A marker-less technique for measuring kinematics in the operating room

Enhancing surgical performance in cardiothoracic surgery with innovations from computer vision and artificial intelligence: a narrative review

When technical requirements are high, and patient outcomes are critical, opportunities for monitoring and improving surgical skills via objective motion analysis feedback may be particularly beneficial. This narrative review synthesises work on technical and non-technical surgical skills, collaborative task performance, and pose estimation to illustrate new opportunities to advance cardiothoracic surgical performance with innovations from computer vision and artificial intelligence. These technological innovations are critically evaluated in terms of the benefits they could offer the cardiothoracic surgical community, and any barriers to the uptake of the technology are elaborated upon. Like some other specialities, cardiothoracic surgery has relatively few opportunities to benefit from tools with data capture technology embedded within them (as is possible with robotic-assisted laparoscopic surgery, for example). In such cases, pose estimation techniques that allow for movement tracking across a conventional operating field without using specialist equipment or markers offer considerable potential. With video data from either simulated or real surgical procedures, these tools can (1) provide insight into the development of expertise and surgical performance over a surgeon's career, (2) provide feedback to trainee surgeons regarding areas for improvement, (3) provide the opportunity to investigate what aspects of skill may be linked to patient outcomes which can (4) inform the aspects of surgical skill which should be focused on within training or mentoring programmes. Classifier or assessment algorithms that use artificial intelligence to 'learn' what expertise is from expert surgical evaluators could further assist educators in determining if trainees meet competency thresholds. With collaborative efforts between surgical teams, medical institutions, computer scientists and researchers to ensure this technology is developed with usability and ethics in mind, the developed feedback tools could improve cardiothoracic surgical practice in a data-driven way.

An Automated Skill Assessment Framework Based on Visual Motion Signals and a Deep Neural Network in Robot-Assisted Minimally Invasive Surgery

Surgical skill assessment can quantify the quality of the surgical operation via the motion state of the surgical instrument tip (SIT), which is considered one of the effective primary means by which to improve the accuracy of surgical operation. Traditional methods have displayed promising results in skill assessment. However, this success is predicated on the SIT sensors, making these approaches impractical when employing the minimally invasive surgical robot with such a tiny end size. To address the assessment issue regarding the operation quality of robot-assisted minimally invasive surgery (RAMIS), this paper proposes a new automatic framework for assessing surgical skills based on visual motion tracking and deep learning. The new method innovatively combines vision and kinematics. The kernel correlation filter (KCF) is introduced in order to obtain the key motion signals of the SIT and classify them by using the residual neural network (ResNet), realizing automated skill assessment in RAMIS. To verify its effectiveness and accuracy, the proposed method is applied to the public minimally invasive surgical robot dataset, the JIGSAWS. The results show that the method based on visual motion tracking technology and a deep neural network model can effectively and accurately assess the skill of robot-assisted surgery in near real-time. In a fairly short computational processing time of 3 to 5 s, the average accuracy of the assessment method is 92.04% and 84.80% in distinguishing two and three skill levels. This study makes an important contribution to the safe and high-quality development of RAMIS.

Specifying Inputs for the Computational Structure of a Surgical System via Optical Method and DLT Algorithm Based on In Vitro Experiments on Cardiovascular Tissue in Minimally Invasive and Robotic Surgery

With the application of four optical CMOS sensors, it was possible to capture the trajectory of an endoscopic tool during an in vitro surgical experiment on a cardiovascular preparation. This was due to the possibility of obtaining a path when a reflective marker was attached. In the work, APAS (Ariel Performance Analysis System) software and DLT (direct linear transformation) algorithm were used. This made it possible to acquire kinematic inputs to the computational model of dynamics, which enabled, regardless of the type of surgical robot structure, derivation of the analogous motion of an endoscopic effector due to the mathematical transformation of the trajectory to joints coordinates. Experiments were carried out with the participation of a practiced cardiac surgeon employing classic endoscopic instruments and robot surgical systems. The results indicated by the experiment showed that the inverse task of kinematics of position for the surgical robot with RCM (remote center of motion) structure was solved. The achieved results from the experiment were used as inputs for deriving a numerical dynamics model of surgical robot during transient states that was obtained by applying the finite element method and by driving dynamics moments acquired through the block diagrams method using a steering system with DC (direct current) motor and PID (proportional–integral–derivative) controller. The results section illustrates the course of kinematic values of endoscopic tools which were employed to apply numerical models as inputs, the course of the driving torque of the model of the surgical robot that enabled the selection of the drive system and the strength values, stresses and displacements according to von Mises hypothesis in its structure during the analysis of transient states that made it possible to establish the strength safety of the surgical robot. For the conducted experiments, the accuracy was ±2 [mm]. In the paper, the employment of optical CMOS sensors in surgical robotics and endoscopy is discussed. The paper concludes that the usage of optical sensors for determining inputs for numerical models of dynamics of surgical robots provides the basis for setting the course of physical quantities that appear in their real object structure, in manners close to reality.

