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
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DOI：
10.1007/s11548-015-1222-1
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发表时间：
2015-09-01
影响因子：
3
通讯作者：
Gibaud, Bernard
中科院分区：
文献类型：
--
作者：
Katic, Darko;Julliard, Chantal;Gibaud, Bernard
术中信息的增加可能会超过我们处理信息的能力。情境感知系统，过滤信息以自动适应外科医生的当前需求，是充分利用计算机化手术的必要条件。为了获得上下文意识，医学知识的表示是至关重要的。然而，大多数现有系统不能以可重用的方式表示知识，这也阻碍了数据的重用。因此，我们的目的是使我们的医学知识计算模型与以LapOntoSPM本体形式建立的知识表示具有可共享性、可扩展性和互操作性。为了显示其有用性，我们将其应用于情境解释，即基于手术活动的手术阶段识别。考虑到本体论工程的最佳实践，在腹腔镜本体论的基础上，我们在手术过程模型新标准OntoSPM框架下形式化了腹腔镜肾上腺切除术、胆囊切除术和胰腺切除术的工作流程。在此基础上，提出了一种基于SQWRL的基于规则的态势解释算法，利用本体识别手术阶段。该系统根据19例人工注释手术的真实数据进行评估。目的是表明相位识别能力等于一个专门的解决方案。新系统的识别率与专业系统相当。然而，解释情况所需的时间从平均0.5秒增加到1.8秒，这对于实际应用来说仍然是可行的。我们成功地将腹腔镜手术的医学知识整合到OntoSPM中，促进了知识和数据的共享。这对于结果的再现性和识别算法的无偏比较尤其重要。相关的识别算法在不损失分类能力的情况下适应新的表示。这项工作是标准化上下文感知领域的知识和数据表示，从而实现统一基准数据集的重要一步。
The rise of intraoperative information threatens to outpace our abilities to process it. Context-aware systems, filtering information to automatically adapt to the current needs of the surgeon, are necessary to fully profit from computerized surgery. To attain context awareness, representation of medical knowledge is crucial. However, most existing systems do not represent knowledge in a reusable way, hindering also reuse of data. Our purpose is therefore to make our computational models of medical knowledge sharable, extensible and interoperational with established knowledge representations in the form of the LapOntoSPM ontology. To show its usefulness, we apply it to situation interpretation, i.e., the recognition of surgical phases based on surgical activities.Considering best practices in ontology engineering and building on our ontology for laparoscopy, we formalized the workflow of laparoscopic adrenalectomies, cholecystectomies and pancreatic resections in the framework of OntoSPM, a new standard for surgical process models. Furthermore, we provide a rule-based situation interpretation algorithm based on SQWRL to recognize surgical phases using the ontology.The system was evaluated on ground-truth data from 19 manually annotated surgeries. The aim was to show that the phase recognition capabilities are equal to a specialized solution. The recognition rates of the new system were equal to the specialized one. However, the time needed to interpret a situation rose from 0.5 to 1.8 s on average which is still viable for practical application.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.