Impact of artificial intelligence on the training of general surgeons of the future: a scoping review of the advances and challenges

PURPOSE

To explore artificial intelligence's impact on surgical education, highlighting its advantages and challenges.

METHODS

A comprehensive search across databases such as PubMed, Scopus, Scientific Electronic Library Online (SciELO), Embase, Web of Science, and Google Scholar was conducted to compile relevant studies.

RESULTS

Artificial intelligence offers several advantages in surgical training. It enables highly realistic simulation environments for the safe practice of complex procedures. Artificial intelligence provides personalized real-time feedback, improving trainees' skills. It efficiently processes clinical data, enhancing diagnostics and surgical planning. Artificial intelligence-assisted surgeries promise precision and minimally invasive procedures. Challenges include data security, resistance to artificial intelligence adoption, and ethical considerations.

CONCLUSIONS

Stricter policies and regulatory compliance are needed for data privacy. Addressing surgeons' and educators' reluctance to embrace artificial intelligence is crucial. Integrating artificial intelligence into curricula and providing ongoing training are vital. Ethical, bioethical, and legal aspects surrounding artificial intelligence demand attention. Establishing clear ethical guidelines, ensuring transparency, and implementing supervision and accountability are essential. As artificial intelligence evolves in surgical training, research and development remain crucial. Future studies should explore artificial intelligence-driven personalized training and monitor ethical and legal regulations. In summary, artificial intelligence is shaping the future of general surgeons, offering advanced simulations, personalized feedback, and improved patient care. However, addressing data security, adoption resistance, and ethical concerns is vital. Adapting curricula and providing continuous training are essential to maximize artificial intelligence's potential, promoting ethical and safe surgery.

Assessment of Open Surgery Suturing Skill: Image-based Metrics Using Computer Vision

OBJECTIVE

This paper presents a computer vision algorithm for extraction of image-based metrics for suturing skill assessment and the corresponding results from an experimental study of resident and attending surgeons.

DESIGN

A suturing simulator that adapts the radial suturing task from the Fundamentals of Vascular Surgery (FVS) skills assessment is used to collect data. The simulator includes a camera positioned under the suturing membrane, which records needle and thread movement during the suturing task. A computer vision algorithm processes the video data and extracts objective metrics inspired by expert surgeons' recommended best practice, to "follow the curvature of the needle."

PARTICIPANTS AND RESULTS

Experimental data from a study involving subjects with various levels of suturing expertise (attending surgeons and surgery residents) are presented. Analysis shows that attendings and residents had statistically different performance on 6 of 9 image-based metrics, including the four new metrics introduced in this paper: Needle Tip Path Length, Needle Swept Area, Needle Tip Area and Needle Sway Length.

CONCLUSION AND SIGNIFICANCE

These image-based process metrics may be represented graphically in a manner conducive to training. The results demonstrate the potential of image-based metrics for assessment and training of suturing skill in open surgery.

Surgical Artificial Intelligence in Urology: Educational Applications

Surgical education has seen immense change recently. Increased demand for iterative evaluation of trainees from medical school to independent practice has led to the generation of an overwhelming amount of data related to an individual's competency. Artificial intelligence has been proposed as a solution to automate and standardize the ability of stakeholders to assess the technical and nontechnical abilities of a surgical trainee. In both the simulation and clinical environments, evidence supports the use of machine learning algorithms to both evaluate trainee skill and provide real-time and automated feedback, enabling a shortened learning curve for many key procedural skills and ensuring patient safety.

Learning Skill Characteristics From Manipulations

Percutaneous coronary intervention (PCI) has increasingly become the main treatment for coronary artery disease. The procedure requires high experienced skills and dexterous manipulations. However, there are few techniques to model PCI skill so far. In this study, a learning framework with local and ensemble learning is proposed to learn skill characteristics of different skill-level subjects from their PCI manipulations. Ten interventional cardiologists (four experts and six novices) were recruited to deliver a medical guidewire to two target arteries on a porcine model for in vivo studies. Simultaneously, translation and twist manipulations of thumb, forefinger, and wrist are acquired with electromagnetic (EM) and fiber-optic bend (FOB) sensors, respectively. These behavior data are then processed with wavelet packet decomposition (WPD) under 1-10 levels for feature extraction. The feature vectors are further fed into three candidate individual classifiers in the local learning layer. Furthermore, the local learning results from different manipulation behaviors are fused in the ensemble learning layer with three rule-based ensemble learning algorithms. In subject-dependent skill characteristics learning, the ensemble learning can achieve 100% accuracy, significantly outperforming the best local result (90%). Furthermore, ensemble learning can also maintain 73% accuracy in subject-independent schemes. These promising results demonstrate the great potential of the proposed method to facilitate skill learning in surgical robotics and skill assessment in clinical practice.

