Development of a Wireless Sensor Glove for Surgical Skills Assessment

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.

Learning-Based Multimodal Information Fusion and Behavior Recognition of Vascular Interventionists' Operating Skills

The operating skills of vascular interventionists have an important impact on the effect of surgery. However, current research on behavior recognition and skills learning of interventionists' operating skills is limited. In this study, an innovative deep learning-based multimodal information fusion architecture is proposed for recognizing and analyzing eight common operating behaviors of interventionists. An experimental platform integrating four modal sensors is used to collect multimodal data from interventionists. The ANOVA and Manner-Whitney tests is used for relevance analysis of the data. The analysis results demonstrate that there is almost no significant difference ( p <0.001) between the actions related to the unimodal data, which cannot be used for accurate behavior recognition. Therefore, a study of the fusion architecture based on the existing machine learning classifier and the proposed deep learning fusion architecture is carried out. The research findings indicate that the proposed deep learning-based fusion architecture achieves an impressive overall accuracy of 98.5%, surpassing both the machine learning classifier (93.51%) and the unimodal data (90.05%). The deep learning-based multimodal information fusion architecture proves the feasibility of behavior recognition and skills learning of interventionist's operating skills. Furthermore, the application of deep learning-based multimodal fusion technology of surgeon's operating skills will help to improve the autonomy and intelligence of surgical robotic systems.

An Image Information-Based Objective Assessment Method of Technical Manipulation Skills for Intravascular Interventions

The clinical success of vascular interventional surgery relies heavily on a surgeon's catheter/guidewire manipulation skills and strategies. An objective and accurate assessment method plays a critical role in evaluating the surgeon's technical manipulation skill level. Most of the existing evaluation methods incorporate the use of information technology to find more objective assessment models based on various metrics. However, in these models, sensors are often attached to the surgeon's hands or to interventional devices for data collection, which constrains the surgeon's operational movements or exerts an influence on the motion trajectory of interventional devices. In this paper, an image information-based assessment method is proposed for the evaluation of the surgeon's manipulation skills without the requirement of attaching sensors to the surgeon or catheters/guidewires. Surgeons are allowed to use their natural bedside manipulation skills during the data collection process. Their manipulation features during different catheterization tasks are derived from the motion analysis of the catheter/guidewire in video sequences. Notably, data relating to the number of speed peaks, slope variations, and the number of collisions are included in the assessment. Furthermore, the contact forces, resulting from interactions between the catheter/guidewire and the vascular model, are sensed by a 6-DoF F/T sensor. A support vector machine (SVM) classification framework is developed to discriminate the surgeon's catheterization skill levels. The experimental results demonstrate that the proposed SVM-based assessment method can obtain an accuracy of 97.02% to distinguish between the expert and novice manipulations, which is higher than that of other existing research achievements. The proposed method has great potential to facilitate skill assessment and training of novice surgeons in vascular interventional surgery.

An update on the novel approaches towards skills assessment of ophthalmology residents in the Indian scenario

An essential part of the teaching-learning paradigm is assessment. It is one of the ways to achieve feedback for the various methods that have been used to impart a particular skill. This is true of ophthalmology training, where various clinical and surgical skills are learned as part of the residency program. In preparation for residents to become proficient ophthalmologists, both formative and summative assessments are of paramount importance. At present, assessment is primarily summative in the form of a university examination, including theory and practical examinations that are conducted at the end of the three years of residency. A formative assessment can make course corrections early on, allowing for an improved understanding of the subject and the acquisition of clinical and surgical skills. Formative assessments also allow us to customize the teaching methodology considering individual residents’ learning capabilities. In addition, formative assessments have the advantage of alleviating the stress of a “final” examination, which could sometimes result in a less-than-optimum performance by the residents. The COVID-19 pandemic has forced us to adopt new teaching methods, which has led to the adoption of changes in assessment. In this regard, we discuss the different assessment tools available, their pros and cons, and how best these tools can be made applicable in the setting of an ophthalmology residency program.

Machine learning for technical skill assessment in surgery: a systematic review

Accurate and objective performance assessment is essential for both trainees and certified surgeons. However, existing methods can be time consuming, labor intensive, and subject to bias. Machine learning (ML) has the potential to provide rapid, automated, and reproducible feedback without the need for expert reviewers. We aimed to systematically review the literature and determine the ML techniques used for technical surgical skill assessment and identify challenges and barriers in the field. A systematic literature search, in accordance with the PRISMA statement, was performed to identify studies detailing the use of ML for technical skill assessment in surgery. Of the 1896 studies that were retrieved, 66 studies were included. The most common ML methods used were Hidden Markov Models (HMM, 14/66), Support Vector Machines (SVM, 17/66), and Artificial Neural Networks (ANN, 17/66). 40/66 studies used kinematic data, 19/66 used video or image data, and 7/66 used both. Studies assessed the performance of benchtop tasks (48/66), simulator tasks (10/66), and real-life surgery (8/66). Accuracy rates of over 80% were achieved, although tasks and participants varied between studies. Barriers to progress in the field included a focus on basic tasks, lack of standardization between studies, and lack of datasets. ML has the potential to produce accurate and objective surgical skill assessment through the use of methods including HMM, SVM, and ANN. Future ML-based assessment tools should move beyond the assessment of basic tasks and towards real-life surgery and provide interpretable feedback with clinical value for the surgeon.PROSPERO: CRD42020226071.

