Automatic knowledge-based recognition of low-level tasks in ophthalmological procedures

Lalys, Florent; Bouget, David; Riffaud, Laurent; Jannin, Pierre

doi:10.1007/s11548-012-0685-6

Cited by 57 publications

(45 citation statements)

References 18 publications

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“…According to the levels of granularity [21], our system takes input at the level of surgical activities and interprets them to infer the current phase of the surgery. In its intended use, the input is provided by intraoperative sensor analysis [30].…”

Section: Methodsmentioning

confidence: 99%

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LapOntoSPM: an ontology for laparoscopic surgeries and its application to surgical phase recognition

et al. 2015

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We successfully integrated medical knowledge for laparoscopic surgeries into OntoSPM, facilitating knowledge and data sharing. This is especially important for reproducibility of results and unbiased comparison of recognition algorithms. The associated recognition algorithm was adapted to the new representation without any loss of classification power. The work is an important step to standardized knowledge and data representation in the field on context awareness and thus toward unified benchmark data sets.

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Section: Methodsmentioning

confidence: 99%

“…It is not geared toward a specific surgical domain and can be used universally [20]. Combinations of formalized and machine learning approaches have also been proposed [21]. A related field is the area of surgical evaluation, where similar methods are used to assess the performance of the surgeon [22].…”

Section: Introductionmentioning

confidence: 99%

LapOntoSPM: an ontology for laparoscopic surgeries and its application to surgical phase recognition

et al. 2015

Self Cite

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“…Most of these approaches achieve accuracies of between 80% and 90%. Additionally, this research, in general, focused on several different clinical use-cases, such as pituitary surgery [33], laparoscopic sigmoidectomy [50], cataract surgery [51,52], or laparoscopic cholecystectomy [36].…”

Section: Data Acquisition By Sensor Systemsmentioning

confidence: 99%

Surgical process modeling

Neumuth

2017

Innovative Surgical Sciences

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Due to the rapidly evolving medical, technological, and technical possibilities, surgical procedures are becoming more and more complex. On the one hand, this offers an increasing number of advantages for patients, such as enhanced patient safety, minimal invasive interventions, and less medical malpractices. On the other hand, it also heightens pressure on surgeons and other clinical staff and has brought about a new policy in hospitals, which must rely on a great number of economic, social, psychological, qualitative, practical, and technological resources. As a result, medical disciplines, such as surgery, are slowly merging with technical disciplines. However, this synergy is not yet fully matured. The current information and communication technology in hospitals cannot manage the clinical and operational sequence adequately. The consequences are breaches in the surgical workflow, extensions in procedure times, and media disruptions. Furthermore, the data accrued in operating rooms (ORs) by surgeons and systems are not sufficiently implemented. A flood of information, "big data", is available from information systems. That might be deployed in the context of Medicine 4.0 to facilitate the surgical treatment. However, it is unused due to infrastructure breaches or communication errors. Surgical process models (SPMs) alleviate these problems. They can be defined as simplified, formal, or semiformal representations of a network of surgery-related activities, reflecting a predefined subset of interest. They can employ different means of generation, languages, and data acquisition strategies. They can represent surgical interventions with high resolution, offering qualifiable and quantifiable information on the course of the intervention on the level of single, minute, surgical work-steps. The basic idea is to gather information concerning the surgical intervention and its activities, such as performance time, surgical instrument used, trajectories, movements, or intervention phases. These data can be gathered by means of workflow recordings.These recordings are abstracted to represent an individual surgical process as a model and are an essential requirement to enable Medicine 4.0 in the OR. Further abstraction can be generated by merging individual process models to form generic SPMs to increase the validity for a larger number of patients. Furthermore, these models can be applied in a wide variety of use-cases. In this regard, the term "modeling" can be used to support either one or more of the following tasks: "to describe", "to understand", "to explain", to optimize", "to learn", "to teach", or "to automate". Possible use-cases are requirements analyses, evaluating surgical assist systems, generating surgeon-specific training-recommendation, creating workflow management systems for ORs, and comparing different surgical strategies. The presented chapter will give an introduction into this challenging topic, presenting different methods to generate SPMs from the workflow in the OR, as well as various...

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“…Surgical tools, 92 visual and anatomical cues 93 as well as kinematic data 94 have been used to recognise surgical phases and actions. Our work has focused on the analysis of the workflow of cardiovascular procedures.…”

Section: Episode Segmentationmentioning

confidence: 99%

Cognitive AutonomouS CAtheters Operating in Dynamic Environments

Poorten

Tran

Devreker

et al. 2016

J. Med. Robot. Res.

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Advances in miniaturized surgical instrumentation are key to less demanding and safer medical interventions. In cardiovascular procedures interventionalists turn towards catheter-based interventions, treating patients considered unfit for more invasive approaches. A positive outcome is not guaranteed. The risk for calcium dislodgement, tissue damage or even vessel rupture cannot be eliminated when instruments are maneuvered through fragile and diseased vessels. This paper reports on the progress made in terms of catheter design, vessel reconstruction, catheter shape modeling, surgical skill analysis, decision-making and control. These efforts are geared towards the development of the necessary technology to autonomously steer catheters through the vasculature, a target of the EU-funded project CASCADE (Cognitive AutonomouS CAtheters operating in Dynamic Environments). Whereas autonomous placement of an aortic valve implant forms the ultimate and concrete goal, the technology of individual building blocks to reach such ambitious goal is expected to be much sooner impacting and assisting interventionalists in their daily clinical practice.

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Automatic knowledge-based recognition of low-level tasks in ophthalmological procedures

Abstract: The addition of human knowledge to traditional bottom-up approaches based on image analysis appears to be promising for low-level task detection. The results of this work could be used for the automatic indexation of post-operative videos.

Cited by 57 publications

References 18 publications

LapOntoSPM: an ontology for laparoscopic surgeries and its application to surgical phase recognition

LapOntoSPM: an ontology for laparoscopic surgeries and its application to surgical phase recognition

Surgical process modeling

Cognitive AutonomouS CAtheters Operating in Dynamic Environments

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