A systematic review on classification and assessment of surgical skill levels for simulation-based training programs

BACKGROUND

Nowadays, advances in medical informatics have made minimally invasive surgery (MIS) procedures the preferred choice. However, there are several problems with the education programs in terms of surgical skill acquisition. For instance, defining and objectively measuring surgical skill levels is a challenging process. Accordingly, the aim of this study is to conduct a literature review for an investigation of the current approaches for classifying the surgical skill levels and for identifying the skill training tools and measurement methods.

MATERIALS AND METHODS

In this research, a search is conducted and a corpus is created. Exclusion and inclusion criteria are applied by limiting the number of articles based on surgical education, training approximations, hand movements, and endoscopic or laparoscopic operations. To satisfy these criteria, 57 articles are included in the corpus of this study.

RESULTS

Currently used surgical skill assessment approaches have been summarized. Results show that various classification approaches for the surgical skill level definitions are being used. Besides, many studies are conducted by omitting particularly important skill levels in between. Additionally, some inconsistencies are also identified across the skill level classification studies.

CONCLUSION

In order to improve the benefits of simulation-based training programs, a standardized interdisciplinary approach should be developed. For this reason, specific to each surgical procedure, the required skills should be identified. Additionally, appropriate measures for assessing these skills, which can be defined in simulation-based MIS training environments, should be refined. Finally, the skill levels gained during the developmental stages of these skills, with their threshold values referencing the identified measures, should be redefined in a standardized manner.

Assessment of open surgery suturing skill: Simulator platform, force-based, and motion-based metrics

Objective

This paper focuses on simulator-based assessment of open surgery suturing skill. We introduce a new surgical simulator designed to collect synchronized force, motion, video and touch data during a radial suturing task adapted from the Fundamentals of Vascular Surgery (FVS) skill assessment. The synchronized data is analyzed to extract objective metrics for suturing skill assessment.

Methods

The simulator has a camera positioned underneath the suturing membrane, enabling visual tracking of the needle during suturing. Needle tracking data enables extraction of meaningful metrics related to both the process and the product of the suturing task. To better simulate surgical conditions, the height of the system and the depth of the membrane are both adjustable. Metrics for assessment of suturing skill based on force/torque, motion, and physical contact are presented. Experimental data are presented from a study comparing attending surgeons and surgery residents.

Results

Analysis shows force metrics (absolute maximum force/torque in z-direction), motion metrics (yaw, pitch, roll), physical contact metric, and image-enabled force metrics (orthogonal and tangential forces) are found to be statistically significant in differentiating suturing skill between attendings and residents.

Conclusion and significance

The results suggest that this simulator and accompanying metrics could serve as a useful tool for assessing and teaching open surgery suturing skill.

Surgesture: a novel instrument based on surgical actions for objective skill assessment

BACKGROUND

Due to varied surgical skills and the lack of an efficient rating system, we developed Surgesture based on elementary functional surgical gestures performed by surgeons, which could serve as objective metrics to evaluate surgical performance in laparoscopic cholecystectomy (LC).

METHODS

We defined 14 LC basic Surgestures. Four surgeons annotated Surgestures among LC videos performed by experts and novices. The counts, durations, average action time, and dissection/exposure ratio (D/E ratio) of LC Surgestures were compared. The phase of mobilizing hepatocystic triangle (MHT) was extracted for skill assessment by three professors using a modified Global Operative Assessment of Laparoscopic Skills (mGOALS).

RESULTS

The novice operation time was significantly longer than the expert operation time (58.12 ± 19.23 min vs. 26.66 ± 8.00 min, P < 0.001), particularly during MHT phase. Novices had significantly more Surgestures than experts in both hands (P < 0.05). The left hand and inefficient Surgesture of novices were dramatically more than those of experts (P < 0.05). The experts demonstrated a significantly higher D/E ratio of duration than novices (0.79 ± 0.37 vs. 2.84 ± 1.98, P < 0.001). The counts and time pattern map of LC Surgestures during MHT demonstrated that novices tended to complete LC with more types of Surgestures and spent more time exposing the surgical scene. The performance metrics of LC Surgesture had significant but weak associations with each aspect of mGOALS.

CONCLUSION

The newly constructed Surgestures could serve as accessible and quantifiable metrics for demonstrating the operative pattern and distinguishing surgeons with various skills. The association between Surgestures and Global Rating Scale laid the foundation for establishing a bridge to automated objective surgical skill evaluation.

