A comparison between a maximum care university hospital and an outpatient clinic - potential for optimization in arthroscopic workflows?

BACKGROUND

Due to the growing economic pressure, there is an increasing interest in the optimization of operational processes within surgical operating rooms (ORs). Surgical departments are frequently dealing with limited resources, complex processes with unexpected events as well as constantly changing conditions. In order to use available resources efficiently, existing workflows and processes have to be analyzed and optimized continuously. Structural and procedural changes without prior data-driven analyses may impair the performance of the OR team and the overall efficiency of the department. The aim of this study is to develop an adaptable software toolset for surgical workflow analysis and perioperative process optimization in arthroscopic surgery.

METHODS

In this study, the perioperative processes of arthroscopic interventions have been recorded and analyzed subsequently. A total of 53 arthroscopic operations were recorded at a maximum care university hospital (UH) and 66 arthroscopic operations were acquired at a special outpatient clinic (OC). The recording includes regular perioperative processes (i.a. patient positioning, skin incision, application of wound dressing) and disruptive influences on these processes (e.g. telephone calls, missing or defective instruments, etc.). For this purpose, a software tool was developed ('s.w.an Suite Arthroscopic toolset'). Based on the data obtained, the processes of the maximum care provider and the special outpatient clinic have been analyzed in terms of performance measures (e.g. Closure-To-Incision-Time), efficiency (e.g. activity duration, OR resource utilization) as well as intra-process disturbances and then compared to one another.

RESULTS

Despite many similar processes, the results revealed considerable differences in performance indices. The OC required significantly less time than UH for surgical preoperative (UH: 30:47 min, OC: 26:01 min) and postoperative phase (UH: 15:04 min, OC: 9:56 min) as well as changeover time (UH: 32:33 min, OC: 6:02 min). In addition, these phases result in the Closure-to-Incision-Time, which lasted longer at the UH (UH: 80:01 min, OC: 41:12 min).

CONCLUSION

The perioperative process organization, team collaboration, and the avoidance of disruptive factors had a considerable influence on the progress of the surgeries. Furthermore, differences in terms of staffing and spatial capacities could be identified. Based on the acquired process data (such as the duration for different surgical steps or the number of interfering events) and the comparison of different arthroscopic departments, approaches for perioperative process optimization to decrease the time of work steps and reduce disruptive influences were identified.

Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark

PURPOSE

Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multicenter setting including more difficult recognition tasks such as surgical action and surgical skill.

METHODS

To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 h was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment.

RESULTS

F1-scores were achieved for phase recognition between 23.9% and 67.7% (n = 9 teams), for instrument presence detection between 38.5% and 63.8% (n = 8 teams), but for action recognition only between 21.8% and 23.3% (n = 5 teams). The average absolute error for skill assessment was 0.78 (n = 1 team).

CONCLUSION

Surgical workflow and skill analysis are promising technologies to support the surgical team, but there is still room for improvement, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost importance to create more open, high-quality datasets in order to allow the development of artificial intelligence and cognitive robotics in surgery.

Anticipation for surgical workflow through instrument interaction and recognized Signals

Surgical workflow anticipation is an essential task for computer-assisted intervention (CAI) systems. It aims at predicting the future surgical phase and instrument occurrence, providing support for intra-operative decision-support system. Recent studies have promoted the development of the anticipation task by transforming it into a remaining time prediction problem, but without factoring the surgical instruments' behaviors and their interactions with surrounding anatomies in the network design. In this paper, we propose an Instrument Interaction Aware Anticipation Network (IIA-Net) to overcome the previous deficiency while retaining the merits of two-stage models through using spatial feature extractor and temporal model. Spatially, feature extractor utilizes tooltips' movement to extracts the instrument-instrument interaction, which helps model concentrate on the surgeon's actions. On the other hand, it introduces the segmentation map to capture the rich instrument-surrounding features about the instrument surroundings. Temporally, the temporal model applies the causal dilated multi-stage temporal convolutional network to capture the long-term dependency in the long and untrimmed surgical videos with a large receptive field. Our IIA-Net enforces an online inference with reliable predictions even with severe noise and artifacts in the recorded videos and presence signals. Extensive experiments on Cholec80 dataset demonstrate the performance of our proposed method exceeds the state-of-the-art method by a large margin (1.03 v.s. 1.12 for MAE_w, 1.40 v.s. 1.75 for MAE_in and 2.14 v.s. 2.68 for MAE_e). For reproduction purposes, all the original codes are made public at https://github.com/Flaick/Surgical-Workflow-Anticipation.

