Search

Due to the complex nonstationarity of ground motion in time–frequency domain, the traditional methods of comparing and evaluating earthquake waveforms have not enough ability and accuracy to distinguish the details and changing features of the similar waves, which makes the similarity evaluation of waveform is difficult to be quantified accurately. The similarity degree of different signals can be calculated precisely according to dynamic time warping (DTW) algorithm, so it can be used for waveform comparison and similarity evaluation. In order to improve the traditional method, a method based on DTW distance is proposed to identify the earthquake waveform and analyze the ground motion characteristics. Based on the statistical analysis of a great quantity of earthquake waves, the changes law of DTW distance considering amplitude, time lag, noise signal ratio, site type, and the comprehensive effect is obtained. DTW distance is proved to be used as a compatible evaluation standard for waveform refinement. It is verified that DTW distance and vector norm are essentially equivalent. In the analysis of ground motion, DTW distance is implicated in the equivalent amplitude and energy of earthquake waves. The physical connotation of DTW distance is demonstrated by analyzing the data of the station array, and the results show that the distribution of DTW distance can accurately imply the time–space variation effect of the earthquake in the region. The reasonable reference range of DTW distance is defined by statistical method, and the corresponding evaluation standard of synthetic multi-point ground motion with real characteristics is proposed. In the synthetic accuracy evaluation of artificial ground motion with spatial variation effect, the combination of ground motions with more real characteristics can be obtained by evaluating and optimizing the waveforms according to the variation rule and range of DTW distance. [ABSTRACT FROM AUTHOR]

Purpose: Integrated operating rooms provide rich sources of temporal information about surgical procedures, which has led to the emergence of surgical data science. However, little emphasis has been put on interactive visualization of such temporal datasets to gain further insights. Our goal is to put heterogeneous data sequences in relation to better understand the workflows of individual procedures as well as selected subsets, e.g., with respect to different surgical phase distributions and surgical instrument usage patterns. Methods: We developed a reusable web-based application design to analyze data derived from surgical procedure recordings. It consists of aggregated, synchronized visualizations for the original temporal data as well as for derived information, and includes tailored interaction techniques for selection and filtering. To enable reproducibility, we evaluated it across four types of surgeries from two openly available datasets (HeiCo and Cholec80). User evaluation has been conducted with twelve students and practitioners with surgical and technical background. Results: The evaluation showed that the application has the complexity of an expert tool (System Usability Score of 57.73) but allowed the participants to solve various analysis tasks correctly (78.8% on average) and to come up with novel hypotheses regarding the data. Conclusion: The novel application supports postoperative expert-driven analysis, improving the understanding of surgical workflows and the underlying datasets. It facilitates analysis across multiple synchronized views representing information from different data sources and, thereby, advances the field of surgical data science. [ABSTRACT FROM AUTHOR]

Automatic surgical workflow analysis (SWA) plays an important role in the modelling of surgical processes. Current automatic approaches for SWA use videos (with accuracies varying from 0.8 and 0.9), but they do not incorporate speech (inherently linked to the ongoing cognitive process). The approach followed in this study uses both video and speech to classify the phases of laparoscopic cholecystectomy, based on neural networks and machine learning. The automatic application implemented in this study uses this information to calculate the total time spent in surgery, the time spent in each phase, the number of occurrences, the minimal, maximal and average time whenever there is more than one occurrence, the timeline of the surgery and the transition probability between phases. This information can be used as an assessment method for surgical procedural skills. [ABSTRACT FROM AUTHOR]