Exploring the roles of artificial intelligence in surgical education: A scoping review

BACKGROUND

Technology-enhanced teaching and learning, including Artificial Intelligence (AI) applications, has started to evolve in surgical education. Hence, the purpose of this scoping review is to explore the current and future roles of AI in surgical education.

METHODS

Nine bibliographic databases were searched from January 2010 to January 2021. Full-text articles were included if they focused on AI in surgical education.

RESULTS

Out of 14,008 unique sources of evidence, 93 were included. Out of 93, 84 were conducted in the simulation setting, and 89 targeted technical skills. Fifty-six studies focused on skills assessment/classification, and 36 used multiple AI techniques. Also, increasing sample size, having balanced data, and using AI to provide feedback were major future directions mentioned by authors.

CONCLUSIONS

AI can help optimize the education of trainees and our results can help educators and researchers identify areas that need further investigation.

Evaluation of an international medical E-learning course with natural language processing and machine learning

BACKGROUND

In the context of the ongoing pandemic, e-learning has become essential to maintain existing medical educational programmes. Evaluation of such courses has thus far been on a small scale at single institutions. Further, systematic appraisal of the large volume of qualitative feedback generated by massive online e-learning courses manually is time consuming. This study aimed to evaluate the impact of an e-learning course targeting medical students collaborating in an international cohort study, with semi-automated analysis of feedback using text mining and machine learning methods.

METHOD

This study was based on a multi-centre cohort study exploring gastrointestinal recovery following elective colorectal surgery. Collaborators were invited to complete a series of e-learning modules on key aspects of the study and complete a feedback questionnaire on the modules. Quantitative data were analysed using simple descriptive statistics. Qualitative data were analysed using text mining with most frequent words, sentiment analysis with the AFINN-111 and syuzhet lexicons and topic modelling using the Latent Dirichlet Allocation (LDA).

RESULTS

One thousand six hundred and eleventh collaborators from 24 countries completed the e-learning course; 1396 (86.7%) were medical students; 1067 (66.2%) entered feedback. 1031 (96.6%) rated the quality of the course a 4/5 or higher (mean 4.56; SD 0.58). The mean sentiment score using the AFINN was + 1.54/5 (5: most positive; SD 1.19) and + 0.287/1 (1: most positive; SD 0.390) using syuzhet. LDA generated topics consolidated into the themes: (1) ease of use, (2) conciseness and (3) interactivity.

CONCLUSIONS

E-learning can have high user satisfaction for training investigators of clinical studies and medical students. Natural language processing may be beneficial in analysis of large scale educational courses.

Ergonomic assessment of a new hand tool design for laparoscopic surgery based on surgeons' muscular activity

Laparoscopic surgery techniques are customarily used in non-invasive procedures. That said traditional surgical instruments and devices used by surgeons suffer from certain ergonomic deficiencies that may lead to physical complaints in upper limbs and back and general discomfort that may, in turn, affect the surgeon's skills during surgery. A novel design of the laparoscopic gripper handle is presented and compared with one of the most used instruments in this field in an attempt to overcome this problem. The assessment of the ergonomic feature of the novel design was performed by using time-frequency analysis of the surface electromyography (sEMG) signal during dynamic activities. Singular Spectrum Analysis (SSA) was used to decompose the sEMG signal and extract the median frequency of each muscle to assess muscle fatigue. The results reveal that using the proposed ergonomic grip reduces the mean values of the muscle activity during each of the proposed tasks. The novel design also improves the ease of use in laparoscopic surgery as it minimises high-pressure contact areas, reduces large amplitude movements and promotes a neutral position of the hand, wrist and forearm. Furthermore, the SSA method for time-frequency analysis provides a powerful tool to analyse a prescribed activity in ergonomic terms. The proposed methodology to assess muscle activity during surgery activities may be useful in the selection of surgical instruments when programming extended procedures, as it provides an additional selection criterion based on the surgeon's biomechanics and the proposed activity.

