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

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.

AI in Surgical Curriculum Design and Unintended Outcomes for Technical Competencies in Simulation Training

“…With advancements in live intraoperative data acquisition for quality improvement, AI systems are believed to play a key role in mitigating adverse events and suggesting instructions in the operating room . Rolling out intelligent systems for patient care or formal postgraduate training should occur in phases and requires the same rigor of scientific practice required for pharmacologic therapies or other medical devices.…”

“…With advancements in live intraoperative data acquisition for quality improvement, AI systems are believed to play a key role in mitigating adverse events and suggesting instructions in the operating room. 5 , 30 , 31 , 32 , 33 Rolling out intelligent systems for patient care or formal postgraduate training should occur in phases and requires the same rigor of scientific practice required for pharmacologic therapies or other medical devices. For our study, this would involve investigating the outcomes of an AI-augmented curriculum designed in collaboration with the neurosurgical training program to offer a hybrid of expert instruction with the state-of-the-art Intelligent Continuous Expertise Monitoring System training in a simulated operative environment with realistic tissues.…”

Layer-Weighted Attention and Ascending Feature Selection: An Approach for Seriousness Level Prediction Using the FDA Adverse Event Reporting System

“…This entailed conducting an extra level of analysis on a substantial portion of the FAERS dataset to gain further insights into these interactions. As explained in [4], AI progress holds promise in allowing for the automatic detection of adverse events in real time. Recent breakthroughs in deep learning and computer vision algorithms have already proven their ability to identify high-risk anatomical regions and possible surgical mistakes during the real-time analysis of laparoscopic surgical videos.…”