A DVRK-based Framework for Surgical Subtask Automation

doi:10.12700/aph.16.8.2019.8.5

Cited by 19 publications

(12 citation statements)

References 22 publications

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“…While efforts have been made for standardization of SPM ontology definition (47), there exist a wide range of techniques that are tailored to a specific type of intervention [e.g., (48)(49)(50)]. Furthermore, as stated in (51), there are multiple definitions of modeling granularity levels available in the prior-art with no consensus across different domains. To this end, we have chosen a combination of recognized modeling schemes presented in (15,52,53), which are among the most widely used approaches to date that describe a SPM based on 6 granularity levels (µ) that correspond to different hierarchical elements, with µ ǫ [0,5].…”

Section: Ontology and Deductive Modeling Of Spm For Open Spinal Surgeriesmentioning

confidence: 99%

Surgical Process Modeling for Open Spinal Surgeries

Carrillo

Esfandiari²,

Muller³

et al. 2022

Front. Surg.

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Modern operating rooms are becoming increasingly advanced thanks to the emerging medical technologies and cutting-edge surgical techniques. Current surgeries are transitioning into complex processes that involve information and actions from multiple resources. When designing context-aware medical technologies for a given intervention, it is of utmost importance to have a deep understanding of the underlying surgical process. This is essential to develop technologies that can correctly address the clinical needs and can adapt to the existing workflow. Surgical Process Modeling (SPM) is a relatively recent discipline that focuses on achieving a profound understanding of the surgical workflow and providing a model that explains the elements of a given surgery as well as their sequence and hierarchy, both in quantitative and qualitative manner. To date, a significant body of work has been dedicated to the development of comprehensive SPMs for minimally invasive baroscopic and endoscopic surgeries, while such models are missing for open spinal surgeries. In this paper, we provide SPMs common open spinal interventions in orthopedics. Direct video observations of surgeries conducted in our institution were used to derive temporal and transitional information about the surgical activities. This information was later used to develop detailed SPMs that modeled different primary surgical steps and highlighted the frequency of transitions between the surgical activities made within each step. Given the recent emersion of advanced techniques that are tailored to open spinal surgeries (e.g., artificial intelligence methods for intraoperative guidance and navigation), we believe that the SPMs provided in this study can serve as the basis for further advancement of next-generation algorithms dedicated to open spinal interventions that require a profound understanding of the surgical workflow (e.g., automatic surgical activity recognition and surgical skill evaluation). Furthermore, the models provided in this study can potentially benefit the clinical community through standardization of the surgery, which is essential for surgical training.

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Section: Ontology and Deductive Modeling Of Spm For Open Spinal Surgeriesmentioning

confidence: 99%

Surgical Process Modeling for Open Spinal Surgeries

Carrillo

Esfandiari²,

Muller³

et al. 2022

Front. Surg.

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“…A great challenge of laparascopic surgery consist of the development of perception algorithms, that extract information from the unpredictable environment containing soft tissue. Therefore, autonomous navigation in MIRS is still challenging (Nagy and Haidegger, 2019).…”

Section: State Of the Artmentioning

confidence: 99%

“…Therefore, shared control could be seen as an intermediate step towards the increase to tasklevel autonomy. Assistance functions may range from displaying relevant data using mixed reality (Qian et al, 2019), over tremor filtering and scaling of input movements (Weber et al, 2013) to the automation of certain steps of a task under the supervision of the surgeon (Nagy and Haidegger, 2019). VFs as described in Rosenberg (1993), Abbott et al (2007), and Bowyer et al (2014) represent a different type of shared control which enables haptic augmentation.…”

Section: State Of the Artmentioning

confidence: 99%

A Digital Twin Approach for Contextual Assistance for Surgeons During Surgical Robotics Training

et al. 2021

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Minimally invasive robotic surgery copes with some disadvantages for the surgeon of minimally invasive surgery while preserving the advantages for the patient. Most commercially available robotic systems are telemanipulated with haptic input devices. The exploitation of the haptics channel, e.g., by means of Virtual Fixtures, would allow for an individualized enhancement of surgical performance with contextual assistance. However, it remains an open field of research as it is non-trivial to estimate the task context itself during a surgery. In contrast, surgical training allows to abstract away from a real operation and thus makes it possible to model the task accurately. The presented approach exploits this fact to parameterize Virtual Fixtures during surgical training, proposing a Shared Control Parametrization Engine that retrieves procedural context information from a Digital Twin. This approach accelerates a proficient use of the robotic system for novice surgeons by augmenting the surgeon’s performance through haptic assistance. With this our aim is to reduce the required skill level and cognitive load of a surgeon performing minimally invasive robotic surgery. A pilot study is performed on the DLR MiroSurge system to evaluate the presented approach. The participants are tasked with two benchmark scenarios of surgical training. The execution of the benchmark scenarios requires basic skills as pick, place and path following. The evaluation of the pilot study shows the promising trend that novel users profit from the haptic augmentation during training of certain tasks.

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“…Currently, many research groups are working on this problem [ 12 , 13 ]; some groups chose to work in ex vivo (or rarely in vivo) [ 14 , 15 ] or realistic phantom environments [ 16 ], but simplified silicone phantoms are utilized mostly [ 15 , 17 , 18 , 19 , 20 , 21 ]. In the most recent years, the automation of simple surgical training exercises on rigid [ 22 , 23 , 24 , 25 , 26 , 27 , 28 ] or deformable [ 29 , 30 ] phantoms tends to receive increasing attention. Among all the training exercises, the automation of different versions of peg transfer is presented in the most significant number of studies [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 31 ], probably due to its simplicity, enabling to elaborate the basic principles and best algorithms for automation.…”

Section: Introductionmentioning

confidence: 99%

Performance and Capability Assessment in Surgical Subtask Automation

Nagy

Haidegger

2022

Sensors

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Robot-Assisted Minimally Invasive Surgery (RAMIS) has reshaped the standard clinical practice during the past two decades. Many believe that the next big step in the advancement of RAMIS will be partial autonomy, which may reduce the fatigue and the cognitive load on the surgeon by performing the monotonous, time-consuming subtasks of the surgical procedure autonomously. Although serious research efforts are paid to this area worldwide, standard evaluation methods, metrics, or benchmarking techniques are still not formed. This article aims to fill the void in the research domain of surgical subtask automation by proposing standard methodologies for performance evaluation. For that purpose, a novel characterization model is presented for surgical automation. The current metrics for performance evaluation and comparison are overviewed and analyzed, and a workflow model is presented that can help researchers to identify and apply their choice of metrics. Existing systems and setups that serve or could serve as benchmarks are also introduced and the need for standard benchmarks in the field is articulated. Finally, the matter of Human–Machine Interface (HMI) quality, robustness, and the related legal and ethical issues are presented.

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A DVRK-based Framework for Surgical Subtask Automation

Cited by 19 publications

References 22 publications

Surgical Process Modeling for Open Spinal Surgeries

Surgical Process Modeling for Open Spinal Surgeries

A Digital Twin Approach for Contextual Assistance for Surgeons During Surgical Robotics Training

Performance and Capability Assessment in Surgical Subtask Automation

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