Computer Vision in the Operating Room: Opportunities and Caveats.

Effectiveness of computer vision techniques has been demonstrated through a number of applications, both within and outside healthcare. The operating room environment specifically is a setting with rich data sources compatible with computational approaches and high potential for direct patient benefit. The aim of this review is to summarize major topics in computer vision for surgical domains. The major capabilities of computer vision are described as an aid to surgical teams to improve performance and contribute to enhanced patient safety. Literature was identified through leading experts in the fields of surgery, computational analysis and modeling in medicine, and computer vision in healthcare. The literature supports the application of computer vision principles to surgery. Potential applications within surgery include operating room vigilance, endoscopic vigilance, and individual and team-wide behavioral analysis. To advance the field, we recommend collecting and publishing carefully annotated datasets. Doing so will enable the surgery community to collectively define well-specified common objectives for automated systems, spur academic research, mobilize industry, and provide benchmarks with which we can track progress. Leveraging computer vision approaches through interdisciplinary collaboration and advanced approaches to data acquisition, modeling, interpretation, and integration promises a powerful impact on patient safety, public health, and financial costs.

Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare.

Shared mental models are critical to team success; however, in practice, team members may have misaligned models due to a variety of factors. In safety-critical domains (e.g., aviation, healthcare), lack of shared mental models can lead to preventable errors and harm. Towards the goal of mitigating such preventable errors, here, we present a Bayesian approach to infer misalignment in team members' mental models during complex healthcare task execution. As an exemplary application, we demonstrate our approach using two simulated team-based scenarios, derived from actual teamwork in cardiac surgery. In these simulated experiments, our approach inferred model misalignment with over 75% recall, thereby providing a building block for enabling computer-assisted interventions to augment human cognition in the operating room and improve teamwork.

Artificial intelligence in cardiothoracic surgery.

The tremendous and rapid technological advances that humans have achieved in the last decade have definitely impacted how surgical tasks are performed in the operating room (OR). As a high-tech work environment, the contemporary OR has incorporated novel computational systems into the clinical workflow, aiming to optimize processes and support the surgical team. Artificial intelligence (AI) is increasingly important for surgical decision making to help address diverse sources of information, such as patient risk factors, anatomy, disease natural history, patient values and cost, and assist surgeons and patients to make better predictions regarding the consequences of surgical decisions. In this review, we discuss the current initiatives that are using AI in cardiothoracic surgery and surgical care in general. We also address the future of AI and how high-tech ORs will leverage human-machine teaming to optimize performance and enhance patient safety.

Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography.

Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces.

Analyzing the effects of human-aware motion planning on close-proximity human-robot collaboration.

OBJECTIVE: The objective of this work was to examine human response to motion-level robot adaptation to determine its effect on team fluency, human satisfaction, and perceived safety and comfort. BACKGROUND: The evaluation of human response to adaptive robotic assistants has been limited, particularly in the realm of motion-level adaptation. The lack of true human-in-the-loop evaluation has made it impossible to determine whether such adaptation would lead to efficient and satisfying human-robot interaction. METHOD: We conducted an experiment in which participants worked with a robot to perform a collaborative task. Participants worked with an adaptive robot incorporating human-aware motion planning and with a baseline robot using shortest-path motions. Team fluency was evaluated through a set of quantitative metrics, and human satisfaction and perceived safety and comfort were evaluated through questionnaires. RESULTS: When working with the adaptive robot, participants completed the task 5.57% faster, with 19.9% more concurrent motion, 2.96% less human idle time, 17.3% less robot idle time, and a 15.1% greater separation distance. Questionnaire responses indicated that participants felt safer and more comfortable when working with an adaptive robot and were more satisfied with it as a teammate than with the standard robot. CONCLUSION: People respond well to motion-level robot adaptation, and significant benefits can be achieved from its use in terms of both human-robot team fluency and human worker satisfaction. APPLICATION: Our conclusion supports the development of technologies that could be used to implement human-aware motion planning in collaborative robots and the use of this technique for close-proximity human-robot collaboration.

Results of thoracic endovascular aortic repair 6 years after United States Food and Drug Administration approval.

BACKGROUND: Since United States Food and Drug Administration approval in 2005, the short-term safety and efficacy of thoracic endovascular aortic repair (TEVAR) have been established. However, longer-term follow-up data remain lacking. The objective of this study is to report 6-year outcomes of TEVAR in clinical practice. METHODS: A prospective cohort review was performed of all patients undergoing TEVAR at a single referral institution between March 2005 and May 2011. Rates of reintervention were noted. Overall and aortic-specific survival were determined using Kaplan-Meier methods. Log-rank tests were used to compare survival between groups. RESULTS: During the study interval, 332 TEVAR procedures were performed in 297 patients. Reintervention was required after 12% of procedures at a mean of 8 ± 14 months after initial TEVAR and was higher in the initial tercile of patients (15.0% vs 9.9%). The 6-year freedom from reintervention was 84%. Type I endoleak was the most common cause of reintervention (5%). Six-year overall survival was 54%, and aorta-specific survival was 92%. Long-term survival was significantly lower than that of an age- and sex-matched United States population (p < 0.001). Survival was similar between patients requiring a reintervention vs those not (p = 0.26). Survival was different based on indication for TEVAR (p = 0.007), and patients with degenerative aneurysms had the lowest survival (47% at 6 years). Cardiopulmonary pathologies were the most common cause of death (27 of 93 total deaths). CONCLUSIONS: Long-term aortic-related survival after TEVAR is high, and the need for reintervention is infrequent. However, overall long-term survival is low, particularly for patients with degenerative aneurysms, and additional work is needed to identify patients unlikely to derive a survival benefit from TEVAR.