Generation of a Realistic Synthetic Laryngeal Cancer Cohort for AI Applications.

BACKGROUND: Obtaining large amounts of real patient data involves great efforts and expenses, and processing this data is fraught with data protection concerns. Consequently, data sharing might not always be possible, particularly when large, open science datasets are needed, as for AI development. For such purposes, the generation of realistic synthetic data may be the solution. Our project aimed to generate realistic cancer data with the use case of laryngeal cancer. METHODS: We used the open-source software Synthea and programmed an additional module for development, treatment and follow-up for laryngeal cancer by using external, real-world (RW) evidence from guidelines and cancer registries from Germany. To generate an incidence-based cohort view, we randomly drew laryngeal cancer cases from the simulated population and deceased persons, stratified by the real-world age and sex distributions at diagnosis. RESULTS: A module with age- and stage-specific treatment and prognosis for laryngeal cancer was successfully implemented. The synthesized population reflects RW prevalence well, extracting a cohort of 50,000 laryngeal cancer patients. Descriptive data on stage-specific and 5-year overall survival were in accordance with published data. CONCLUSIONS: We developed a large cohort of realistic synthetic laryngeal cancer cases with Synthea. Such data can be shared and published open source without data protection issues.

Using Digital Twins to Support Multiple Stages of the Patient Journey.

New possibilities in personalized medicine need to be complemented by clinical decision support systems as well as context-specific applications to be used in clinical routine. We aim to implement a shared technical backend for a large variety of applications in personalized head-and-neck cancer treatment. The infrastructure is conceptualized as a multi-purpose digital twin for cancer treatment. A set of prototypes of clinical applications demonstrates the feasibility of using digital twins to support multiple stages of the patient journey.

Ontology-based surgical workflow recognition and prediction.

BACKGROUND: Surgical context-aware systems can adapt to the current situation in the operating room and thus provide computer-aided assistance functionalities and intraoperative decision-support. To interact with the surgical team perceptively and assist the surgical process, the system needs to monitor the intraoperative activities, understand the current situation in the operating room at any time, and anticipate the following possible situations. METHODS: A structured representation of surgical process knowledge is a prerequisite for any applications in the intelligent operating room. For this purpose, a surgical process ontology, which is formally based on standard medical terminology (SNOMED CT) and an upper-level ontology (GFO), was developed and instantiated for a neurosurgical use case. A new ontology-based surgical workflow recognition and a novel prediction method are presented utilizing ontological reasoning, abstraction, and explication. This way, a surgical situation representation with combined phase, high-level task, and low-level task recognition and prediction was realized based on the currently used instrument as the only input information. RESULTS: The ontology-based approach performed efficiently, and decent accuracy was achieved for situation recognition and prediction. Especially during situation recognition, the missing sensor information were reasoned based on the situation representation provided by the process ontology, which resulted in improved recognition results compared to the state-of-the-art. CONCLUSIONS: In this work, a reference ontology was developed, which provides workflow support and a knowledge base for further applications in the intelligent operating room, for instance, context-aware medical device orchestration, (semi-) automatic documentation, and surgical simulation, education, and training.

[Evaluation of an integrated OR based on open standards in Cochlea Implant surgery]. / Evaluation eines integrierten OP-Saals auf Basis offener Standards in der Cochlea-Implantat-Chirurgie.

BACKGROUND: Digitalization in surgery makes it necessary to develop modern surgical concepts. New approaches to system networking with integration and open standardized communication of all medical devices are being pursued. METHODS: At the University Hospital Leipzig, a demonstration of the integrated OR was carried out together with the Innovation Center for Computer Assisted Surgery (ICCAS) using the example of a cochlea implantation. The preoperative management, technical preparation, surgical procedure and postoperative documentation by a total of n = 30 study participants (2 expert groups) were evaluated. In addition to the collection of objective parameters, qualitative questionnaires and quantitative, interval-scaled questions were used. RESULTS: Preoperatively, the digital presentation of the patient's clinical data was considered as helpful by both groups (group 1: median = 5, group 2: median = 4). This also applies to the personalized OR settings, the intraoperative display options and the dynamic, surgeon-centered visualization (median = 4). Similar positive conclusions were drawn for postoperative documentation and postoperative follow-up (median = 4). A significant difference in the final evaluation of the integrated surgical concept between the two expert groups could not be determined (p > 0.05). CONCLUSIONS: The positive study results show that the theoretical idea of system networking based on open standards can be successfully implemented in practice using the example of a cochlea implantation. Thus, the intelligent "operating room of the future" no longer seems to be a fictitious idea, but a realistic image of modern surgical medicine.

