Heinz U. Lemke

Acquisition of process descriptions from surgical interventions

The recording and analysis of process descriptions from running surgical interventions is a very new and promising field named Surgical Workflows. Surgical Workflows fulfill two major objectives: they form the base of scientific evaluation and rapid prototyping of surgical assist systems, and they pave the road for the entering of workflow management systems into the operating room for intraoperative support of the surgeon. In this paper we describe how process descriptions from surgical interventions can be obtained for Surgical Process Modelling (SPM) as a specific domain of Business Process Modelling (BPM). After the introduction into the field of Surgical Workflows and the motivation of the research efforts, we deal with theoretical considerations about surgical interventions and the identification of classifications. Based on that, we propose the extendable structure for computational data acquisition support and conclude with use cases. The presented approach was applied to more than 200 surgical interventions of 10 different intervention types from otorhinolaryngology, neurosurgery, heart surgery, eye surgery, and interventional radiology, and it represents an ongoing project.

Validation workflow for a clinical Bayesian network model in multidisciplinary decision making in head and neck oncology treatment

PurposeOncological treatment is being increasingly complex, and therefore, decision making in multidisciplinary teams is becoming the key activity in the clinical pathways. The increased complexity is related to the number and variability of possible treatment decisions that may be relevant to a patient. In this paper, we describe validation of a multidisciplinary cancer treatment decision in the clinical domain of head and neck oncology.MethodProbabilistic graphical models and corresponding inference algorithms, in the form of Bayesian networks, can support complex decision-making processes by providing a mathematically reproducible and transparent advice. The quality of BN-based advice depends on the quality of the model. Therefore, it is vital to validate the model before it is applied in practice.ResultsFor an example BN subnetwork of laryngeal cancer with 303 variables, we evaluated 66 patient records. To validate the model on this dataset, a validation workflow was applied in combination with quantitative and qualitative analyses. In the subsequent analyses, we observed four sources of imprecise predictions: incorrect data, incomplete patient data, outvoting relevant observations, and incorrect model. Finally, the four problems were solved by modifying the data and the model.ConclusionThe presented validation effort is related to the model complexity. For simpler models, the validation workflow is the same, although it may require fewer validation methods. The validation success is related to the model’s well-founded knowledge base. The remaining laryngeal cancer model may disclose additional sources of imprecise predictions.

Visual Verification of Cancer Staging for Therapy Decision Support

It is generally accepted practice that each cancer patient case should be discussed in a clinical expert meeting, the so‐called tumor board. A central role in finding the best therapy options for patients with solid tumors plays the Tumor, lymph Node, and Metastasis staging (TNM staging). Correctness of TNM staging has a significant impact on the therapy choice and hence on the patients post‐therapeutic quality of life or even survival. If inconsistencies in the TNM staging occur, possible explanations and solutions must be found based on the complex patient records, which takes the costly time of (multiple) physicians. We propose a more efficient visual analysis component, which supports a physician in verifying the given TNM staging before forwarding it to the tumor board. Our component comprises a Bayesian network model of the TNM staging process. Using information from the patient records and Bayesian inference, the models computes a patient‐specific TNM staging, which is then explored and compared to the given staging by means of a graph‐based visualization. Our component is implemented in a research prototype that supports an understanding of the model computations, allows for a fast identification of important influencing factors, and facilitates a quick detection of differences between two TNM stagings. We evaluated our component with five physicians, each studying 20 cases of laryngeal cancer.

Real Time Issues for usage of Vision and Image Data in the Future Operating Room

The operating room (OR) is more and more equipped with surgical assist systems which make use of modern image acquisition and processing technologies. Within this scenario, the surgeon has to deal with a large amount of information which must be available at the right time and in the right place. This results in various real-time problems which must be addressed while building surgical assist systems. The components of such systems and their real time requirements are presented in this paper. Several concrete systems are presented and their real time properties discussed