Development of workflow task analysis during cerebral diagnostic angiographies: Time-based comparison of junior and senior tasks

Gentric, Jean-Christophe; Trelhu, Brivael; Jannin, Pierre; Riffaud, Laurent; Ferré, Jean–Christophe; Gauvrit, Jean‐Yves

doi:10.1016/j.neurad.2013.01.002

Cited by 8 publications

(7 citation statements)

References 22 publications

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“…C linicians and surgical staff are the main actors in the operating room (OR). Detecting clinicians and staff as well as localizing their body parts are therefore important to a wide range of applications, such as modeling and recognition of medical activities [1]- [5], performance assessment of the surgical team [6], safe human-robot interaction and collision avoidance [7], [8], and radiation safety monitoring [9], [10]. Current methods proposed to estimate the poses of humans in the OR use multi-view systems and rely on foreground estimation [7], [9].…”

Section: Introductionmentioning

confidence: 99%

Articulated clinician detection using 3D pictorial structures on RGB-D data

Kadkhodamohammadi

Gangi

Mathelin

et al. 2017

Medical Image Analysis

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Abstract-Reliable human pose estimation (HPE) is essential to many clinical applications, such as surgical workflow analysis, radiation safety monitoring and human-robot cooperation. Proposed methods for the operating room (OR) rely either on foreground estimation using a multi-camera system, which is a challenge in real ORs due to color similarities and frequent illumination changes, or on wearable sensors or markers, which are invasive and therefore difficult to introduce in the room. Instead, we propose a novel approach based on Pictorial Structures (PS) and on RGB-D data, which can be easily deployed in real ORs. We extend the PS framework in two ways. First, we build robust and discriminative part detectors using both color and depth images. We also present a novel descriptor for depth images, called histogram of depth differences (HDD). Second, we extend PS to 3D by proposing 3D pairwise constraints and a new method that makes exact inference tractable. Our approach is evaluated for pose estimation and clinician detection on a challenging RGB-D dataset recorded in a busy operating room during live surgeries. We conduct series of experiments to study the different part detectors in conjunction with the various 2D or 3D pairwise constraints. Our comparisons demonstrate that 3D PS with RGB-D part detectors significantly improves the results in a visually challenging operating environment.

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Section: Introductionmentioning

confidence: 99%

Articulated clinician detection using 3D pictorial structures on RGB-D data

Kadkhodamohammadi

Gangi

Mathelin

et al. 2017

Medical Image Analysis

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“…These sources may be monitored using different methods [8]. For instance, sensors based on RFID technologies have been used for instrument, medications and surgical staff tracking [9,10]. More complex tracking systems have been studied to be applied for the creation of surgical process models, such as eye tracking [11] or ultrasonic systems [12].…”

Section: Clinical Contextmentioning

confidence: 99%

“…One of the steps involved in SPM is surgical workflow analysis (SWA), which is the deconstructing process by which a surgical process is divided into a list of different phases, activities or tasks [9]. This analysis is not straightforward, mainly because it is necessarily different for each surgical procedure.…”

Section: Clinical Contextmentioning

confidence: 99%

Speech-Based Surgical Phase Recognition for Non-Intrusive Surgical Skills’ Assessment in Educational Contexts

Guzmán-García

Gómez-Tome

Sánchez-González

et al. 2021

Sensors

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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).

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“…One of the steps involved in surgical process modelling (SPM) is surgical workflow analysis (SWA), which is the deconstructing process by which a surgical process is divided into a list of different phases, activities or tasks [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Assessment of Procedural Skills Based on the Surgical Workflow Analysis Derived from Speech and Video

et al. 2022

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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.

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Development of workflow task analysis during cerebral diagnostic angiographies: Time-based comparison of junior and senior tasks

Abstract: This approach allows reliable skill assessment in the radiology OR and comparison of junior and senior competencies during cerebral diagnostic angiography. This new tool can improve the quality and safety of procedures, and facilitate the learning process for neuroradiologists.

Cited by 8 publications

References 22 publications

Articulated clinician detection using 3D pictorial structures on RGB-D data

Articulated clinician detection using 3D pictorial structures on RGB-D data

Speech-Based Surgical Phase Recognition for Non-Intrusive Surgical Skills’ Assessment in Educational Contexts

Automatic Assessment of Procedural Skills Based on the Surgical Workflow Analysis Derived from Speech and Video

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