Modeling and processing up-to-dateness of patient information in probabilistic therapy decision support.

OBJECTIVES: Probabilistic modeling of a patient's situation with the goal of providing calculated therapy recommendations can improve the decision making of interdisciplinary teams. Relevant information entities and direct causal dependencies, as well as uncertainty, must be formally described. Possible therapy options, tailored to the patient, can be inferred from the clinical data using these descriptions. However, there are several avoidable factors of uncertainty influencing the accuracy of the inference. For instance, inaccuracy may emerge from outdated information. In general, probabilistic models, e.g. Bayesian Networks can depict the causality and relations of individual information entities, but in general cannot evaluate individual entities concerning their up-to-dateness. The goal of the work at hand is to model diagnostic up-to-dateness, which can reasonably adjust the influence of outdated diagnostic information to improve the inference results of clinical decision models. METHODS AND MATERIALS: We analyzed 68 laryngeal cancer cases and modeled the state of up-to-dateness of different diagnostic modalities. All cases were used for cross-validation. 55 cases were used to train the model, 13 for testing. Each diagnostic procedure involved in the decision making process of these cases was associated with a specific threshold for the time the information is considered up-to-date, i.e. reliable. Based on this threshold, outdated findings could be identified and their impact on probabilistic calculations could be reduced. We applied the model for reducing the weight of outdated patient data in the computation of TNM stagings for the 13 test cases and compared the results to the manually derived TNM stagings in the patient files. RESULTS: With the implementation of these weights in the laryngeal cancer model, we increased the accuracy of the TNM calculation from 0.61 (8 out of 13 cases correct) to 0.76 (10 out of 13 cases correct). CONCLUSION: Decision delay may cause specific patient data to be outdated. This can cause contradictory or false information and impair calculations for clinical decision support. Our approach demonstrates that the accuracy of Bayesian Network models can be improved when pre-processing the patient-specific data and evaluating their up-to-dateness with reduced weights on outdated information.

Extending BPMN 2.0 for intraoperative workflow modeling with IEEE 11073 SDC for description and orchestration of interoperable, networked medical devices.

PURPOSE: Surgical workflow management in integrated operating rooms (ORs) enables the implementation of novel computer-aided surgical assistance and new applications in process automation, situation awareness, and decision support. The context-sensitive configuration and orchestration of interoperable, networked medical devices is a prerequisite for an effective reduction in the surgeons' workload, by providing the right service and right information at the right time. The information about the surgical situation must be described as surgical process models and distributed to the medical devices and IT systems in the OR. Available modeling languages are not capable of describing surgical processes for this application. METHODS: In this work, the BPMNSIX modeling language for intraoperative processes is technically enhanced and implemented for workflow build-time and run-time. Therefore, particular attention is given to the integration of the recently published IEEE 11073 SDC standard family for a service-oriented architecture of networked medical devices. In addition, interaction patterns for context-aware configuration and device orchestration were presented. RESULTS: The identified interaction patterns were implemented in BPMNSIX for an ophthalmologic use case. Therefore, the examples of the process-driven incorporation and control of device services could be demonstrated. CONCLUSION: The modeling of surgical procedures with BPMNSIX allows the implementation of context-sensitive surgical assistance functionalities and enables flexibility in terms of the orchestration of dynamically changing device ensembles and integration of unknown devices in the surgical workflow management.

Enabling artificial intelligence in high acuity medical environments.

