Toward a standard ontology of surgical process models.

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.

Bridging the gap between formal and experience-based knowledge for context-aware laparoscopy.

PURPOSE: Computer assistance is increasingly common in surgery. However, the amount of information is bound to overload processing abilities of surgeons. We propose methods to recognize the current phase of a surgery for context-aware information filtering. The purpose is to select the most suitable subset of information for surgical situations which require special assistance. METHODS: We combine formal knowledge, represented by an ontology, and experience-based knowledge, represented by training samples, to recognize phases. For this purpose, we have developed two different methods. Firstly, we use formal knowledge about possible phase transitions to create a composition of random forests. Secondly, we propose a method based on cultural optimization to infer formal rules from experience to recognize phases. RESULTS: The proposed methods are compared with a purely formal knowledge-based approach using rules and a purely experience-based one using regular random forests. The comparative evaluation on laparoscopic pancreas resections and adrenalectomies employs a consistent set of quality criteria on clean and noisy input. The rule-based approaches proved best with noisefree data. The random forest-based ones were more robust in the presence of noise. CONCLUSION: Formal and experience-based knowledge can be successfully combined for robust phase recognition.

Toward cognitive pipelines of medical assistance algorithms.

PURPOSE: Assistance algorithms for medical tasks have great potential to support physicians with their daily work. However, medicine is also one of the most demanding domains for computer-based support systems, since medical assistance tasks are complex and the practical experience of the physician is crucial. Recent developments in the area of cognitive computing appear to be well suited to tackle medicine as an application domain. METHODS: We propose a system based on the idea of cognitive computing and consisting of auto-configurable medical assistance algorithms and their self-adapting combination. The system enables automatic execution of new algorithms, given they are made available as Medical Cognitive Apps and are registered in a central semantic repository. Learning components can be added to the system to optimize the results in the cases when numerous Medical Cognitive Apps are available for the same task. Our prototypical implementation is applied to the areas of surgical phase recognition based on sensor data and image progressing for tumor progression mappings. RESULTS: Our results suggest that such assistance algorithms can be automatically configured in execution pipelines, candidate results can be automatically scored and combined, and the system can learn from experience. Furthermore, our evaluation shows that the Medical Cognitive Apps are providing the correct results as they did for local execution and run in a reasonable amount of time. CONCLUSION: The proposed solution is applicable to a variety of medical use cases and effectively supports the automated and self-adaptive configuration of cognitive pipelines based on medical interpretation algorithms.

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

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

A system for context-aware intraoperative augmented reality in dental implant surgery.

PURPOSE: Large volumes of information in the OR are ignored by surgeons when the amount outpaces human mental processing abilities. We developed an augmented reality (AR) system for dental implant surgery that acts as an automatic information filter, selectively displaying only relevant information. The purpose is to reduce information overflow and offer intuitive image guidance. The system was evaluated in a pig cadaver experiment. METHODS: Information filtering is implemented via rule-based situation interpretation with description logics. The interpretation is based on intraoperative distances measurement between anatomical structures and the dental drill with optical tracking. For AR, a head-mounted display is used, which was calibrated with a novel method based on SPAAM. To adapt to surgeon specific preferences, we offer two alternative display formats: one with static and another with contact analog AR. RESULTS: The system made the surgery easier and showed ergonomical benefits, as assessed by a questionnaire. All relevant phases were recognized reliably. The new calibration showed significant improvements, while the deviation of the realized implants was <2.5 mm. CONCLUSION: The system allowed the surgeon to fully concentrate on the surgery itself. It offered greater flexibility since the surgeon received all relevant information, but was free to deviate from it. Accuracy of the realized implants remains an open issue and part of future work.

Context-aware Augmented Reality in laparoscopic surgery.

Augmented Reality is a promising paradigm for intraoperative assistance. Yet, apart from technical issues, a major obstacle to its clinical application is the man-machine interaction. Visualization of unnecessary, obsolete or redundant information may cause confusion and distraction, reducing usefulness and acceptance of the assistance system. We propose a system capable of automatically filtering available information based on recognized phases in the operating room. Our system offers a specific selection of available visualizations which suit the surgeon's needs best. The system was implemented for use in laparoscopic liver and gallbladder surgery and evaluated in phantom experiments in conjunction with expert interviews.