Utilization of Flexible-Wearable Sensors to Describe the Kinematics of Surgical Proficiency

BACKGROUND

Traditional assessment (e.g., checklists, videotaping) for surgical proficiency may lead to subjectivity and does not predict performance in the clinical setting. Hand motion analysis is evolving as an objective tool for grading technical dexterity; however, most devices accompany with technical limitations or discomfort. We purpose the use of flexible wearable sensors to evaluate the kinematics of surgical proficiency.

METHODS

Surgeons were recruited and performed a vascular anastomosis task in a single institution. A modified objective structured assessment of technical skills (mOSATS) was used for technical qualification. Flexible wearable sensors (BioStamp RCTM, mc10 Inc., Lexington, MA) were placed on the dorsum of the dominant hand (DH) and nondominant hand (nDH) to measure kinematic parameters: path length (T_path), mean (V_mean) and peak (V_peak) velocity, number of hand movements (N_move), ratio of DH to nDH movements (rMov), and time of task (tTask) and further compared with the mOSATS score.

RESULTS

Participants were categorized as experts (n = 12) and novices (n = 8) based on a cutoff mean mOSATS score. Significant differences for tTask (P = 0.02), rMov (P = 0.07), DH T_path (P = 0.04), V_mean (P = 0.07), V_peak (P = 0.04), and nDH N_move (P = 0.02) were in favor of the experts. Overall, mOSATS had significant correlation with tTask (r = -0.69, P = 0.001), N_move of DH (r = -0.44, P = 0.047) and nDH (r = -0.66, P = 0.001), and rMov (r = 0.52, P = 0.017).

CONCLUSIONS

Hand motion analysis evaluated by flexible wearable sensors is feasible and informative. Experts utilize coordinated two-handed motion, whereas novices perform one-handed tasks in a hastily jerky manner. These tendencies create opportunity for improvement in surgical proficiency among trainees.

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.

Analysis of Interventionalists' Natural Behaviors for Recognizing Motion Patterns of Endovascular Tools During Percutaneous Coronary Interventions

Many robotic platforms can indeed reduce radiation exposure to clinicians during percutaneous coronary intervention (PCI), however, interventionalists' natural manipulations are rarely involved in robot-assisted PCI. This requires more attention to analyze interventionalists' natural behaviors during conventional PCI. In this study, four types of natural behavior (i.e., muscle activity, hand motion, proximal force, and finger motion) were synchronously acquired from ten subjects while performing six typical types of guidewire manipulation. These behaviors are evaluated by a hidden Markov model (HMM) based analysis framework for relevant behavior selection. Relevant behaviors are further used as the input of two HMM-based classification frameworks to recognize guidewire motion patterns. Experimental results show that under the basic classification framework (BCF), 91.01% and 93.32% recognition accuracies can be achieved by using all behaviors and relevant behaviors, respectively. Furthermore, the hierarchical classification framework can significantly enhance the recognition ability of relevant behaviors with an accuracy of 96.39%. These promising results demonstrate great potential of proposed methods for promoting the future design of human-robot interfaces in robot-assisted PCI.

An HMM-based recognition framework for endovascular manipulations

Robotic surgical systems are becoming increasingly popular for the treatment of cardiovascular diseases. However, most of them have been designed without considering techniques and skills of natural surgical manipulations, which are key factors to clinical success of percutaneous coronary intervention. This paper proposes an HMM-based framework to recognize six typical endovascular manipulations for surgical skill analysis. A simulative surgical platform is built for endovascular manipulations assessed by five subjects (1 expert and 4 novices). The performances of the proposed framework are evaluated by three experimental schemes with the optimal model parameters. The results show that endovascular manipulations are recognized with high accuracy and reliable performance. Furthermore, the acceptable results can also be applied to the design of next generation vascular interventional robots.

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.

Objective Surgical Skill Assessment: An Initial Experience by Means of a Sensory Glove Paving the Way to Open Surgery Simulation?

INTRODUCTION

Simulation and training in surgery are very promising tools for enhancing a surgeon's skill base. Accurate tracking of hand movements can be a strategy for objectively gauging a surgeon's dexterity, although "open" work is much more difficult to evaluate than are laparoscopic tasks. To the authors' knowledge, a system taking into account the movements of each finger joint has never been applied to open surgery simulation. This work intends to make up for this shortcoming and to perform a data analysis of the surgeon's entire gesture.