Acute patient treatment can heavily profit from AI-based assistive and decision support systems, in terms of improved patient outcome as well as increased efficiency. Yet, only very few applications have been reported because of the limited accessibility of device data due to the lack of adoption of open standards, and the complexity of regulatory/approval requirements for AI-based systems. The fragmentation of data, still being stored in isolated silos, results in limited accessibility for AI in healthcare and machine learning is complicated by the loss of semantics in data conversions. We outline a reference model that addresses the requirements of innovative AI-based research systems as well as the clinical reality. The integration of networked medical devices and Clinical Repositories based on open standards, such as IEEE 11073 SDC and HL7 FHIR, will foster novel assistance and decision support. The reference model will make point-of-care device data available for AI-based approaches. Semantic interoperability between Clinical and Research Repositories will allow correlating patient data, device data, and the patient outcome. Thus, complete workflows in high acuity environments can be analysed. Open semantic interoperability will enable the improvement of patient outcome and the increase of efficiency on a large scale and across clinical applications.

Activity of Spore-Specific Respiratory Nitrate Reductase 1 of Streptomyces coelicolor A3(2) Requires a Functional Cytochrome bcc-aa3 Oxidase Supercomplex.

Spores have strongly reduced metabolic activity and are produced during the complex developmental cycle of the actinobacterium Streptomyces coelicolor Resting spores can remain viable for decades, yet little is known about how they conserve energy. It is known, however, that they can reduce either oxygen or nitrate using endogenous electron sources. S. coelicolor uses either a cytochrome bd oxidase or a cytochrome bcc-aa3 oxidase supercomplex to reduce oxygen, while nitrate is reduced by Nar-type nitrate reductases, which typically oxidize quinol directly. Here, we show that in resting spores the Nar1 nitrate reductase requires a functional bcc-aa3 supercomplex to reduce nitrate. Mutants lacking the complete qcr-cta genetic locus encoding the bcc-aa3 supercomplex showed no Nar1-dependent nitrate reduction. Recovery of Nar1 activity was achieved by genetic complementation but only when the complete qcr-cta locus was reintroduced to the mutant strain. We could exclude that the dependence on the supercomplex for nitrate reduction was via regulation of nitrate transport. Moreover, the catalytic subunit, NarG1, of Nar1 was synthesized in the qcr-cta mutant, ruling out transcriptional control. Constitutive synthesis of Nar1 in mycelium revealed that the enzyme was poorly active in this compartment, suggesting that the Nar1 enzyme cannot act as a typical quinol oxidase. Notably, nitrate reduction by the Nar2 enzyme, which is active in growing mycelium, was not wholly dependent on the bcc-aa3 supercomplex for activity. Together, our data suggest that Nar1 functions together with the proton-translocating bcc-aa3 supercomplex to increase the efficiency of energy conservation in resting spores.IMPORTANCEStreptomyces coelicolor forms spores that respire with either oxygen or nitrate, using only endogenous electron donors. This helps maintain a membrane potential and, thus, viability. Respiratory nitrate reductase (Nar) usually receives electrons directly from reduced quinone species; however, we show that nitrate respiration in spores requires a respiratory supercomplex comprising cytochrome bcc oxidoreductase and aa3 oxidase. Our findings suggest that the Nar1 enzyme in the S. coelicolor spore functions together with the proton-translocating bcc-aa3 supercomplex to help maintain the membrane potential more efficiently. Dissecting the mechanisms underlying this survival strategy is important for our general understanding of bacterial persistence during infection processes and of how bacteria might deal with nutrient limitation in the natural environment.

Desulfitobacterium contributes to the microbial transformation of 2,4,5-T by methanogenic enrichment cultures from a Vietnamese active landfill.

