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Review
. 2023 Feb 20;12(4):1687.
doi: 10.3390/jcm12041687.

Automated Capture of Intraoperative Adverse Events Using Artificial Intelligence: A Systematic Review and Meta-Analysis

Michael B Eppler  1 , Aref S Sayegh  1 , Marissa Maas  1 , Abhishek Venkat  1 , Sij Hemal  1 , Mihir M Desai  1 , Andrew J Hung  1 , Teodor Grantcharov  2 , Giovanni E Cacciamani  1 , Mitchell G Goldenberg  1
Affiliations
Review

Automated Capture of Intraoperative Adverse Events Using Artificial Intelligence: A Systematic Review and Meta-Analysis

Michael B Eppler et al. J Clin Med. .

Abstract

Intraoperative adverse events (iAEs) impact the outcomes of surgery, and yet are not routinely collected, graded, and reported. Advancements in artificial intelligence (AI) have the potential to power real-time, automatic detection of these events and disrupt the landscape of surgical safety through the prediction and mitigation of iAEs. We sought to understand the current implementation of AI in this space. A literature review was performed to PRISMA-DTA standards. Included articles were from all surgical specialties and reported the automatic identification of iAEs in real-time. Details on surgical specialty, adverse events, technology used for detecting iAEs, AI algorithm/validation, and reference standards/conventional parameters were extracted. A meta-analysis of algorithms with available data was conducted using a hierarchical summary receiver operating characteristic curve (ROC). The QUADAS-2 tool was used to assess the article risk of bias and clinical applicability. A total of 2982 studies were identified by searching PubMed, Scopus, Web of Science, and IEEE Xplore, with 13 articles included for data extraction. The AI algorithms detected bleeding (n = 7), vessel injury (n = 1), perfusion deficiencies (n = 1), thermal damage (n = 1), and EMG abnormalities (n = 1), among other iAEs. Nine of the thirteen articles described at least one validation method for the detection system; five explained using cross-validation and seven divided the dataset into training and validation cohorts. Meta-analysis showed the algorithms were both sensitive and specific across included iAEs (detection OR 14.74, CI 4.7-46.2). There was heterogeneity in reported outcome statistics and article bias risk. There is a need for standardization of iAE definitions, detection, and reporting to enhance surgical care for all patients. The heterogeneous applications of AI in the literature highlights the pluripotent nature of this technology. Applications of these algorithms across a breadth of urologic procedures should be investigated to assess the generalizability of these data.

Keywords: PRISMA; adverse effects; artificial intelligence; intraoperative adverse events; intraoperative period; systematic review.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flow diagram: study identification and screening process.
Figure 2
Figure 2
Intraoperative adverse event type identified by artificial intelligence (AI).
Figure 3
Figure 3
Random effects meta-analysis of intraoperative adverse events machine learning algorithms. HSROC: hierarchical summary receiver operating characteristics.
Figure 4
Figure 4
QUADAS-2 grading of article risk of bias and concerns regarding applicability (n = 13).
Figure 5
Figure 5
Flow-chart depicting methods of included articles, generally applicable to all included studies. AUC area under curve, RMSE root–mean square error.
See this image and copyright information in PMC

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