Artificial Intelligence for Computer Vision in Surgery

摘要

Advances in computing power and the availability of digital data have led to significant progress in artificial intelligence (AI) algorithms. As a result, novel and innovative applications of AI in healthcare continue to surface both in the scientific community and the lay press at a rapid pace. AI is the field of computer science that focuses on the development of algorithms that enable high-level and rational response, interaction, and advanced cognitive and perceptual functions by machines. One area of AI that has particularly bourgeoned over the last decade is computer vision (CV)—an interdisciplinary scientific field that deals with how computers can gain a high-level understanding of digital images or videos and the ability to perform functions, such as object identification and tracking and scene recognition.1 Various fields in medicine have had significant success in the development of AI models capable of performing a variety of diagnostic functions using CV (eg, identifying abnormalities in diagnostic radiology, identifying malignant skin lesions, and interpreting electrocardiograms), and there is potential for similar success in procedural specialties such as surgery. Clinicians and innovators alike have sought to develop AI algorithms capable of improving our ability to provide therapeutic interventions, such as with real-time decision-support and computer-assisted surgery. The number of scientific publications involving AI has increased steadily over the past decade, and many AI algorithms for medical applications have been approved for use by the Food and Drug Administration.2 However, despite early successes with this new technology, there are concerns regarding the most-appropriate methodology for the design, development, and validation of AI algorithms. Furthermore, the existing literature suffers from a methodological “black box” caused by incomplete reporting.2 Therefore, more transparency and interpretability of AI-based clinical research in medicine are necessary. The Consolidated Standard of Reporting (CONSORT) and Standard Protocol Items: Recommendations and Intervention Trials (SPIRIT) guidelines have been extended to AI studies through CONSORT-AI3 and SPIRIT-AI.4 The Standards for Reporting of Diagnostic Accuracy Studies (STARD) and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) will also be extended to STARD-AI5 and TRIPOD-ML.6 In addition, the minimal information about clinical artificial intelligence modeling 7 and minimum information for medical AI reporting 8 have been published as minimum reporting guidelines that aim to standardize medical AI research in terms of transparency and utility. Since the majority of AI interventions in medicine involve the field of computer-assisted diagnosis, the existing reporting guidelines have focused on studies related to computer-assisted diagnosis, such as diagnostic accuracy, prediction models, clinical decision support, and implementations in clinical trials (Table 1). There has not been much attention paid to surgical applications of AI algorithms that could assist in decision-making based on CV analysis of operative performance. TABLE 1 - Reporting Guidelines for Studies Involving Artificial Intelligence and Machine Learning Reporting Guideline(Year of Publication) Type of Study Number of Items Concept/Intended Use CONSORT-AI(2020) RCT 25(5 extended items) Recommendations for investigators to provide clear descriptions of the AI intervention regarding instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention as well as human-AI interaction, and analysis of error cases. SPIRIT-AI (2020) Study protocols of clinical trials 33(7 extended items) MI-CLAIM(2020) Clinical AI modeling 6 Developed to better inform readers and users about the machine learning models themselves, especially regarding its design and