The herbicide 2,4,5-trichlorophenoxyacetic acid (2,4,5-T) was a major component of Agent Orange, which was used as a defoliant in the Vietnam War. Little is known about its degradation under anoxic conditions. Established enrichment cultures using soil from an Agent Orange bioremediation plant in southern Vietnam with pyruvate as potential electron donor and carbon source were shown to degrade 2,4,5-T via ether cleavage to 2,4,5-trichlorophenol (2,4,5-TCP), which was further dechlorinated to 3,4-dichlorophenol. Pyruvate was initially fermented to hydrogen, acetate and propionate. Hydrogen was then used as the direct electron donor for ether cleavage of 2,4,5-T and subsequent dechlorination of 2,4,5-TCP. 16S rRNA gene amplicon sequencing indicated the presence of bacteria and archaea mainly belonging to the Firmicutes, Bacteroidetes, Spirochaetes, Chloroflexi and Euryarchaeota. Desulfitobacterium hafniense was identified as the dechlorinating bacterium. Metaproteomics of the enrichment culture indicated higher protein abundances of 60 protein groups in the presence of 2,4,5-T. A reductive dehalogenase related to RdhA3 of D. hafniense showed the highest fold change, supporting its function in reductive dehalogenation of 2,4,5-TCP. Despite an ether-cleaving enzyme not being detected, the inhibition of ether cleavage but not of dechlorination, by 2-bromoethane sulphonate, suggested that the two reactions are catalysed by different organisms.

The intelligent OR: design and validation of a context-aware surgical working environment.

PURPOSE: Interoperability of medical devices based on standards starts to establish in the operating room (OR). Devices share their data and control functionalities. Yet, the OR technology rarely implements cooperative, intelligent behavior, especially in terms of active cooperation with the OR team. Technical context-awareness will be an essential feature of the next generation of medical devices to address the increasing demands to clinicians in information seeking, decision making, and human-machine interaction in complex surgical working environments. METHODS: The paper describes the technical validation of an intelligent surgical working environment for endoscopic ear-nose-throat surgery. We briefly summarize the design of our framework for context-aware system's behavior in integrated OR and present example realizations of novel assistance functionalities. In a study on patient phantoms, twenty-four procedures were implemented in the proposed intelligent surgical working environment based on recordings of real interventions. Subsequently, the whole processing pipeline for context-awareness from workflow recognition to the final system's behavior is analyzed. RESULTS: Rule-based behavior that considers multiple perspectives on the procedure can partially compensate recognition errors. A considerable robustness could be achieved with a reasonable quality of the recognition. Overall, reliable reactive as well as proactive behavior of the surgical working environment can be implemented in the proposed environment. CONCLUSIONS: The obtained validation results indicate the suitability of the overall approach. The setup is a reliable starting point for a subsequent evaluation of the proposed context-aware assistance. The major challenge for future work will be to implement the complex approach in a cross-vendor setting.

From SOMDA to application - integration strategies in the OR.NET demonstration sites.

The effective development and dissemination of the open integration for the next generation of operating rooms require a comprehensive testing environment. In this paper, we present the various challenges to be addressed in demonstration applications, and we discuss the implementation approach, the foci of the demonstration sites and the evaluation efforts. Overall, the demonstrator setups have proven the feasibility of the service-oriented medical device architecture (SOMDA) and real-time approaches with a large variety of example applications. The applications demonstrate the potentials of open device interoperability. The demonstrator implementations were technically evaluated as well as discussed with many clinicians from various disciplines. However, the evaluation is still an ongoing research at the demonstration sites. Technical evaluation focused on the properties of a network of medical devices, latencies in data transmission and stability. A careful evaluation of the SOMDA design decisions and implementations are essential to a safe and reliable interoperability of integrated medical devices and information technology (IT) system in the especially critical working environment. The clinical evaluation addressed the demands of future users and stakeholders, especially surgeons, anesthesiologists, scrub nurses and hospital operators. The opinions were carefully collected to gain further insights into the potential benefits of the technology and pitfalls in future work.

OR.NET: a service-oriented architecture for safe and dynamic medical device interoperability.