Comparative Study of the Use of Different Sizes of an Ergonomic Instrument Handle for Laparoscopic Surgery

Previous studies have shown that the handle design of laparoscopic instruments is crucial to surgical performance and surgeon’s ergonomics. In this study, four different sizes of an ergonomic laparoscopic handle design were tested in a blind and randomized fashion with twelve surgeons. They performed three laparoscopic tasks in order to analyze the influence of handle size. Execution time, wrist posture, and finger and palm pressure were evaluated during the performance of each task. The results show a significant reduction in the time required to complete the eye-manual coordination task using the appropriate handle. The incorrectly sized handle resulted in a rise in palm pressure and a reduction in the force exerted by the thumb during the transfer task. In the hand-eye coordination task, the use of the right handle size led to an increase in middle finger pressure. In general, surgeons had an ergonomically adequate wrist flexion in all tasks and an acceptable radio-ulnar deviation during the transfer task using the ergonomic instrument handle. Surgeons found it comfortable the use of the ergonomic handle. Therefore, the use of an appropriately sized instrument handle allows surgeons to improve ergonomics and surgical performance during the laparoscopic practice.

Artificial Intelligence in Medical Education: Best Practices Using Machine Learning to Assess Surgical Expertise in Virtual Reality Simulation

OBJECTIVE

Virtual reality simulators track all movements and forces of simulated instruments, generating enormous datasets which can be further analyzed with machine learning algorithms. These advancements may increase the understanding, assessment and training of psychomotor performance. Consequently, the application of machine learning techniques to evaluate performance on virtual reality simulators has led to an increase in the volume and complexity of publications which bridge the fields of computer science, medicine, and education. Although all disciplines stand to gain from research in this field, important differences in reporting exist, limiting interdisciplinary communication and knowledge transfer. Thus, our objective was to develop a checklist to provide a general framework when reporting or analyzing studies involving virtual reality surgical simulation and machine learning algorithms. By including a total score as well as clear subsections of the checklist, authors and reviewers can both easily assess the overall quality and specific deficiencies of a manuscript.

DESIGN

The Machine Learning to Assess Surgical Expertise (MLASE) checklist was developed to help computer science, medicine, and education researchers ensure quality when producing and reviewing virtual reality manuscripts involving machine learning to assess surgical expertise.

SETTING

This study was carried out at the McGill Neurosurgical Simulation and Artificial Intelligence Learning Centre.

PARTICIPANTS

The authors applied the checklist to 12 articles using machine learning to assess surgical expertise in virtual reality simulation, obtained through a systematic literature review.

RESULTS

Important differences in reporting were found between medical and computer science journals. The medical journals proved stronger in discussion quality and weaker in areas related to study design. The opposite trends were observed in computer science journals.

CONCLUSIONS

This checklist will aid in narrowing the knowledge divide between computer science, medicine, and education: helping facilitate the burgeoning field of machine learning assisted surgical education.

Machine Learning Identification of Surgical and Operative Factors Associated With Surgical Expertise in Virtual Reality Simulation

IMPORTANCE

Despite advances in the assessment of technical skills in surgery, a clear understanding of the composites of technical expertise is lacking. Surgical simulation allows for the quantitation of psychomotor skills, generating data sets that can be analyzed using machine learning algorithms.

OBJECTIVE

To identify surgical and operative factors selected by a machine learning algorithm to accurately classify participants by level of expertise in a virtual reality surgical procedure.

DESIGN, SETTING, AND PARTICIPANTS

Fifty participants from a single university were recruited between March 1, 2015, and May 31, 2016, to participate in a case series study at McGill University Neurosurgical Simulation and Artificial Intelligence Learning Centre. Data were collected at a single time point and no follow-up data were collected. Individuals were classified a priori as expert (neurosurgery staff), seniors (neurosurgical fellows and senior residents), juniors (neurosurgical junior residents), and medical students, all of whom participated in 250 simulated tumor resections.

EXPOSURES

All individuals participated in a virtual reality neurosurgical tumor resection scenario. Each scenario was repeated 5 times.

MAIN OUTCOMES AND MEASURES

Through an iterative process, performance metrics associated with instrument movement and force, resection of tissues, and bleeding generated from the raw simulator data output were selected by K-nearest neighbor, naive Bayes, discriminant analysis, and support vector machine algorithms to most accurately determine group membership.

RESULTS

A total of 50 individuals (9 women and 41 men; mean [SD] age, 33.6 [9.5] years; 14 neurosurgeons, 4 fellows, 10 senior residents, 10 junior residents, and 12 medical students) participated. Neurosurgeons were in practice between 1 and 25 years, with 9 (64%) involving a predominantly cranial practice. The K-nearest neighbor algorithm had an accuracy of 90% (45 of 50), the naive Bayes algorithm had an accuracy of 84% (42 of 50), the discriminant analysis algorithm had an accuracy of 78% (39 of 50), and the support vector machine algorithm had an accuracy of 76% (38 of 50). The K-nearest neighbor algorithm used 6 performance metrics to classify participants, the naive Bayes algorithm used 9 performance metrics, the discriminant analysis algorithm used 8 performance metrics, and the support vector machine algorithm used 8 performance metrics. Two neurosurgeons, 1 fellow or senior resident, 1 junior resident, and 1 medical student were misclassified.