Modeling Performance of Open Surgical Cases

INTRODUCTION

Previous efforts used digital video to develop computer-generated assessments of surgical hand motion economy and fluidity of motion. This study tests how well previously trained assessment models match expert ratings of suturing and tying video clips recorded in a new operating room (OR) setting.

METHODS

Enabled through computer vision of the hands, this study tests the applicability of assessments born out of benchtop simulations to in vivo suturing and tying tasks recorded in the OR.

RESULTS

Compared with expert ratings, computer-generated assessments for fluidity of motion (slope = 0.83, intercept = 1.77, R2 = 0.55) performed better than motion economy (slope = 0.73, intercept = 2.04, R2 = 0.49), although 85% of ratings for both models were within ±2 of the expert response. Neither assessment performed as well in the OR as they did on the training data. Assessments were sensitive to changing hand postures, dropped ligatures, and poor tissue contact-features typically missing from training data. Computer-generated assessment of OR tasks was contingent on a clear, consistent view of both surgeon's hands.

CONCLUSIONS

Computer-generated assessment may help provide formative feedback during deliberate practice, albeit with greater variability in the OR compared with benchtop simulations. Future work will benefit from expanded available bimanual video records.

Surgical training 2.0: A systematic approach reviewing the literature focusing on oral maxillofacial surgery - Part I

PURPOSE

Many technologies are emerging in the medical field. Having an overview of the technological arsenal available to train new surgeons seems very interesting to guide subsequent surgical training protocols.

METHODS

This article is a systematic approach reviewing new technologies in surgical training, in particular in oral and maxillofacial surgery. This review explores what new technologies can do compared to traditional methods in the field of surgical education. A structured literature search of PubMed was performed in adherence to PRISMA guidelines. The articles were selected when they fell within predefined inclusion criteria while respecting the key objectives of this systematic review. We looked at medical students and more specifically in surgery and analysed whether exposure to new technologies improved their surgical skills compared to traditional methods. Each technology is reviewed by highlighting its advantages and disadvantages and studying the feasibility of integration into current practice.

RESULTS

The results are encouraging. Indeed, all of these technologies make it possible to reduce the learning time, the operating times, the operating complications and increase the enthusiasm of the students compared to more conventional methods. The start-up cost, the complexity to develop new models, and the openness of mind necessary for the integration of these technologies are all obstacles to immediate development. The main limitations of this review are that many of the studies have been carried out on small numbers, they are not interested in acquiring knowledge or skills over the long term and obviously there is a publication bias.

CONCLUSION

Surgical education methods will probably change in the years to come, integrating these new technologies into the curriculum seems essential so as not to remain on the side. This first part therefore reviews, open field camera, telemedicine and 3D printing. This systematic review is registered on PROSPERO.

Quantifying surgeon maneuevers across experience levels through marker-less hand motion kinematics of simulated surgical tasks