Multi-task temporal convolutional networks for joint recognition of surgical phases and steps in gastric bypass procedures

PURPOSE

Automatic segmentation and classification of surgical activity is crucial for providing advanced support in computer-assisted interventions and autonomous functionalities in robot-assisted surgeries. Prior works have focused on recognizing either coarse activities, such as phases, or fine-grained activities, such as gestures. This work aims at jointly recognizing two complementary levels of granularity directly from videos, namely phases and steps.

METHODS

We introduce two correlated surgical activities, phases and steps, for the laparoscopic gastric bypass procedure. We propose a multi-task multi-stage temporal convolutional network (MTMS-TCN) along with a multi-task convolutional neural network (CNN) training setup to jointly predict the phases and steps and benefit from their complementarity to better evaluate the execution of the procedure. We evaluate the proposed method on a large video dataset consisting of 40 surgical procedures (Bypass40).

RESULTS

We present experimental results from several baseline models for both phase and step recognition on the Bypass40. The proposed MTMS-TCN method outperforms single-task methods in both phase and step recognition by 1-2% in accuracy, precision and recall. Furthermore, for step recognition, MTMS-TCN achieves a superior performance of 3-6% compared to LSTM-based models on all metrics.

CONCLUSION

In this work, we present a multi-task multi-stage temporal convolutional network for surgical activity recognition, which shows improved results compared to single-task models on a gastric bypass dataset with multi-level annotations. The proposed method shows that the joint modeling of phases and steps is beneficial to improve the overall recognition of each type of activity.

Active learning using deep Bayesian networks for surgical workflow analysis

PURPOSE

For many applications in the field of computer-assisted surgery, such as providing the position of a tumor, specifying the most probable tool required next by the surgeon or determining the remaining duration of surgery, methods for surgical workflow analysis are a prerequisite. Often machine learning-based approaches serve as basis for analyzing the surgical workflow. In general, machine learning algorithms, such as convolutional neural networks (CNN), require large amounts of labeled data. While data is often available in abundance, many tasks in surgical workflow analysis need annotations by domain experts, making it difficult to obtain a sufficient amount of annotations.

METHODS

The aim of using active learning to train a machine learning model is to reduce the annotation effort. Active learning methods determine which unlabeled data points would provide the most information according to some metric, such as prediction uncertainty. Experts will then be asked to only annotate these data points. The model is then retrained with the new data and used to select further data for annotation. Recently, active learning has been applied to CNN by means of deep Bayesian networks (DBN). These networks make it possible to assign uncertainties to predictions. In this paper, we present a DBN-based active learning approach adapted for image-based surgical workflow analysis task. Furthermore, by using a recurrent architecture, we extend this network to video-based surgical workflow analysis. To decide which data points should be labeled next, we explore and compare different metrics for expressing uncertainty.

RESULTS

We evaluate these approaches and compare different metrics on the Cholec80 dataset by performing instrument presence detection and surgical phase segmentation. Here we are able to show that using a DBN-based active learning approach for selecting what data points to annotate next can significantly outperform a baseline based on randomly selecting data points. In particular, metrics such as entropy and variation ratio perform consistently on the different tasks.

CONCLUSION

We show that using DBN-based active learning strategies make it possible to selectively annotate data, thereby reducing the required amount of labeled training in surgical workflow-related tasks.

Weakly supervised convolutional LSTM approach for tool tracking in laparoscopic videos

PURPOSE

Real-time surgical tool tracking is a core component of the future intelligent operating room (OR), because it is highly instrumental to analyze and understand the surgical activities. Current methods for surgical tool tracking in videos need to be trained on data in which the spatial positions of the tools are manually annotated. Generating such training data is difficult and time-consuming. Instead, we propose to use solely binary presence annotations to train a tool tracker for laparoscopic videos.

