Distance-based time series classification approach for task recognition with application in surgical robot autonomy

Convolutional-de-convolutional neural networks for recognition of surgical workflow

Computer-assisted surgery (CAS) has occupied an important position in modern surgery, further stimulating the progress of methodology and technology. In recent years, a large number of computer vision-based methods have been widely used in surgical workflow recognition tasks. For training the models, a lot of annotated data are necessary. However, the annotation of surgical data requires expert knowledge and thus becomes difficult and time-consuming. In this paper, we focus on the problem of data deficiency and propose a knowledge transfer learning method based on artificial neural network to compensate a small amount of labeled training data. To solve this problem, we propose an unsupervised method for pre-training a Convolutional-De-Convolutional (CDC) neural network for sequencing surgical workflow frames, which performs neural convolution in space (for semantic abstraction) and neural de-convolution in time (for frame level resolution) simultaneously. Specifically, through neural convolution transfer learning, we only fine-tuned the CDC neural network to classify the surgical phase. We performed some experiments for validating the model, and it showed that the proposed model can effectively extract the surgical feature and determine the surgical phase. The accuracy (Acc), recall, precision (Pres) of our model reached 91.4, 78.9, and 82.5%, respectively.

A Multilayer and Multimodal-Fusion Architecture for Simultaneous Recognition of Endovascular Manipulations and Assessment of Technical Skills

The clinical success of the percutaneous coronary intervention (PCI) is highly dependent on endovascular manipulation skills and dexterous manipulation strategies of interventionalists. However, the analysis of endovascular manipulations and related discussion for technical skill assessment are limited. In this study, a multilayer and multimodal-fusion architecture is proposed to recognize six typical endovascular manipulations. The synchronously acquired multimodal motion signals from ten subjects are used as the inputs of the architecture independently. Six classification-based and two rule-based fusion algorithms are evaluated for performance comparisons. The recognition metrics under the determined architecture are further used to assess technical skills. The experimental results indicate that the proposed architecture can achieve the overall accuracy of 96.41%, much higher than that of a single-layer recognition architecture (92.85%). In addition, the multimodal fusion brings significant performance improvement in comparison with single-modal schemes. Furthermore, the K -means-based skill assessment can obtain an accuracy of 95% to cluster the attempts made by different skill-level groups. These hopeful results indicate the great possibility of the architecture to facilitate clinical skill assessment and skill learning.

Evaluating robotic-assisted surgery training videos with multi-task convolutional neural networks

We seek to understand if an automated algorithm can replace human scoring of surgical trainees performing the urethrovesical anastomosis in radical prostatectomy with synthetic tissue. Specifically, we investigate neural networks for predicting the surgical proficiency score (GEARS score) from video clips. We evaluate videos of surgeons performing the urethral anastomosis using synthetic tissue. The algorithm tracks surgical instrument locations from video, saving the positions of key points on the instruments over time. These positional features are used to train a multi-task convolutional network to infer each sub-category of the GEARS score to determine the proficiency level of trainees. Experimental results demonstrate that the proposed method achieves good performance with scores matching manual inspection in 86.1% of all GEARS sub-categories. Furthermore, the model can detect the difference between proficiency (novice to expert) in 83.3% of videos. Evaluation of GEARS sub-categories with artificial neural networks is possible for novice and intermediate surgeons, but additional research is needed to understand if expert surgeons can be evaluated with a similar automated system.

Robotic totally endoscopic coronary artery bypass grafting: current status and future prospects

Introduction: Totally endoscopic coronary artery bypass grafting (TECAB) can only be performed in a reproducible manner using robotic technology. This operation has been developed for more than 20 years seeing three generations of surgical robots. TECAB can be carried out beating heart but also on the arrested heart. Single and multiple grafts can be placed and TECAB can be combined with percutaneous coronary intervention in hybrid procedures.Areas covered: This review outlines indications for the procedure, the surgical technique, and the postoperative care. Intra- and postoperative results as available in the literature are reported. Further areas focus on technological development, training methods, learning curves as well as on cost. Finally, we give an outlook on the potential future of this operation.Expert opinion: Robotic TECAB represents a complex, sophisticated but safe, and over-the-years grown procedure. Even though results seem to be in line with conventional coronary surgery worldwide adoption still has been slow probably due to procedure times, costs and learning curves. Main advantages of TECAB are minimized surgical trauma and subsequent reduction of postoperative healing time. With the current introduction of new robotic devices, a new era of procedure development is on its way.