CONCLUSIONS AND RELEVANCE

In a virtual reality neurosurgical tumor resection study, a machine learning algorithm successfully classified participants into 4 levels of expertise with 90% accuracy. These findings suggest that algorithms may be capable of classifying surgical expertise with greater granularity and precision than has been previously demonstrated in surgery.

Qualitative and Quantitative Assessment of Technical Skills in Percutaneous Coronary Intervention: In Vivo Porcine Studies

OBJECTIVE

Technical skill assessment plays an important role in the professional development of an interventionalist in percutaneous coronary intervention (PCI). However, most of the traditional assessment methods are time consuming and subjective. This paper aims to develop objective assessment techniques.

METHODS

In this study, a natural-behavior-based assessment framework is proposed to qualitatively and quantitatively assess technical skills in PCI. In vivo porcine studies were conducted to deliver a medical guidewire to two target coronaries of left circumflex arteries by six novice and four expert interventionalists. Simultaneously, four types of natural behaviors (i.e., hand motion, proximal force, muscle activity, and finger motion) were acquired from the subjects' dominant hand and arm. The features extracted from the behaviors of different skill-level groups were compared using the Mann-Whitney U-test for effective behavior selection. The effective ones were further applied in the Gaussian-mixture-model-based qualitative assessment and Mahalanobis-distance-based quantitative assessment.

RESULTS

The qualitative assessment achieves an accuracy of 92% to distinguish the novice and expert attempts, which is significantly higher than that of using single guidewire motions. Furthermore, the quantitative assessment can assign objective and effective scores for all attempts, indicating high correlation ( R = 0.9225) to those obtained by traditional methods.

CONCLUSION

The objective, effective, and comprehensive assessment of technical skills can be provided by qualitatively and quantitatively analyzing interventionalists' natural behaviors in PCI.

SIGNIFICANCE

This paper suggests a novel approach for the technical skill assessment and the promising results demonstrate the great importance and effectiveness of the proposed method for promoting the development of objective assessment techniques.

Development of novel force-limiting grasping forceps with a simple mechanism

OBJECTIVES

In endoscopic surgery, fragile tissues may be damaged by the application of excessive force. Thus, we developed novel endoscopic forceps with a simple force-limiting mechanism.

METHODS

The novel forceps were constructed with a leaf spring, and the spring thickness determines grasping pressure. We established an evaluation system (maximum score is 11 points) for lung tissue damage leading to complications. We tested the conventional forceps (186.8 kPa) and 3 novel spring forceps with the following thicknesses: 1.3 mm (53.0 kPa), 2.2 mm (187.7 kPa) and 2.8 mm (369.2 kPa). After grasping, peripheral canine lung tissues were microscopically examined for acute- and late-phase damages.

RESULTS

In the acute phase (20 sites), the novel forceps caused capillary congestion and haemorrhage in the subpleural tissue, whereas the conventional forceps caused deep tissue and pleural damages. In the late phase (30 sites), both forceps caused fibroblast formation and interstitial thickening, which progressed to the deeper tissues as grasping pressure increased. In the acute phase, the median scores were 2.0 and 6.0 for the novel and conventional forceps, respectively (P = 0.003). In the late phase, the median scores were 2.0, 2.5 and 5.0 for 1.3-, 2.2- and 2.8-mm thick forceps, respectively, and 5.0 for the conventional forceps (P < 0.001). In both phases, the novel forceps with grasping pressure set below 187.7 kPa (2.2 mm) caused significantly less lung tissue damage than the conventional forceps.

CONCLUSIONS

The novel endoscopic forceps are able to regulate the tissue-grasping pressure and induce less damage in lung tissues than conventional forceps.

Video content analysis of surgical procedures

BACKGROUND

In addition to its therapeutic benefits, minimally invasive surgery offers the potential for video recording of the operation. The videos may be archived and used later for reasons such as cognitive training, skills assessment, and workflow analysis. Methods from the major field of video content analysis and representation are increasingly applied in the surgical domain. In this paper, we review recent developments and analyze future directions in the field of content-based video analysis of surgical operations.

METHODS

The review was obtained from PubMed and Google Scholar search on combinations of the following keywords: 'surgery', 'video', 'phase', 'task', 'skills', 'event', 'shot', 'analysis', 'retrieval', 'detection', 'classification', and 'recognition'. The collected articles were categorized and reviewed based on the technical goal sought, type of surgery performed, and structure of the operation.

