technology intraoperatively, is termed digital surgery and can be delivered without the need for high-end capital robotic investment. An area close to clinical usefulness right now harnesses advances in near infrared endolaparoscopy and fluorescence guidance for tissue characterisation through the use of biophysics-inspired algorithms. This represents a potential synergistic methodology for the deep learning methods currently advancing in ophthalmology, radiology, and recently gastroenterology colonoscopy. As databanks of more general surgical videos are created, greater analytic insights can be derived across the operative spectrum of gastroenterological disease and operations (including instrumentation and operative step sequencing and recognition, followed over time by surgeon and instrument performance assessment) and linked to value-based outcomes. However, issues of legality, ethics and even morality need consideration, as do the limiting effects of monopolies, cartels and isolated data silos. Furthermore, the role of the surgeon, surgical societies and healthcare institutions in this evolving field needs active deliberation, as the default risks relegation to bystander or passive recipient. This editorial provides insight into this accelerating field by illuminating the near-future and next decade evolutionary steps towards widespread clinical integration for patient and societal benefit.
机器学习、计算机视觉和人工智能方法的进步，结合处理和云计算能力的进步，预示着在实时干预过程中即将出现真正的决策支持，也许很快就会出现自动化手术步骤。这种能力与术中技术一起部署，被称为数字手术，不需要高端资本机器人投资即可实现。目前接近临床应用的一个领域利用近红外内镜和荧光引导技术，通过使用受生物物理启发的算法来对组织特征进行分类。这代表了一种潜在的协同方法，可将目前在眼科、放射学以及最近的胃肠病学结肠镜检查中不断发展的深度学习方法应用于其中。随着更广泛的外科手术视频数据库的创建，可以在胃肠病学疾病和手术的整个手术范围内（包括仪器和手术步骤的排序和识别，以及随着时间的推移对医生和仪器性能的评估）得出更深入的分析见解，并与基于价值的结果相关联。但是，需要考虑合法性、道德甚至道德问题，以及垄断、卡特尔和孤立的数据孤岛的限制影响。此外，外科医生、外科协会和医疗机构在这一不断发展的领域中的作用需要进行积极的审议，因为默认风险是将其降级为旁观者或被动接受者。本社论通过阐明未来近十年向广泛的临床整合发展的步骤，为患者和社会带来益处，为这一加速发展的领域提供了深入的见解。