The main forms of gas kicks into the wellbore during drilling in fractured carbonate reservoirs are underbalanced pressure and gravity displacement. These two forms of gas kicks have different mechanisms of gas entry into the wellbore and different well control measures, which require the timely identification of the type of gas kick when it occurs. A two-phase flow model with a wellbore-formation coupling was developed, based on the gas kick rate models. The variation characteristics of the bottomhole gas influx rate, the wellbore free gas, the bottomhole pressure, the bottomhole pressure change rate, the pit gain and the outlet flow rate during an underbalanced pressure gas kick and a gravity displacement gas kick, were compared and analyzed. Combining the dynamic time warping (DTW) and the wellbore-formation, coupled with a two-phase flow model, an identification method of the gas kick type, based on the DTW was proposed. Following the detection of the gas kick, the identification is performed by calculating the DTW distance between the surface parameter time series, obtained from the two-phase flow simulation and the surface parameter time series, measured in real time. The field example results show that the method can identify the type of gas kick, based on the real-time surface measurement parameters and provide a basis for taking targeted well control measures. [ABSTRACT FROM AUTHOR]

As a rich clean and environmentally friendly renewable resources, wind energy has emerged as a strategic choice for countries around the world. Because the wind turbines often operate in severe working conditions such as variable load and large temperature difference they are prone to failures and possible shutdowns. The shutdowns however seriously affect the economic benefits of the wind turbines. Initiative maintenance has become a worldwide recognized scientific method for planning and determining preventive maintenance work, the implementation of this strategy relies on real-time condition monitoring and fault signal identification methods. The condition monitoring of wind turbine can help master the health state and power generation performance of wind turbine, so as to timely formulate maintenance strategies and adopt technical modification measures to improve power generation performance, reduce the down time of wind turbine, avoid the occurrence of major faults, save maintenance cost and improve power generation capacity. Therefore, a condition monitoring system is built on a wind turbine of Zhangjiakou, and a systematic signal analysis method is proposed, time-domain synchronous averaging technology, based on variable period, impulse signal feature extraction technology based on Teager and signal decomposition technology based on CEEMD. The proposed method realizes the signal analysis and feature extraction of non-stationary nonlinear, weak signal and frequency aliasing signals, and successfully diagnose the gearbox secondary meshing failure during the long-term monitoring. This confirms that the monitoring system methods and signal analysis technology proposed in this paper can effectively realize the condition monitoring and fault diagnosis of wind turbines. [ABSTRACT FROM AUTHOR]

Purpose: Microsurgical techniques require highly skilled manual handling of specialized surgical instruments. Surgical process models are central for objective evaluation of these skills, enabling data-driven solutions that can improve intraoperative efficiency. Method: We built a surgical process model, defined at movement level in terms of elementary surgical actions ( n = 4 ) and targets ( n = 4 ). The model also included nonproductive movements, which enabled us to evaluate suturing efficiency and bi-manual dexterity. The elementary activities were used to investigate differences between novice ( n = 5 ) and expert surgeons ( n = 5 ) by comparing the cosine similarity of vector representations of a microsurgical suturing training task and its different segments. Results: Based on our model, the experts were significantly more efficient than the novices at using their tools individually and simultaneously. At suture level, the experts were significantly more efficient at using their left hand tool, but the differences were not significant for the right hand tool. At the level of individual suture segments, the experts had on average 21.0 % higher suturing efficiency and 48.2 % higher bi-manual efficiency, and the results varied between segments. Similarity of the manual actions showed that expert and novice surgeons could be distinguished by their movement patterns. Conclusions: The surgical process model allowed us to identify differences between novices' and experts' movements and to evaluate their uni- and bi-manual tool use efficiency. Analyzing surgical tasks in this manner could be used to evaluate surgical skill and help surgical trainees detect problems in their performance computationally. [ABSTRACT FROM AUTHOR]