MATERIALS AND METHODS

The authors developed a sensory glove to measure flexion/extension of each finger joint and wrist movement. Totally 9 experts and 9 novices performed a basic suturing task and their manual performances were recorded within 2 days of measurements. Intraclass correlation coefficients were calculated to assess the ability of the executors to repeat and reproduce the proposed exercise. Wilcoxon signed-rank tests and Mann-Whitney U-tests were used to determine whether the 2 groups differ significantly in terms of execution time, repeatability, and reproducibility. Finally, a questionnaire was used to gather operators' subjective opinions.

RESULTS

The experts needed a similar reduced execution time comparing the 2 recording sessions (p = 0.09), whereas novices spent more time during the first day (p = 0.01). Repeatability did not differ between the 2 days, either for experts (p = 0.26) or for novices (p = 0.86). The 2 groups performed differently in terms of time (p < 0.001), repeatability (p = 0.01), and reproducibility (p < 0.001) of the same gesture. The system showed an overall moderate repeatability (intraclass correlation coefficient: experts = 0.64; novices = 0.53) and an overall high reproducibility. The questionnaire revealed performers' positive feedback with the glove.

CONCLUSIONS

This initial experience confirmed the validity and reliability of the proposed system in objectively assessing surgeons' technical skill, thus paving the way to a more complex project involving open surgery simulation.

Combination of wearable multi-biosensor platform and resonance frequency training for stress management of the unemployed population

Currently considerable research is being directed toward developing methodologies for controlling emotion or releasing stress. An applied branch of the basic field of psychophysiology, known as biofeedback, has been developed to fulfill clinical and non-clinical needs related to such control. Wearable medical devices have permitted unobtrusive monitoring of vital signs and emerging biofeedback services in a pervasive manner. With the global recession, unemployment has become one of the most serious social problems; therefore, the combination of biofeedback techniques with wearable technology for stress management of unemployed population is undoubtedly meaningful. This article describes a wearable biofeedback system based on combining integrated multi-biosensor platform with resonance frequency training (RFT) biofeedback strategy for stress management of unemployed population. Compared to commercial system, in situ experiments with multiple subjects indicated that our biofeedback system was discreet, easy to wear, and capable of offering ambulatory RFT biofeedback.Moreover, the comparative studies on the altered autonomic nervous system (ANS) modulation before and after three week RFT biofeedback training was performed in unemployed population with the aid of our wearable biofeedback system. The achieved results suggested that RFT biofeedback in combination with wearable technology was capable of significantly increasingoverall HRV, which indicated by decreasing sympathetic activities, increasing parasympathetic activities, and increasing ANS synchronization. After 3-week RFT-based respiration training, the ANS's regulating function and coping ability of unemployed population have doubled, and tended toward a dynamic balance.

The Contribution of Simulation Training in Enhancing Key Components of Laparoscopic Competence

This study aims to investigate how basic training contributes to the performance of complex laparoscopic tasks performed in a virtual reality (VR) environment. An assessment methodology is proposed based on quantitative error analysis of key components of laparoscopic competence. Twenty-five inexperienced surgeons were trained on four basic tasks. The effect of training was assessed on three independent scenarios (two procedural: adhesiolysis and bowel suturing, and a laparoscopic cholecystectomy [LC]). Several error parameters were post hoc analyzed to yield a quantitative performance index for two fundamental skills: proficiency and safety. Time and instrument path length were also measured and compared. Correlation analysis was performed to study how these indices correlate one another. Significant learning curves were demonstrated during training. For adhesiolysis, all four indices improved significantly ( P < 0.05). Time and path length presented plateaus for all basic tasks, whereas proficiency and safety only for two and one task(s), respectively. For bowel suturing, only time and safety errors showed a decrease ( P < 0.05). Significant performance enhancement was observed for LC in which errors and path length reduced after training ( P < 0.05). Our results revealed also an increased number of correlations after training, especially for proficiency. This study finds it possible to assess key competence skills based on the quantitative analysis of various parameters generated by a VR simulator. The improvement in basic training is transferred to more complex tasks. The proposed methodology is useful for structured evaluation of laparoscopic performance demonstrating fundamental elements of surgical competence.

A wearable respiratory biofeedback system based on generalized body sensor network

Wearable medical devices have enabled unobtrusive monitoring of vital signs and emerging biofeedback services in a pervasive manner. This article describes a wearable respiratory biofeedback system based on a generalized body sensor network (BSN) platform. The compact BSN platform was tailored for the strong requirements of overall system optimizations. A waist-worn biofeedback device was designed using the BSN. Extensive bench tests have shown that the generalized BSN worked as intended. In-situ experiments with 22 subjects indicated that the biofeedback device was discreet, easy to wear, and capable of offering wearable respiratory trainings. Pilot studies on wearable training patterns and resultant heart rate variability suggested that paced respirations at abdominal level and with identical inhaling/exhaling ratio were more appropriate for decreasing sympathetic arousal and increasing parasympathetic activities.