RESULTS

A total of 81 articles were included. The publication activity is constantly increasing; more than 50% of these articles were published in the last 3 years. Significant research has been performed for video task detection and retrieval in eye surgery. In endoscopic surgery, the research activity is more diverse: gesture/task classification, skills assessment, tool type recognition, shot/event detection and retrieval. Recent works employ deep neural networks for phase and tool recognition as well as shot detection.

CONCLUSIONS

Content-based video analysis of surgical operations is a rapidly expanding field. Several future prospects for research exist including, inter alia, shot boundary detection, keyframe extraction, video summarization, pattern discovery, and video annotation. The development of publicly available benchmark datasets to evaluate and compare task-specific algorithms is essential.

Video analysis in basic skills training: a way to expand the value and use of BlackBox training?

Background

Basic skills training in laparoscopic high-fidelity simulators (LHFS) improves laparoscopic skills. However, since LHFS are expensive, their availability is limited. The aim of this study was to assess whether automated video analysis of low-cost BlackBox laparoscopic training could provide an alternative to LHFS in basic skills training.

Methods

Medical students volunteered to participate during their surgical semester at the Karolinska University Hospital. After written informed consent, they performed two laparoscopic tasks (PEG-transfer and precision-cutting) on a BlackBox trainer. All tasks were videotaped and sent to MPLSC for automated video analysis, generating two parameters (Pl and Prtcl_tot) that assess the total motion activity. The students then carried out final tests on the MIST-VR simulator. This study was a European collaboration among two simulation centers, located in Sweden and Greece, within the framework of ACS-AEI.

Results

31 students (19 females and 12 males), mean age of 26.2 ± 0.8 years, participated in the study. However, since two of the students completed only one of the three MIST-VR tasks, they were excluded. The three MIST-VR scores showed significant positive correlations to both the Pl variable in the automated video analysis of the PEG-transfer (RSquare 0.48, P < 0.0001; 0.34, P = 0.0009; 0.45, P < 0.0001, respectively) as well as to the Prtcl_tot variable in that same exercise (RSquare 0.42, P = 0.0002; 0.29, P = 0.0024; 0.45, P < 0.0001). However, the correlations were exclusively shown in the group with less PC gaming experience as well as in the female group.

Conclusions

Automated video analysis provides accurate results in line with those of the validated MIST-VR. We believe that a more frequent use of automated video analysis could provide an extended value to cost-efficient laparoscopic BlackBox training. However, since there are gender-specific as well as PC gaming experience differences, this should be taken in account regarding the value of automated video analysis.

Automated robot-assisted surgical skill evaluation: Predictive analytics approach

BACKGROUND

Surgical skill assessment has predominantly been a subjective task. Recently, technological advances such as robot-assisted surgery have created great opportunities for objective surgical evaluation. In this paper, we introduce a predictive framework for objective skill assessment based on movement trajectory data. Our aim is to build a classification framework to automatically evaluate the performance of surgeons with different levels of expertise.

METHODS

Eight global movement features are extracted from movement trajectory data captured by a da Vinci robot for surgeons with two levels of expertise - novice and expert. Three classification methods - k-nearest neighbours, logistic regression and support vector machines - are applied.

RESULTS

The result shows that the proposed framework can classify surgeons' expertise as novice or expert with an accuracy of 82.3% for knot tying and 89.9% for a suturing task.

CONCLUSION

This study demonstrates and evaluates the ability of machine learning methods to automatically classify expert and novice surgeons using global movement features.

Modeling Touch and Palpation Using Autoregressive Models

OBJECTIVE

The human haptic system uses a set of reproducible and subconscious hand maneuvers to identify objects. Similar subconscious maneuvers are used during medical palpation for screening and diagnosis. The goal of this work was to develop a mathematical model that can be used to describe medical palpation techniques.

METHODS

Palpation data were measured using a two-dimensional array of force sensors. A novel algorithm for estimating the hand position from force data was developed. The hand position data were then modeled using multivariate autoregressive models. Analysis of these models provided palpation direction and frequency as well as palpation type. The models were tested and validated using three different data sets: simulated data, a simplified experiment in which participant followed a known pattern, and breast simulator palpation data.

RESULTS

Simulated data showed that the minimal error in estimating palpation direction and frequency is achieved when the sampling frequency is five to ten times the palpation frequency. The classification accuracy was for the simplified experiment and for the breast simulator data.

CONCLUSION

Proper palpation is one of the vital components of many hands-on clinical examinations. In this study, an algorithm for characterizing medical palpation was developed. The algorithm measured palpation frequency and direction for the first time and provided classification of palpation type.