This paper compares clinician hand motion for common suturing tasks across a range of experience levels and tissue types. Medical students (32), residents (41), attending surgeons (10), and retirees (2) were recorded on digital video while suturing on one of: foam, pig feet, or porcine bowel. Depending on time in position, each medical student, resident, and attending participant was classified as junior or senior, yielding six experience categories. This work focuses on trends associated with increasing tenure observed from those medical students (10), residents (15), and attendings (10) who sutured on foam, and draws comparison across tissue types where pertinent. Utilizing custom software, the two-dimensional location of each of the participant's hands were automatically recorded in every video frame, producing a rich spatiotemporal feature set. While suturing on foam, increasing clinician experience was associated with conserved path length per cycle of the non-dominant hand, significantly reducing from junior medical students (mean = 73.63 cm, sd = 33.21 cm) to senior residents (mean = 46.16 cm, sd = 14.03 cm, p = 0.015), and again between senior residents and senior attendings (mean = 30.84 cm, sd = 14.51 cm, p = 0.045). Despite similar maneuver rates, attendings also accelerated less with their non-dominant hand (mean = 16.27 cm/s², sd = 81.12 cm/s², p = 0.002) than senior residents (mean = 24.84 cm/s², sd = 68.29 cm/s², p = 0.002). While tying, medical students moved their dominant hands slower (mean = 4.39 cm/s, sd = 1.73 cm/s, p = 0.033) than senior residents (mean = 6.53 cm/s, sd = 2.52 cm/s). These results suggest that increased psychomotor performance during early training manifest through faster dominant hand function, while later increases are characterized by conserving energy and efficiently distributing work between hands. Incorporating this scalable video-based motion analysis into regular formative assessment routines may enable greater quality and consistency of feedback throughout a surgical career.

Evaluation of Intraoperative Hand-Off Frequency, Duration, and Context: A Mixed Methods Analysis

BACKGROUND

Hand-offs in the operating room contribute to poor communication, reduced team function, and may be poorly coordinated with other activities. Conversely, they may represent a missed opportunity for improved communication. We sought to better understand the coordination and impact of intraoperative hand-offs.

METHODS

We prospectively audio-video (AV) recorded 10 operations and evaluated intraoperative hand-offs. Data collected included percentage of time team members were absent due to breaks, relationships between hand-offs and intraoperative events (incision, surgical counts), and occurrences of simultaneous hand-offs. We also identified announcement that a hand-off had occurred and anchoring, in which team members not involved in the hand-off participated and provided information.

RESULTS

Spanning 2919 min of audio-video data, there were 74 hand-offs (range, 4-14 per case) totaling 225.2 min, representing 7.7% of time recorded. Thirty-two (45.1%) hand-offs were interrupted or delayed because of competing activities; eight hand-offs occurred during an instrument or laparotomy pad count. Six cases had simultaneous hand-offs; two cases had two episodes of simultaneous hand-offs. Eight hand-offs included an announcement. Seven included anchoring. Evaluating both temporary and permanent hand-offs, one or more original team members was absent for 40.7% of time recorded and >one team member was absent for 20.5% of time recorded.

CONCLUSIONS

Intraoperative hand-offs are frequent and not well coordinated with intraoperative events including counts and other hand-offs. Anchoring and announced hand-offs occurred in a small proportion of cases. Future work must focus on optimizing timing, content, and participation in intraoperative hand-offs.

Evidence That Female Urologists and Urology Trainees Tend to Underrate Surgical Skills on Self-Assessment

BACKGROUND

Historically low, the proportion of female urology residents now exceeds 25% in recent years. Self-assessment is a widely used tool to track progress in medical education. However, the validity of its results and gender differences may influence interpretation. Simulation of surgical skills is increasingly common in modern residency training and standardizes certain objective tasks and skills. The objective of this study was to identify gender differences in self-assessment of surgeons and trainees when using simulation of surgical skills.

METHODS

Medical students, residents, and attending and retired surgeons completed simple interrupted suturing. Assessment was self-rated using previously tested visual analog motion scales. Tasks were video recorded and rated by blinded expert surgeons using identical motion scales. Computer vision motion tracking software was used to objectively analyze the kinematics of surgical tasks.

RESULTS

Proportion of female (n = 17) and male (n = 20) participants did not differ significantly by the level of training, P = 0.76. Five expert surgeons evaluated 84 video segments of simple interrupted suturing tasks (mean 3.0 segments per task per participant). Self-assessment correlated well overall with expert rating for motion economy (Pearson correlation coefficient 0.61, P < 0.001) and motion fluidity (0.55, P = 0.002). Women underrated their performance in accordance with mean individual difference of self-assessment and expert assessment scores (Δ SAS-EAS) for both economy of motion (mean ± SEM -1.1 ± 0.38, P = 0.01) and fluidity of motion (-1.3 ± 0.39, P < 0.01). On the same measures, men tended to rate themselves in accordance with experts (-0.16 ± 0.36, P = 0.63; -0.09 ± 0.41, P = 0.82, respectively). Δ SAS-EAS did not differ significantly on any rating scale across levels of training. Expert ratings did not differ significantly by gender for any domain.