COVID-19-associated respiratory failure offers the unprecedented opportunity to evaluate the differential host response to a uniform pathogenic insult. Understanding whether there are distinct subphenotypes of severe COVID-19 may offer insight into its pathophysiology. Sequential Organ Failure Assessment (SOFA) score is an objective and comprehensive measurement that measures dysfunction severity of six organ systems, i.e., cardiovascular, central nervous system, coagulation, liver, renal, and respiration. Our aim was to identify and characterize distinct subphenotypes of COVID-19 critical illness defined by the post-intubation trajectory of SOFA score. Intubated COVID-19 patients at two hospitals in New York city were leveraged as development and validation cohorts. Patients were grouped into mild, intermediate, and severe strata by their baseline post-intubation SOFA. Hierarchical agglomerative clustering was performed within each stratum to detect subphenotypes based on similarities amongst SOFA score trajectories evaluated by Dynamic Time Warping. Distinct worsening and recovering subphenotypes were identified within each stratum, which had distinct 7-day post-intubation SOFA progression trends. Patients in the worsening suphenotypes had a higher mortality than those in the recovering subphenotypes within each stratum (mild stratum, 29.7% vs. 10.3%, p = 0.033; intermediate stratum, 29.3% vs. 8.0%, p = 0.002; severe stratum, 53.7% vs. 22.2%, p < 0.001). Pathophysiologic biomarkers associated with progression were distinct at each stratum, including findings suggestive of inflammation in low baseline severity of illness versus hemophagocytic lymphohistiocytosis in higher baseline severity of illness. The findings suggest that there are clear worsening and recovering subphenotypes of COVID-19 respiratory failure after intubation, which are more predictive of outcomes than baseline severity of illness. Distinct progression biomarkers at differential baseline severity of illness suggests a heterogeneous pathobiology in the progression of COVID-19 respiratory failure. [ABSTRACT FROM AUTHOR]

Objective: Currently, there is a worldwide shift toward competency-based medical education. This necessitates the use of automated skills assessment methods during self-guided interventions training. Making assessment methods that are transparent and configurable will allow assessment to be interpreted into instructional feedback. The purpose of this work is to develop and validate skills assessment methods in ultrasound-guided interventions that are transparent and configurable. Methods: We implemented a method based upon decision trees and a method based upon fuzzy inference systems for technical skills assessment. Subsequently, we validated these methods for their ability to predict scores of operators on a 25-point global rating scale in ultrasound-guided needle insertions and their ability to provide useful feedback for training. Results: Decision tree and fuzzy rule-based assessment performed comparably to state-of-the-art assessment methods. They produced median errors (on a 25-point scale) of 1.7 and 1.8 for in-plane insertions and 1.5 and 3.0 for out-of-plane insertions, respectively. In addition, these methods provided feedback that was useful for trainee learning. Decision tree assessment produced feedback with median usefulness 7 out of 7; fuzzy rule-based assessment produced feedback with median usefulness 6 out of 7. Conclusion: Transparent and configurable assessment methods are comparable to the state of the art and, in addition, can provide useful feedback. This demonstrates their value in self-guided interventions training curricula. [ABSTRACT FROM AUTHOR]

Purpose: The development of common ontologies has recently been identified as one of the key challenges in the emerging field of surgical data science (SDS). However, past and existing initiatives in the domain of surgery have mainly been focussing on individual groups and failed to achieve widespread international acceptance by the research community. To address this challenge, the authors of this paper launched a European initiative—OntoSPM Collaborative Action—with the goal of establishing a framework for joint development of ontologies in the field of SDS. This manuscript summarizes the goals and the current status of the international initiative.Methods: A workshop was organized in 2016, gathering the main European research groups having experience in developing and using ontologies in this domain. It led to the conclusion that a common ontology for surgical process models (SPM) was absolutely needed, and that the existing OntoSPM ontology could provide a good starting point toward the collaborative design and promotion of common, standard ontologies on SPM.Results: The workshop led to the OntoSPM Collaborative Action—launched in mid-2016—with the objective to develop, maintain and promote the use of common ontologies of SPM relevant to the whole domain of SDS. The fundamental concept, the architecture, the management and curation of the common ontology have been established, making it ready for wider public use.Conclusion: The OntoSPM Collaborative Action has been in operation for 24 months, with a growing dedicated membership. Its main result is a modular ontology, undergoing constant updates and extensions, based on the experts’ suggestions. It remains an open collaborative action, which always welcomes new contributors and applications. [ABSTRACT FROM AUTHOR]

