EndoCAS, Center for Computer Assisted Surgery, University of Pisa, 56124, Pisa, Italy 1st Propaedeutic Surgical Unit, Hippocrateion Athens General Hospital, Athens Medical School, National and Kapodistrian University of Athens, Greece MPLSC, Athens Medical School, National and Kapodistrian University of Athens, Greece Department of Surgery, University of Washington Medical Center, Seattle, WA, United States Scuola Superiore Sant'Anna of Pisa, 56214, Pisa, Italy Institute for Medical Science and Technology, University of Dundee, Dundee, DD2 1FD, United Kingdom.
Int J Surg. 2021 Nov;95:106151. doi: 10.1016/j.ijsu.2021.106151. Epub 2021 Oct 22.
Despite the extensive published literature on the significant potential of artificial intelligence (AI) there are no reports on its efficacy in improving patient safety in robot-assisted surgery (RAS). The purposes of this work are to systematically review the published literature on AI in RAS, and to identify and discuss current limitations and challenges.
A literature search was conducted on PubMed, Web of Science, Scopus, and IEEExplore according to PRISMA 2020 statement. Eligible articles were peer-review studies published in English language from January 1, 2016 to December 31, 2020. Amstar 2 was used for quality assessment. Risk of bias was evaluated with the Newcastle Ottawa Quality assessment tool. Data of the studies were visually presented in tables using SPIDER tool.
Thirty-five publications, representing 3436 patients, met the search criteria and were included in the analysis. The selected reports concern: motion analysis (n = 17), urology (n = 12), gynecology (n = 1), other specialties (n = 1), training (n = 3), and tissue retraction (n = 1). Precision for surgical tools detection varied from 76.0% to 90.6%. Mean absolute error on prediction of urinary continence after robot-assisted radical prostatectomy (RARP) ranged from 85.9 to 134.7 days. Accuracy on prediction of length of stay after RARP was 88.5%. Accuracy on recognition of the next surgical task during robot-assisted partial nephrectomy (RAPN) achieved 75.7%.
The reviewed studies were of low quality. The findings are limited by the small size of the datasets. Comparison between studies on the same topic was restricted due to algorithms and datasets heterogeneity. There is no proof that currently AI can identify the critical tasks of RAS operations, which determine patient outcome. There is an urgent need for studies on large datasets and external validation of the AI algorithms used. Furthermore, the results should be transparent and meaningful to surgeons, enabling them to inform patients in layman's words.
Review Registry Unique Identifying Number: reviewregistry1225.
尽管人工智能（AI）具有广泛的文献记载，其具有极大的潜力，但目前尚无报告表明其在提高机器人辅助手术（RAS）中的患者安全性方面的功效。本研究的目的是系统地回顾 RAS 中人工智能的文献，并确定和讨论当前的局限性和挑战。
根据 PRISMA 2020 声明，在 PubMed、Web of Science、Scopus 和 IEEExplore 上进行了文献检索。纳入的文章为 2016 年 1 月 1 日至 2020 年 12 月 31 日期间以英文发表的同行评议研究。使用 Amstar 2 进行质量评估。采用纽卡斯尔-渥太华质量评估工具评估偏倚风险。使用 SPIDER 工具将研究数据以表格形式直观呈现。
符合检索标准并纳入分析的有 35 篇文献，共 3436 例患者。所选报告涉及：运动分析（n=17）、泌尿科（n=12）、妇科（n=1）、其他专业（n=1）、培训（n=3）和组织回缩（n=1）。手术工具检测的精度范围为 76.0%至 90.6%。机器人辅助根治性前列腺切除术（RARP）后尿控预测的平均绝对误差范围为 85.9 至 134.7 天。RARP 后住院时间预测的准确率为 88.5%。在机器人辅助部分肾切除术（RAPN）中识别下一个手术任务的准确率达到 75.7%。
所审查的研究质量较低。由于数据集的规模较小，研究结果受到限制。由于算法和数据集的异质性，对同一主题的研究之间的比较受到限制。目前没有证据表明人工智能可以识别决定患者预后的 RAS 手术的关键任务。迫切需要进行大型数据集研究和对使用的人工智能算法进行外部验证。此外，结果应该对外科医生透明且有意义，使他们能够用通俗易懂的语言告知患者。
审查注册表唯一识别号：reviewregistry1225。