CONCLUSIONS

Female surgeons and trainees underrate some technical skills on self-assessment when compared with expert ratings, whereas male surgeon and trainee self-ratings and expert ratings were similar. Further work is needed to determine if these differences are accentuated across increasingly difficult tasks.

Video review for measuring and improving skill in urological surgery

Interest is growing within the urological surgery community for objective assessments of technical skill. Surgical video review relies on the use of objective assessment tools to evaluate both global and procedure-specific skill. These evaluations provide structured feedback to surgeons with the aim of improving technique, which has been associated with patient outcomes. Currently, skill assessments can be performed by using expert peer-review, crowdsourcing or computer-based methods. Given the relationship between skill and patient outcomes, surgeons might be required in the future to provide empirical evidence of their technical skill for certification, employment, credentialing and quality improvement. Interventions such as coaching and skills workshops incorporating video review might help surgeons improve their skill, with the ultimate goal of improving patient outcomes.

Using Surgeon Hand Motions to Predict Surgical Maneuvers

OBJECTIVE

This study explores how common machine learning techniques can predict surgical maneuvers from a continuous video record of surgical benchtop simulations.

BACKGROUND

Automatic computer vision recognition of surgical maneuvers (suturing, tying, and transition) could expedite video review and objective assessment of surgeries.

METHOD

We recorded hand movements of 37 clinicians performing simple and running subcuticular suturing benchtop simulations, and applied three machine learning techniques (decision trees, random forests, and hidden Markov models) to classify surgical maneuvers every 2 s (60 frames) of video.

RESULTS

Random forest predictions of surgical video correctly classified 74% of all video segments into suturing, tying, and transition states for a randomly selected test set. Hidden Markov model adjustments improved the random forest predictions to 79% for simple interrupted suturing on a subset of randomly selected participants.

CONCLUSION

Random forest predictions aided by hidden Markov modeling provided the best prediction of surgical maneuvers. Training of models across all users improved prediction accuracy by 10% compared with a random selection of participants.

APPLICATION

Marker-less video hand tracking can predict surgical maneuvers from a continuous video record with similar accuracy as robot-assisted surgical platforms, and may enable more efficient video review of surgical procedures for training and coaching.

In Search of Characterizing Surgical Skill

OBJECTIVE

This paper provides a literature review and detailed discussion of surgical skill terminology. Culminating in a novel model that proposes a set of unique definitions, this review is designed to facilitate shared understanding to study and develop metrics quantifying surgical skill.

DESIGN

Objective surgical skill analysis depends on consistent definitions and shared understanding of terms like performance, expertise, experience, aptitude, ability, competency, and proficiency.

STRUCTURE

Each term is discussed in turn, drawing from existing literature and colloquial uses.

IMPLICATIONS

A new model of definitions is proposed to cement a common and consistent lexicon for future skills analysis, and to quantitatively describe a surgeon's performance throughout their career.

Modeling Surgical Technical Skill Using Expert Assessment for Automated Computer Rating

OBJECTIVE

Computer vision was used to predict expert performance ratings from surgeon hand motions for tying and suturing tasks.

SUMMARY BACKGROUND DATA

Existing methods, including the objective structured assessment of technical skills (OSATS), have proven reliable, but do not readily discriminate at the task level. Computer vision may be used for evaluating distinct task performance throughout an operation.

METHODS

Open surgeries was videoed and surgeon hands were tracked without using sensors or markers. An expert panel of 3 attending surgeons rated tying and suturing video clips on continuous scales from 0 to 10 along 3 task measures adapted from the broader OSATS: motion economy, fluidity of motion, and tissue handling. Empirical models were developed to predict the expert consensus ratings based on the hand kinematic data records.