Purpose: Surgical processes are generally only studied by identifying differences in populations such as participants or level of expertise. But the similarity between this population is also important in understanding the process. We therefore proposed to study these two aspects.Methods: In this article, we show how similarities in process workflow within a population can be identified as sequential surgical signatures. To this purpose, we have proposed a pattern mining approach to identify these signatures.Validation: We validated our method with a data set composed of seventeen micro-surgical suturing tasks performed by four participants with two levels of expertise.Results: We identified sequential surgical signatures specific to each participant, shared between participants with and without the same level of expertise. These signatures are also able to perfectly define the level of expertise of the participant who performed a new micro-surgical suturing task. However, it is more complicated to determine who the participant is, and the method correctly determines this information in only 64% of cases.Conclusion: We show for the first time the concept of sequential surgical signature. This new concept has the potential to further help to understand surgical procedures and provide useful knowledge to define future CAS systems. [ABSTRACT FROM AUTHOR]

Purpose: Lack of annotated training data hinders automatic recognition and prediction of surgical activities necessary for situation-aware operating rooms. We propose using knowledge transfer to compensate for data deficit and improve prediction.Methods: We used two approaches to extract and transfer surgical process knowledge. First, we encoded semantic information about surgical terms using word embedding. Secondly, we passed knowledge between different clinical datasets of neurosurgical procedures using transfer learning.Results: The combination of two methods provided 22% improvement of activity prediction. We also made several pertinent observations about surgical practices based on the results of the performed transfer.Conclusion: Word embedding boosts learning process. Transfer learning was shown to be more effective than a simple combination of data, especially for less similar procedures. [ABSTRACT FROM AUTHOR]

Purpose: Surgery is one of the riskiest and most important medical acts that are performed today. The need to improve patient outcomes and surgeon training, and to reduce the costs of surgery, has motivated the equipment of operating rooms with sensors that record surgical interventions. The richness and complexity of the data that are collected call for new methods to support computer-assisted surgery. The aim of this paper is to support the monitoring of junior surgeons learning their surgical skill sets.Methods: Our method is fully automatic and takes as input a series of surgical interventions each represented by a low-level recording of all activities performed by the surgeon during the intervention (e.g., cut the skin with a scalpel). Our method produces a curve describing the process of standardization of the behavior of junior surgeons. Given the fact that junior surgeons receive constant feedback from senior surgeons during surgery, these curves can be directly interpreted as learning curves.Results: Our method is assessed using the behavior of a junior surgeon in anterior cervical discectomy and fusion surgery over his first three years after residency. They revealed the ability of the method to accurately represent the surgical skill evolution. We also showed that the learning curves can be computed by phases allowing a finer evaluation of the skill progression.Conclusion: Preliminary results suggest that our approach constitutes a useful addition to surgical training monitoring. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for breast cancer classification using time series analysis. In particular, the region of interest (ROI) in mammogram images is classified as normal or abnormal using dynamic time warping (DTW) as a similarity measure. According to the analogous case in time series analysis, the DTW subsumes Euclidean distance (ED) as a specific case with increased robustness due to DTW flexibility to address local horizontal/vertical deformations. This method is especially attractive for biomedical image analysis and is applied to mammogram classification for the first time in this paper. The current study concludes that varying the size of the ROI images and the restriction on the search criteria for the warping path do not affect the performance because the method produces good classification results with reduced computational complexity. The method is tested on the IRMA and MIAS dataset using the k-nearest neighbour classifier for different k values, which produces an area under curve (AUC) value of 0.9713 for one of the best scenarios. [ABSTRACT FROM AUTHOR]