SIGNIFICANCE

These newly developed models can be used for quantifying and assessing clinical technique, and consequently, lead to improved performance in palpation-based exams. Furthermore, they provide a general tool for the study of human haptics.

An Instrumented Glove to Assess Manual Dexterity in Simulation-Based Neurosurgical Education

The traditional neurosurgical apprenticeship scheme includes the assessment of trainee’s manual skills carried out by experienced surgeons. However, the introduction of surgical simulation technology presents a new paradigm where residents can refine surgical techniques on a simulator before putting them into practice in real patients. Unfortunately, in this new scheme, an experienced surgeon will not always be available to evaluate trainee’s performance. For this reason, it is necessary to develop automatic mechanisms to estimate metrics for assessing manual dexterity in a quantitative way. Authors have proposed some hardware-software approaches to evaluate manual dexterity on surgical simulators. This paper presents IGlove, a wearable device that uses inertial sensors embedded on an elastic glove to capture hand movements. Metrics to assess manual dexterity are estimated from sensors signals using data processing and information analysis algorithms. It has been designed to be used with a neurosurgical simulator called Daubara NS Trainer, but can be easily adapted to another benchtop- and manikin-based medical simulators. The system was tested with a sample of 14 volunteers who performed a test that was designed to simultaneously evaluate their fine motor skills and the IGlove’s functionalities. Metrics obtained by each of the participants are presented as results in this work; it is also shown how these metrics are used to automatically evaluate the level of manual dexterity of each volunteer.

Objective Assessment of Surgical Technical Skill and Competency in the Operating Room

Training skillful and competent surgeons is critical to ensure high quality of care and to minimize disparities in access to effective care. Traditional models to train surgeons are being challenged by rapid advances in technology, an intensified patient-safety culture, and a need for value-driven health systems. Simultaneously, technological developments are enabling capture and analysis of large amounts of complex surgical data. These developments are motivating a "surgical data science" approach to objective computer-aided technical skill evaluation (OCASE-T) for scalable, accurate assessment; individualized feedback; and automated coaching. We define the problem space for OCASE-T and summarize 45 publications representing recent research in this domain. We find that most studies on OCASE-T are simulation based; very few are in the operating room. The algorithms and validation methodologies used for OCASE-T are highly varied; there is no uniform consensus. Future research should emphasize competency assessment in the operating room, validation against patient outcomes, and effectiveness for surgical training.

Performance comparison of various feature detector-descriptors and temporal models for video-based assessment of laparoscopic skills

BACKGROUND

Despite the significant progress in hand gesture analysis for surgical skills assessment, video-based analysis has not received much attention. In this study we investigate the application of various feature detector-descriptors and temporal modeling techniques for laparoscopic skills assessment.

METHODS

Two different setups were designed: static and dynamic video-histogram analysis. Four well-known feature detection-extraction methods were investigated: SIFT, SURF, STAR-BRIEF and STIP-HOG. For the dynamic setup two temporal models were employed (LDS and GMMAR model). Each method was evaluated for its ability to classify experts and novices on peg transfer and knot tying.

RESULTS

STIP-HOG yielded the best performance (static: 74-79%; dynamic: 80-89%). Temporal models had equivalent performance. Important differences were found between the two groups with respect to the underlying dynamics of the video-histogram sequences.

CONCLUSIONS

Temporal modeling of feature histograms extracted from laparoscopic training videos provides information about the skill level and motion pattern of the operator. Copyright © 2015 John Wiley & Sons, Ltd.

The Effect of Mixed-Task Basic Training in the Acquisition of Advanced Laparoscopic Skills

The aim of this study was to assess whether mixed practice of basic tasks on a virtual reality (VR) simulator improves the performance of advanced tasks on the same device used for training as well as on a video trainer (VT). Thirty-six novices were allocated into 3 equal groups. Each group practiced on different combinations of basic tasks on a VR simulator: (A) peg transfer, (B) peg transfer and clipping, and (C) peg transfer, clipping, and cutting. Before and after training, each group performed a laparoscopic cholecystectomy (LC) scenario on the simulator and intracorporeal knot tying (KT) on a VT. Assessment metrics included time, instrument’s path length, penalty score, and hand motion synchronization. Results showed that for the common training tasks, plateau values were statistically equivalent for most assessment metrics ( P > .05). For LC, all groups showed significant performance improvement ( P < .05). For KT, group C improved significantly in pathlength ( P < .005), penalty score ( P < .05), and hand motion synchronization ( P < .05); the other groups failed to show an improvement ( P > .05). In conclusion, training on different VR tasks seems to have no effect on the performance of more demanding tasks on the same device. However, the number of different tasks practiced on the VR simulator seems to favorably affect the performance of advanced tasks on the VT.