METHODS

The proposed approach is composed of a CNN + Convolutional LSTM (ConvLSTM) neural network trained end to end, but weakly supervised on tool binary presence labels only. We use the ConvLSTM to model the temporal dependencies in the motion of the surgical tools and leverage its spatiotemporal ability to smooth the class peak activations in the localization heat maps (Lh-maps).

RESULTS

We build a baseline tracker on top of the CNN model and demonstrate that our approach based on the ConvLSTM outperforms the baseline in tool presence detection, spatial localization, and motion tracking by over [Formula: see text], [Formula: see text], and [Formula: see text], respectively.

CONCLUSIONS

In this paper, we demonstrate that binary presence labels are sufficient for training a deep learning tracking model using our proposed method. We also show that the ConvLSTM can leverage the spatiotemporal coherence of consecutive image frames across a surgical video to improve tool presence detection, spatial localization, and motion tracking.

Comparative analysis of surgical processes for image-guided endoscopic sinus surgery

PURPOSE

This study proposes a method to analyze surgical performance by modeling, aligning, and comparing surgical processes. This method is intended to serve as a means to support the enhancement of surgical skills for endoscopic sinus surgeries (ESSs). We focus on surgical navigation systems used in image-guided ESSs and aim to construct a comparative analysis method for surgical processes based on the information about the surgical instruments motion obtained from the navigation system.

METHODS

The proposed method consists of the following three parts: quantification of surgical features, modeling of surgical processes, and alignment and comparison of surgical process models (SPMs). First, we defined time-series parameters using the navigation-based surgical data. Second, we created SPMs by applying the defined parameters and the relative positional information of the instruments to the patient's anatomy. Third, we constructed a method to align and compare SPMs based on dynamic time warping with barycenter averaging.

RESULTS

The proposed method was validated on a dataset containing surgical data obtained by an optical tracking system from 14 clinical ESS cases. We evaluated the validity of the comparative analysis by aligning and comparing SPMs between experts and residents. The validation results suggested that the proposed method could achieve proper alignment of the SPMs and clarify the differences in surgical processes between experts and residents.

CONCLUSION

We developed a method to enable a time-series comparative analysis of surgical processes based on the surgical data from the navigation system. This method can allow surgeons to identify differences between their procedures and reference procedures such as experts' procedures.

System events: readily accessible features for surgical phase detection

PURPOSE

Surgical phase recognition using sensor data is challenging due to high variation in patient anatomy and surgeon-specific operating styles. Segmenting surgical procedures into constituent phases is of significant utility for resident training, education, self-review, and context-aware operating room technologies. Phase annotation is a highly labor-intensive task and would benefit greatly from automated solutions.

METHODS

We propose a novel approach using system events-for example, activation of cautery tools-that are easily captured in most surgical procedures. Our method involves extracting event-based features over 90-s intervals and assigning a phase label to each interval. We explore three classification techniques: support vector machines, random forests, and temporal convolution neural networks. Each of these models independently predicts a label for each time interval. We also examine segmental inference using an approach based on the semi-Markov conditional random field, which jointly performs phase segmentation and classification. Our method is evaluated on a data set of 24 robot-assisted hysterectomy procedures.

RESULTS

Our framework is able to detect surgical phases with an accuracy of 74 % using event-based features over a set of five different phases-ligation, dissection, colpotomy, cuff closure, and background. Precision and recall values for the cuff closure (Precision: 83 %, Recall: 98 %) and dissection (Precision: 75 %, Recall: 88 %) classes were higher than other classes. The normalized Levenshtein distance between predicted and ground truth phase sequence was 25 %.

CONCLUSIONS

Our findings demonstrate that system events features are useful for automatically detecting surgical phase. Events contain phase information that cannot be obtained from motion data and that would require advanced computer vision algorithms to extract from a video. Many of these events are not specific to robotic surgery and can easily be recorded in non-robotic surgical modalities. In future work, we plan to combine information from system events, tool motion, and videos to automate phase detection in surgical procedures.