In recent years, digital endoscopy has established as key technology for medical screenings and minimally invasive surgery. Since then, various research communities with manifold backgrounds have picked up on the idea of processing and automatically analyzing the inherently available video signal that is produced by the endoscopic camera. Proposed works mainly include image processing techniques, pattern recognition, machine learning methods and Computer Vision algorithms. While most contributions deal with real-time assistance at procedure time, the post-procedural processing of recorded videos is still in its infancy. Many post-processing problems are based on typical Multimedia methods like indexing, retrieval, summarization and video interaction, but have only been sparsely addressed so far for this domain. The goals of this survey are (1) to introduce this research field to a broader audience in the Multimedia community to stimulate further research, (2) to describe domain-specific characteristics of endoscopic videos that need to be addressed in a pre-processing step, and (3) to systematically bring together the very diverse research results for the first time to provide a broader overview of related research that is currently not perceived as belonging together. [ABSTRACT FROM AUTHOR]

Purpose: Teleoperated robotic systems are nowadays routinely used for specific interventions. Benefits of robotic training courses have already been acknowledged by the community since manipulation of such systems requires dedicated training. However, robotic surgical simulators remain expensive and require a dedicated human-machine interface. Methods: We present a low-cost contactless optical sensor, the Leap Motion, as a novel control device to manipulate the RAVEN-II robot. We compare peg manipulations during a training task with a contact-based device, the electro-mechanical Sigma.7. We perform two complementary analyses to quantitatively assess the performance of each control method: a metric-based comparison and a novel unsupervised spatiotemporal trajectory clustering. Results: We show that contactless control does not offer as good manipulability as the contact-based. Where part of the metric-based evaluation presents the mechanical control better than the contactless one, the unsupervised spatiotemporal trajectory clustering from the surgical tool motions highlights specific signature inferred by the human-machine interfaces. Conclusions: Even if the current implementation of contactless control does not overtake manipulation with high-standard mechanical interface, we demonstrate that using the optical sensor complete control of the surgical instruments is feasible. The proposed method allows fine tracking of the trainee's hands in order to execute dexterous laparoscopic training gestures. This work is promising for development of future human-machine interfaces dedicated to robotic surgical training systems. [ABSTRACT FROM AUTHOR]

Purpose: Hospitals' effectiveness and efficiency can be enhanced by automating the resource and time management of the most cost-intensive unit in the hospital: the operating room (OR). The key elements required for the ideal organization of hospital staff and technical resources (such as instruments in the OR) are an exact online forecast of both the surgeon's resource usage and the remaining intervention time. Methods: This paper presents a novel online approach relying on time series analysis and the application of a linear time-variant system. We calculated the power spectral density and the spectrogram of surgical perspectives (e.g., used instrument) of interest to compare several surgical workflows. Results: Considering only the use of the surgeon's right hand during an intervention, we were able to predict the remaining intervention time online with an error of 21 min 45 s ±9 min 59 s for lumbar discectomy. Furthermore, the performance of forecasting of technical resource usage in the next 20 min was calculated for a combination of spectral analysis and the application of a linear time-variant system (sensitivity: 74 %; specificity: 75 %) focusing on just the use of surgeon's instrument in question. Conclusion: The outstanding benefit of these methods is that the automated recording of surgical workflows has minimal impact during interventions since the whole set of surgical perspectives need not be recorded. The resulting predictions can help various stakeholders such as OR staff and hospital technicians. Moreover, reducing resource conflicts could well improve patient care. [ABSTRACT FROM AUTHOR]

There is a growing awareness of the need for evidence-based surgery and of the issues that are specific to research in surgery. Well-conducted anatomical studies can represent the first, preclinical step for evidence-based surgical innovation and evaluation. In the last two decades, various reports have quantified and compared neurosurgical approaches in the anatomy laboratory using different methods and technology. The aim of this study was to critically review these papers. A PubMed and Scopus search was performed to select articles that quantified and compared different neurosurgical approaches in the preclinical setting. The basic characteristics that anatomically define a surgical approach were defined. Each study was analyzed for measured features and quantification method and technique. Ninety-nine papers, published from 1990 to 2013, were included in this review. A heterogeneous use of terms to define the features of a surgical approach was evident. Different methods to study these features have been reported; they are generally based on quantification of distances, angles, and areas. Measuring tools have evolved from the simple ruler to frameless stereotactic devices. The reported methods have each specific advantages and limits; a common limitation is the lack of 3D visualization and surgical volume quantification. There is a need for a uniform nomenclature in anatomical studies. Frameless stereotactic devices provide a powerful tool for anatomical studies. Volume quantification and 3D visualization of the surgical approach is not provided with most available methods. [ABSTRACT FROM AUTHOR]