Development of force-based metrics for skills assessment in minimally invasive surgery

BACKGROUND

The loss of haptic information that results from the reduced-access conditions present in minimally invasive surgery (MIS) may compromise the safety of the procedures. This limitation must be overcome through training. However, current methods for determining the skill level of trainees do not measure critical elements of skill attainment. This study aimed to evaluate the usefulness of force information for the assessment of skill during MIS.

METHODS

To achieve the study goal, experiments were performed using a set of sensorized instruments capable of measuring instrument position and tissue interaction forces. Several force-based metrics were developed as well as metrics that combine force and position information.

RESULTS

The results show that experience level has a strong correlation with the new force-based metrics presented in this article. In particular, the integral and the derivative of the forces or the metrics that combine force and position provide the strongest correlations.

CONCLUSIONS

This study showed that force-based metrics are better indications of performance than metrics based on task completion time or position information alone. The proposed metrics can be automatically computed, are completely objective, and measure important aspects of performance.

Does the incorporation of motion metrics into the existing FLS metrics lead to improved skill acquisition on simulators? A single blinded, randomized controlled trial

OBJECTIVE

We hypothesized that training to expert-derived levels of speed and motion will lead to improved learning and will translate to better operating room (OR) performance of novices than training to goals of speed or motion alone.

BACKGROUND

Motion tracking has been suggested to be a more sensitive performance metric than time and errors for the assessment of surgical performance.

METHODS

An institutional review board-approved, single blinded, randomized controlled trial was conducted at our level-I American College of Surgeons accredited Education Institute. Forty-two novices trained to proficiency in laparoscopic suturing after being randomized into 3 groups: The speed group (n = 14) had to achieve expert levels of speed, the motion group (n = 15) expert levels of motion (path length and smoothness), and the speed and motion group (n = 13) both levels. To achieve proficiency, all groups also had to demonstrate error-free performance. The FLS suture module (task 5) was used for training inside the ProMIS simulator that tracks instrument motion. All groups participated in transfer and retention tests in the OR. OR performance was assessed by a blinded expert rater using Global Operative Assessment of Laparoscopic Skills, speed, accuracy, and inadvertent injuries.

RESULTS

Thirty (71%) participants achieved proficiency and participated in the transfer and retention tests. The speed group achieved simulator proficiency significantly faster than the other groups (P < 0.001). With the exception of a higher injury rate during the transfer test for the speed group (that reversed during the retention test), there were no significant performance differences among the groups on all assessed parameters.

CONCLUSIONS

The incorporation of motion metrics into the time/accuracy goals of the FLS laparoscopic suturing curriculum had limited impact on participant skill transfer to the OR. Given the increased training requirements for such a curriculum, further study is needed before the addition of motion metrics to the current FLS metrics can be recommended.

An integrated approach to endoscopic instrument tracking for augmented reality applications in surgical simulation training

BACKGROUND

Despite the popular use of virtual and physical reality simulators in laparoscopic training, the educational potential of augmented reality (AR) has not received much attention. A major challenge is the robust tracking and three-dimensional (3D) pose estimation of the endoscopic instrument, which are essential for achieving interaction with the virtual world and for realistic rendering when the virtual scene is occluded by the instrument. In this paper we propose a method that addresses these issues, based solely on visual information obtained from the endoscopic camera.

METHODS

Two different tracking algorithms are combined for estimating the 3D pose of the surgical instrument with respect to the camera. The first tracker creates an adaptive model of a colour strip attached to the distal part of the tool (close to the tip). The second algorithm tracks the endoscopic shaft, using a combined Hough-Kalman approach. The 3D pose is estimated with perspective geometry, using appropriate measurements extracted by the two trackers.

RESULTS

The method has been validated on several complex image sequences for its tracking efficiency, pose estimation accuracy and applicability in AR-based training. Using a standard endoscopic camera, the absolute average error of the tip position was 2.5 mm for working distances commonly found in laparoscopic training. The average error of the instrument's angle with respect to the camera plane was approximately 2°. The results are also supplemented by video segments of laparoscopic training tasks performed in a physical and an AR environment.

CONCLUSIONS

The experiments yielded promising results regarding the potential of applying AR technologies for laparoscopic skills training, based on a computer vision framework. The issue of occlusion handling was adequately addressed. The estimated trajectory of the instruments may also be used for surgical gesture interpretation and assessment.