Modern surgical departments are characterized by a high degree of automation supporting complex procedures. It recently became apparent that integrated operating rooms can improve the quality of care, simplify clinical workflows, and mitigate equipment-related incidents and human errors. Particularly using computer assistance based on data from integrated surgical devices is a promising opportunity. However, the lack of manufacturer-independent interoperability often prevents the deployment of collaborative assistive systems. The German flagship project OR.NET has therefore developed, implemented, validated, and standardized concepts for open medical device interoperability. This paper describes the universal OR.NET interoperability concept enabling a safe and dynamic manufacturer-independent interconnection of point-of-care (PoC) medical devices in the operating room and the whole clinic. It is based on a protocol specifically addressing the requirements of device-to-device communication, yet also provides solutions for connecting the clinical information technology (IT) infrastructure. We present the concept of a service-oriented medical device architecture (SOMDA) as well as an introduction to the technical specification implementing the SOMDA paradigm, currently being standardized within the IEEE 11073 service-oriented device connectivity (SDC) series. In addition, the Session concept is introduced as a key enabler for safe device interconnection in highly dynamic ensembles of networked medical devices; and finally, some security aspects of a SOMDA are discussed.

Connecting the clinical IT infrastructure to a service-oriented architecture of medical devices.

The new medical device communication protocol known as IEEE 11073 SDC is well-suited for the integration of (surgical) point-of-care devices, so are the established Health Level Seven (HL7) V2 and Digital Imaging and Communications in Medicine (DICOM) standards for the communication of systems in the clinical IT infrastructure (CITI). An integrated operating room (OR) and other integrated clinical environments, however, need interoperability between both domains to fully unfold their potential for improving the quality of care as well as clinical workflows. This work thus presents concepts for the propagation of clinical and administrative data to medical devices, physiologic measurements and device parameters to clinical IT systems, as well as image and multimedia content in both directions. Prototypical implementations of the derived components have proven to integrate well with systems of networked medical devices and with the CITI, effectively connecting these heterogeneous domains. Our qualitative evaluation indicates that the interoperability concepts are suitable to be integrated into clinical workflows and are expected to benefit patients and clinicians alike. The upcoming HL7 Fast Healthcare Interoperability Resources (FHIR) communication standard will likely change the domain of clinical IT significantly. A straightforward mapping to its resource model thus ensures the tenability of these concepts despite a foreseeable change in demand and requirements.

3,7-Isoquinoline quinones from the ascidian tunicate Ascidia virginea.

A new isoquinoline quinone system and its iodinated derivatives were isolated from the ascidian tunicate Ascidia virginea Müller 1776 (Phlebobranchia: Ascidiidae). Structures were elucidated by spectroscopic methods and derivatization reactions. Ascidine A (3,7-dihydro-1,8-dihydroxy-4-(4'-hydroxyphenyl)isoquinoline-3,7-dione (1), ascidine B (3,7-dihydro-1,8-dihydroxy-4-(4'-hydroxy-3'-iodophenyl)isoquinoline-3,7-dione (2), and ascidine C (3,7-dihydro-1,8-dihydroxy-4-(4'-hydroxy-3',5'-diiodophenyl)isoquinoline-3,7-dione (3) represent a novel type of tyrosine-derived alkaloids.

Video-based detection of device interaction in the operating room.

The establishment of modern workflow management technologies requires the integration of dated devices. The extraction of the essential device data and usage time spans is a central requirement for an integrated OR environment. Therefore, methods are required that extract such information from the output provided by older generation devices, namely video signals. We developed a four-level approach for video-based device information extraction. Usually, video streams contain all relevant patient data and device usage information. We propose an approach consisting of defining regions of interest, grabbing video signals, analyzing the signals and storing the data in a centralized and structured location. The analysis considers textual information and graphical visualization. A prototype of the analysis approach was implemented and applied to a neurosurgical case. An evaluation study was conducted to measure the performance of the approach on video recordings of real interventions. Three medical devices were considered: intraoperative ultrasound, neuro-navigation and microscope. Overall, recognition rates for device usage higher than 95% were obtained. The approach is not limited to a single surgical discipline and does not require modification of medical devices. Furthermore, the analysis of microscopic video streams expands the detectable aspects of the surgical workflow beyond the recognition of device usage.