Purpose: Identifying key steps and barriers within complex and simple surgical procedures can be accomplished in a structured and rigorous manner using surgical process modeling. For lower extremity long bone fracture stabilization, the current standard of care is closed intramedullary (IM) nailing, which, despite its widespread use, is associated with challenges that greatly impact operative time and lead to the frustration of medical staff. The aim of this study was to identify challenging surgical steps in IM nailing and understand their underlying causation. Methods: Eight semi-structured interviews with staff orthopedic surgeons and eight detailed surgical observations were conducted to understand the surgical steps, challenges and adapted techniques used in IM nailing. Hierarchical decomposition was then utilized to structure the IM nailing surgical procedure into phases, steps and activities. Results: In the developed IM nailing surgical process model, the most challenging steps were identified as fracture reduction (75 %) and entry point selection (25 %), both of which were associated with high levels of frustration in the observed surgeries. Both of these steps utilize 2D fluoroscopic imaging to guide 3D alignment. Challenges arise when the alignment in one plane is lost while adjusting the alignment in the perpendicular plane. This leads to unpredictable repetition of activities which can be time-consuming and frustrating. Conclusion: Identifying the causation of surgical challenges in IM nailing through surgical process modeling forms a knowledge base that can be used to guide future improvements to techniques and surgical instrumentation. [ABSTRACT FROM AUTHOR]

Purpose: A methodological framework is introduced to assess and compare a conventional fluoroscopy protocol for peripheral angioplasty with a new magnetic resonant imaging (MRI)-guided protocol. Different scenarios were considered during interventions on a perfused arterial phantom with regard to time-based and cognitive task analysis, user experience and ergonomics. Methods: Three clinicians with different expertise performed a total of 43 simulated common iliac angioplasties (9 fluoroscopic, 34 MRI-guided) in two blocks of sessions. Six different configurations for MRI guidance were tested in the first block. Four of them were evaluated in the second block and compared to the fluoroscopy protocol. Relevant stages' durations were collected, and interventions were audio-visually recorded from different perspectives. A cued retrospective protocol analysis (CRPA) was undertaken, including personal interviews. In addition, ergonomic constraints in the MRI suite were evaluated. Results: Significant differences were found when comparing the performance between MRI configurations versus fluoroscopy. Two configurations [with times of 8.56 (0.64) and 9.48 (1.13) min] led to reduce procedure time for MRI guidance, comparable to fluoroscopy [8.49 (0.75) min]. The CRPA pointed out the main influential factors for clinical procedure performance. The ergonomic analysis quantified musculoskeletal risks for interventional radiologists when utilising MRI. Several alternatives were suggested to prevent potential low-back injuries. Conclusions: This work presents a step towards the implementation of efficient operational protocols for MRI-guided procedures based on an integral and multidisciplinary framework, applicable to the assessment of current vascular protocols. The use of first-user perspective raises the possibility of establishing new forms of clinical training and education. [ABSTRACT FROM AUTHOR]