A Robotic Recording and Playback Platform for Training Surgeons and Learning Autonomous Behaviors Using the da Vinci Surgical System

This paper describes a recording and playback system developed using a da Vinci Standard Surgical System and research kit. The system records stereo laparoscopic videos, robot arm joint angles, and surgeon–console interactions in a synchronized manner. A user can then, on-demand and at adjustable speeds, watch stereo videos and feel recorded movements on the hand controllers of entire procedures or sub procedures. Currently, there is no reported comprehensive ability to capture expert surgeon movements and insights and reproduce them on hardware directly. This system has important applications in several areas: (1) training of surgeons, (2) collection of learning data for the development of advanced control algorithms and intelligent autonomous behaviors, and (3) use as a “black box” for retrospective error analysis. We show a prototype of such an immersive system on a clinically-relevant platform along with its recording and playback fidelity. Lastly, we convey possible research avenues to create better systems for training and assisting robotic surgeons.

SATR-DL: Improving Surgical Skill Assessment And Task Recognition In Robot-Assisted Surgery With Deep Neural Networks

PURPOSE

This paper focuses on an automated analysis of surgical motion profiles for objective skill assessment and task recognition in robot-assisted surgery. Existing techniques heavily rely on conventional statistic measures or shallow modelings based on hand-engineered features and gesture segmentation. Such developments require significant expert knowledge, are prone to errors, and are less efficient in online adaptive training systems.

METHODS

In this work, we present an efficient analytic framework with a parallel deep learning architecture, SATR-DL, to assess trainee expertise and recognize surgical training activity. Through an end-to-end learning technique, abstract information of spatial representations and temporal dynamics is jointly obtained directly from raw motion sequences.

RESULTS

By leveraging a shared highlevel representation learning, the resulting model is successful in the recognition of trainee skills and surgical tasks, suturing, needle-passing, and knot-tying. Meanwhile, we explore the use of ensemble in classification at the trial level, where the SATR-DL outperforms state-of-the-art performance by achieving accuracies of 0.960 and 1.000 in skill assessment and task recognition, respectively.

CONCLUSION

This study highlights the potential of SATR-DL to provide improvements for an efficient data-driven assessment in intelligent robotic surgery.

Surgical motion analysis using discriminative interpretable patterns

OBJECTIVE

The analysis of surgical motion has received a growing interest with the development of devices allowing their automatic capture. In this context, the use of advanced surgical training systems makes an automated assessment of surgical trainee possible. Automatic and quantitative evaluation of surgical skills is a very important step in improving surgical patient care.

MATERIAL AND METHOD

In this paper, we present an approach for the discovery and ranking of discriminative and interpretable patterns of surgical practice from recordings of surgical motions. A pattern is defined as a series of actions or events in the kinematic data that together are distinctive of a specific gesture or skill level. Our approach is based on the decomposition of continuous kinematic data into a set of overlapping gestures represented by strings (bag of words) for which we compute comparative numerical statistic (tf-idf) enabling the discriminative gesture discovery via its relative occurrence frequency.

RESULTS

We carried out experiments on three surgical motion datasets. The results show that the patterns identified by the proposed method can be used to accurately classify individual gestures, skill levels and surgical interfaces. We also present how the patterns provide a detailed feedback on the trainee skill assessment.

CONCLUSIONS

The proposed approach is an interesting addition to existing learning tools for surgery as it provides a way to obtain a feedback on which parts of an exercise have been used to classify the attempt as correct or incorrect.