RESULTS

The predicted versus panel ratings for suturing had slopes from 0.73 to 1, and intercepts from 0.36 to 1.54 (Average R2 = 0.81). Predicted versus panel ratings for tying had slopes from 0.39 to 0.88, and intercepts from 0.79 to 4.36 (Average R2 = 0.57). The mean square error among predicted and expert ratings was consistently less than the mean squared difference among individual expert ratings and the eventual consensus ratings.

CONCLUSIONS

The computer algorithm consistently predicted the panel ratings of individual tasks, and were more objective and reliable than individual assessment by surgical experts.

Familiarity and Communication in the Operating Room

BACKGROUND

Poor communication is implicated in many adverse events in the operating room (OR); however, many hospitals' scheduling practices permit unfamiliar operative teams. The relationship between unfamiliarity, team communication and effectiveness of communication is poorly understood. We sought to evaluate the relationship between familiarity, communication rates, and communication ineffectiveness of health care providers in the OR.

MATERIALS AND METHODS

We performed purposive sampling of 10 open operations. For each case, six providers (anesthesiology attending, in-room anesthetist, circulator, scrub, surgery attending, and surgery resident) were queried about the number of mutually shared cases. We identified communication events and created dyad-specific communication rates.

RESULTS

Analysis of 48 h of audio-video content identified 2570 communication events. Operations averaged 58.0 communication events per hour (range, 29.4-76.1). Familiarity was not associated with communication rate (P = 0.69) or communication ineffectiveness (P = 0.21). Cross-disciplinary dyads had lower communication rates than intradisciplinary dyads (P < 0.001). Anesthesiology-nursing, anesthesiology-surgery, and nursing-surgery dyad communication rates were 20.1%, 42.7%, and 57.3% the rate predicted from intradisciplinary dyads, respectively. In addition, cross-disciplinary dyad status was a significant predictor of having at least one ineffective communication event (P = 0.02).

CONCLUSIONS

Team members do not compensate for unfamiliarity by increasing their verbal communication, and dyad familiarity is not protective against ineffective communication. Cross-disciplinary communication remains vulnerable in the OR suggesting poor crosstalk across disciplines in the operative setting. Further investigation is needed to explore these relationships and identify effective interventions, ensuring that all team members have the necessary information to optimize their performance.

Surgical data science: The new knowledge domain

Healthcare in general, and surgery/interventional care in particular, is evolving through rapid advances in technology and increasing complexity of care, with the goal of maximizing the quality and value of care. Whereas innovations in diagnostic and therapeutic technologies have driven past improvements in the quality of surgical care, future transformation in care will be enabled by data. Conventional methodologies, such as registry studies, are limited in their scope for discovery and research, extent and complexity of data, breadth of analytical techniques, and translation or integration of research findings into patient care. We foresee the emergence of surgical/interventional data science (SDS) as a key element to addressing these limitations and creating a sustainable path toward evidence-based improvement of interventional healthcare pathways. SDS will create tools to measure, model, and quantify the pathways or processes within the context of patient health states or outcomes and use information gained to inform healthcare decisions, guidelines, best practices, policy, and training, thereby improving the safety and quality of healthcare and its value. Data are pervasive throughout the surgical care pathway; thus, SDS can impact various aspects of care, including prevention, diagnosis, intervention, or postoperative recovery. The existing literature already provides preliminary results, suggesting how a data science approach to surgical decision-making could more accurately predict severe complications using complex data from preoperative, intraoperative, and postoperative contexts, how it could support intraoperative decision-making using both existing knowledge and continuous data streams throughout the surgical care pathway, and how it could enable effective collaboration between human care providers and intelligent technologies. In addition, SDS is poised to play a central role in surgical education, for example, through objective assessments, automated virtual coaching, and robot-assisted active learning of surgical skill. However, the potential for transforming surgical care and training through SDS may only be realized through a cultural shift that not only institutionalizes technology to seamlessly capture data but also assimilates individuals with expertise in data science into clinical research teams. Furthermore, collaboration with industry partners from the inception of the discovery process promotes optimal design of data products as well as their efficient translation and commercialization. As surgery continues to evolve through advances in technology that enhance delivery of care, SDS represents a new knowledge domain to engineer surgical care of the future.