Purpose: Three main approaches can be identified for modelling surgical performance: a competency-based approach, a task-based approach, both largely explored in the literature, and a less known work domain-based approach. The work domain-based approach first describes the work domain properties that constrain the agent's actions and shape the performance. This paper presents a work domain-based approach for modelling performance during cervical spine surgery, based on the idea that anatomical structures delineate the surgical performance. This model was evaluated through an analysis of junior and senior surgeons' actions. Method: Twenty-four cervical spine surgeries performed by two junior and two senior surgeons were recorded in real time by an expert surgeon. According to a work domain-based model describing an optimal progression through anatomical structures, the degree of adjustment of each surgical procedure to a statistical polynomial function was assessed. Results: Each surgical procedure showed a significant suitability with the model and regression coefficient values around 0.9. However, the surgeries performed by senior surgeons fitted this model significantly better than those performed by junior surgeons. Analysis of the relative frequencies of actions on anatomical structures showed that some specific anatomical structures discriminate senior from junior performances. Conclusion: The work domain-based modelling approach can provide an overall statistical indicator of surgical performance, but in particular, it can highlight specific points of interest among anatomical structures that the surgeons dwelled on according to their level of expertise. [ABSTRACT FROM AUTHOR]

Purpose: Analyzing surgical activities has received a growing interest in recent years. Several methods have been proposed to identify surgical activities and surgical phases from data acquired in operating rooms. These context-aware systems have multiple applications including: supporting the surgical team during the intervention, improving the automatic monitoring, designing new teaching paradigms. Methods: In this paper, we use low-level recordings of the activities that are performed by a surgeon to automatically predict the current (high-level) phase of the surgery. We augment a decision tree algorithm with the ability to consider the local context of the surgical activities and a hierarchical clustering algorithm. Results: Experiments were performed on 22 surgeries of lumbar disk herniation. We obtained an overall precision of 0.843 in detecting phases of 51,489 single activities. We also assess the robustness of the method with regard to noise. Conclusion: We show that using the local context allows us to improve the results compared with methods only considering single activity. Experiments show that the use of the local context makes our method very robust to noise and that clustering the input data first improves the predictions. [ABSTRACT FROM AUTHOR]

Background: The expansion of endovascular techniques for intracranial aneurysms has led to a global decrease in vascular neurosurgery activity. This situation might impact neurosurgeons' level of expertise, even though they all might have to deal with this surgically challenging pathology. In that context, we wanted to assess the impact of cumulative surgical experience on the outcome of patients with poor-grade subarachnoid haemorrhage (SAH) and intracerebral haemorrhage (ICH) treated by microsurgery. Methods: Sixty-seven patients who underwent surgery for a ruptured aneurysm with SAH and ICH, and a WFNS scale of IV/V, were included. Surgeries were performed by five surgeons, whose experience was judged by the total number of aneurysm surgeries performed. The outcome was assessed by three indicators: intraoperative rupture (IOR), early mortality, and the modified Rankin Scale at last follow-up. The time of IOR was reported on an IOR score. The correlation between surgical experience and outcome was assessed by linear regression. Nonlinear regression was used to assess the correlation of the data with a learning curve model. Results: The analysis showed an influence of surgical experience on intraoperative rupture, with no effect on long-term outcome. No influence was found on early mortality. Increased surgical experience seems to reduce IOR during aneurysm dissection and clip repositioning. Intraoperative rupture data fit Wright's learning curve model. Conclusion: This study suggests a direct impact of cumulative experience on the course of ruptured aneurysm surgery and pleads for the use of training and simulation programmes dedicated to neurovascular surgery. [ABSTRACT FROM AUTHOR]

Purpose: Advanced surgical procedures, which have become complex and difficult, increase the burden of surgeons. Quantitative analysis of surgical procedures can improve training, reduce variability, and enable optimization of surgical procedures. To this end, a surgical task analysis system was developed that uses only surgical navigation information. Methods: Division of the surgical procedure, task progress analysis, and task efficiency analysis were done. First, the procedure was divided into five stages. Second, the operating time and progress rate were recorded to document task progress during specific stages, including the dissecting task. Third, the speed of the surgical instrument motion (mean velocity and acceleration), as well as the size and overlap ratio of the approximate ellipse of the location log data distribution, was computed to estimate the task efficiency during each stage. These analysis methods were evaluated based on experimental validation with two groups of surgeons, i.e., skilled and 'other' surgeons. The performance metrics and analytical parameters included incidents during the operation, the surgical environment, and the surgeon's skills or habits. Results: Comparison of groups revealed that skilled surgeons tended to perform the procedure in less time and involved smaller regions; they also manipulated the surgical instruments more gently. Conclusion: Surgical task analysis developed for quantitative assessment of surgical procedures and surgical performance may provide practical methods and metrics for objective evaluation of surgical expertise. [ABSTRACT FROM AUTHOR]