Multi-perspective workflow modeling for online surgical situation models.

INTRODUCTION: Surgical workflow management is expected to enable situation-aware adaptation and intelligent systems behavior in an integrated operating room (OR). The overall aim is to unburden the surgeon and OR staff from both manual maintenance and information seeking tasks. A major step toward intelligent systems behavior is a stable classification of the surgical situation from multiple perspectives based on performed low-level tasks. MATERIAL AND METHODS: The present work proposes a method for the classification of surgical situations based on multi-perspective workflow modeling. A model network that interconnects different types of surgical process models is described. Various aspects of a surgical situation description were considered: low-level tasks, high-level tasks, patient status, and the use of medical devices. A study with sixty neurosurgical interventions was conducted to evaluate the performance of our approach and its robustness against incomplete workflow recognition input. RESULTS: A correct classification rate of over 90% was measured for high-level tasks and patient status. The device usage models for navigation and neurophysiology classified over 95% of the situations correctly, whereas the ultrasound usage was more difficult to predict. Overall, the classification rate decreased with an increasing level of input distortion. DISCUSSION: Autonomous adaptation of medical devices and intelligent systems behavior do not currently depend solely on low-level tasks. Instead, they require a more general type of understanding of the surgical condition. The integration of various surgical process models in a network provided a comprehensive representation of the interventions and allowed for the generation of extensive situation descriptions. CONCLUSION: Multi-perspective surgical workflow modeling and online situation models will be a significant pre-requisite for reliable and intelligent systems behavior. Hence, they will contribute to a cooperative OR environment.

Rule-based medical device adaptation for the digital operating room.

A workflow-driven cooperative operating room needs to be established in order to successfully unburden the surgeon and the operating room staff very time-consuming information-seeking and configuration tasks. We propose an approach towards the integration of intraoperative surgical workflow management and integration technologies. The concept of rule-based behavior is adapted to situation-aware medical devices. A prototype was implemented and experiments with sixty recorded brain tumor removal procedures were conducted to test the proposed approach. An analysis of the recordings indicated numerous applications, such as automatic display configuration, room light adaptation and pre-configuration of medical devices and systems.

Requirements for the structured recording of surgical device data in the digital operating room.

PURPOSE: Due to the increasing complexity of the surgical working environment, increasingly technical solutions must be found to help relieve the surgeon. This objective is supported by a structured storage concept for all relevant device data. METHODS: In this work, we present a concept and prototype development of a storage system to address intraoperative medical data. The requirements of such a system are described, and solutions for data transfer, processing, and storage are presented. In a subsequent study, a prototype based on the presented concept is tested for correct and complete data transmission and storage and for the ability to record a complete neurosurgical intervention with low processing latencies. In the final section, several applications for the presented data recorder are shown. RESULTS: The developed system based on the presented concept is able to store the generated data correctly, completely, and quickly enough even if much more data than expected are sent during a surgical intervention. CONCLUSIONS: The Surgical Data Recorder supports automatic recognition of the interventional situation by providing a centralized data storage and access interface to the OR communication bus. In the future, further data acquisition technologies should be integrated. Therefore, additional interfaces must be developed. The data generated by these devices and technologies should also be stored in or referenced by the Surgical Data Recorder to support the analysis of the OR situation.

Design of a modular protein-based MRI contrast agent for targeted application.

Magnetic resonance imaging (MRI) offers a non-radioactive alternative for the non-invasive detection of tumours. Low molecular weight MRI contrast agents currently in clinical use suffer either from a lack of specificity for tumour tissue or from low relaxivity and thus low contrast amplification. In this study, we present the newly designed two domain fusion protein Zarvin, which is able to bind to therapeutic IgG antibodies suitable for targeting, while facilitating contrast enhancement through high affinity binding sites for Gd(3+). We show that the Zarvin fold is stable under serum conditions, specifically targets a cancer cell-line when bound to the Cetuximab IgG, and allows for imaging with high relaxivity, a property that would be advantageous for the detection of small tumours and metastases at 1.5 or 3 T.