Introduction: Automatic surgical activity recognition in the operating room (OR) is mandatory to enable assistive surgical systems to manage the information presented to the surgical team. Therefore the purpose of our study was to develop and evaluate an activity recognition model. Material and methods: The system was conceived as a hierarchical recognition model which separated the recognition task into activity aspects. The concept used radio frequency identification (RFID) for instrument recognition and accelerometers to infer the performed surgical action. Activity recognition was done by combining intermediate results of the aspect recognition. A basic scheme of signal feature generation, clustering and sequence learning was replicated in all recognition subsystems. Hidden Markov models (HMM) were used to generate probability distributions over aspects and activities. Simulated functional endoscopic sinus surgeries (FESS) were used to evaluate the system. Results and discussion: The system was able to detect surgical activities with an accuracy of 95%. Instrument recognition performed best with 99% accuracy. Action recognition showed lower accuracies with 81% due to the high variability of surgical motions. All stages of the recognition scheme were evaluated. The model allows distinguishing several surgical activities in an unconstrained surgical environment. Future improvements could push activity recognition even further. [ABSTRACT FROM AUTHOR]

Purpose : Surgery is continuously subject to technological and medical innovations that are transforming daily surgical routines. In order to gain a better understanding and description of surgeries, the field of surgical process modelling (SPM) has recently emerged. The challenge is to support surgery through the quantitative analysis and understanding of operating room activities. Related surgical process models can then be introduced into a new generation of computer-assisted surgery systems. Methods : In this paper, we present a review of the literature dealing with SPM. This methodological review was obtained from a search using Google Scholar on the specific keywords: 'surgical process analysis', 'surgical process model' and 'surgical workflow analysis'. Results : This paper gives an overview of current approaches in the field that study the procedural aspects of surgery. We propose a classification of the domain that helps to summarise and describe the most important components of each paper we have reviewed, i.e., acquisition, modelling, analysis, application and validation/evaluation. These five aspects are presented independently along with an exhaustive list of their possible instantiations taken from the studied publications. Conclusion : This review allows a greater understanding of the SPM field to be gained and introduces future related prospects. [ABSTRACT FROM AUTHOR]

Surgeons' procedural skills and intraoperative decision making are key elements of clinical practice. However, the objective assessment of these skills remains a challenge to this day. Surgical workflow analysis (SWA) is emerging as a powerful tool to solve this issue in surgical educational environments in real time. Typically, SWA makes use of video signals to automatically identify the surgical phase. We hypothesize that the analysis of surgeons' speech using natural language processing (NLP) can provide deeper insight into the surgical decision-making processes. As a preliminary step, this study proposes to use audio signals registered in the educational operating room (OR) to classify the phases of a laparoscopic cholecystectomy (LC). To do this, we firstly created a database with the transcriptions of audio recorded in surgical educational environments and their corresponding phase. Secondly, we compared the performance of four feature extraction techniques and four machine learning models to find the most appropriate model for phase recognition. The best resulting model was a support vector machine (SVM) coupled to a hidden-Markov model (HMM), trained with features obtained with Word2Vec (82.95% average accuracy). The analysis of this model's confusion matrix shows that some phrases are misplaced due to the similarity in the words used. The study of the model's temporal component suggests that further attention should be paid to accurately detect surgeons' normal conversation. This study proves that speech-based classification of LC phases can be effectively achieved. This lays the foundation for the use of audio signals for SWA, to create a framework of LC to be used in surgical training, especially for the training and assessment of procedural and decision-making skills (e.g., to assess residents' procedural knowledge and their ability to react to adverse situations). [ABSTRACT FROM AUTHOR]