Towards Automated COVID-19 Presence and Severity Classification.

COVID-19 presence classification and severity prediction via (3D) thorax computed tomography scans have become important tasks in recent times. Especially for capacity planning of intensive care units, predicting the future severity of a COVID-19 patient is crucial. The presented approach follows state-of-theart techniques to aid medical professionals in these situations. It comprises an ensemble learning strategy via 5-fold cross-validation that includes transfer learning and combines pre-trained 3D-versions of ResNet34 and DenseNet121 for COVID19 classification and severity prediction respectively. Further, domain-specific preprocessing was applied to optimize model performance. In addition, medical information like the infection-lung-ratio, patient age, and sex were included. The presented model achieves an AUC of 79.0% to predict COVID-19 severity, and 83.7% AUC to classify the presence of an infection, which is comparable with other currently popular methods. This approach is implemented using the AUCMEDI framework and relies on well-known network architectures to ensure robustness and reproducibility.

Multipotent Systems: Combining Planning, Self-Organization, and Reconfiguration in Modular Robot Ensembles.

Mobile multirobot systems play an increasing role in many disciplines. Their capabilities can be used, e.g., to transport workpieces in industrial applications or to support operational forces in search and rescue scenarios, among many others. Depending on the respective application, the hardware design and accompanying software of mobile robots are of various forms, especially for integrating different sensors and actuators. Concerning this design, robots of one system compared to each other can be classified to exclusively be either homogeneous or heterogeneous, both resulting in different system properties. While homogeneously configured systems are known to be robust against failures through redundancy but are highly specialized for specific use cases, heterogeneously designed systems can be used for a broad range of applications but suffer from their specialization, i.e., they can only hardly compensate for the failure of one specialist. Up to now, there has been no known approach aiming to unify the benefits of both these types of system. In this paper, we present our approach to filling this gap by introducing a reference architecture for mobile robots that defines the interplay of all necessary technologies for achieving this goal. We introduce the class of robot systems implementing this architecture as multipotent systems that bring together the benefits of both system classes, enabling homogeneously designed robots to become heterogeneous specialists at runtime. When many of these robots work together, we call the structure of this cooperation an ensemble. To achieve multipotent ensembles, we also integrate reconfigurable and self-descriptive hardware (i.e., sensors and actuators) in this architecture, which can be freely combined to change the capabilities of robots at runtime. Because typically a high degree of autonomy in such systems is a prerequisite for their practical usage, we also present the integration of necessary mechanisms and algorithms for achieving the systems' multipotency. We already achieved the first results with robots implementing our approach of multipotent systems in real-world experiments as well as in a simulation environment, which we present in this paper.

Verification of medical guidelines in KIV.

We propose to use computerised medical guidelines as models for verification tools, so they can be validated with medical properties. To test the applicability we provide an implementation of the semantics of the medical planning language Asbru and also provide a formalised guideline for the treatment of breast cancer. With this case study we conduct experiments testing different proof techniques to cope with several challenges which guidelines provide.

Improving medical protocols by formal methods.

OBJECTIVES: During the last decade, evidence-based medicine has given rise to an increasing number of medical practice guidelines and protocols. However, the work done on developing and distributing protocols outweighs the efforts on guaranteeing their quality. Indeed, anomalies like ambiguity and incompleteness are frequent in medical protocols. Recent efforts have tried to address the problem of protocol improvement, but they are not sufficient since they rely on informal processes and notations. Our objective is to improve the quality of medical protocols. APPROACH: The solution we suggest to the problem of quality improvement of protocols consists in the utilisation of formal methods. It requires the definition of an adequate protocol representation language, the development of techniques for the formal analysis of protocols described in that language and, more importantly, the evaluation of the feasibility of the approach based on the formalisation and verification of real-life medical protocols. For the first two aspects we rely on earlier work from the fields of knowledge representation and formal methods. The third aspect, i.e. the evaluation of the use of formal methods in the quality improvement of protocols, constitutes our main objective. The steps with which we have carried out this evaluation are the following: (1) take two real-life reference protocols which cover a wide variety of protocol characteristics; (2) formalise these reference protocols; (3) check the formalisation for the verification of interesting protocol properties; and (4) determine how many errors can be uncovered in this way. RESULTS: Our main results are: a consolidated formal language to model medical practice protocols; two protocols, each both modelled and formalised; a list of properties that medical protocols should satisfy; verification proofs for these protocols and properties; and perspectives of the potentials of this approach. Our results have been evaluated by a panel of medical experts, who judged that the problems we detected in the protocols with the help of formal methods were serious and should be avoided. CONCLUSIONS: We have succeeded in demonstrating the feasibility of formal methods for improving medical protocols.

Protocure: supporting the development of medical protocols through formal methods.

Medical guidelines and protocols describe the optimal care for a specific group of patients and therefore, when properly applied, improve the quality of patient care. During the last decade, a large number of medical guidelines and protocols have been published. However, the work done on developing and disseminating them far outweighs the efforts on guaranteeing their quality. Indeed, anomalies like ambiguity and incompleteness are frequent in medical guidelines and protocols. An approach grounded on a formal representation, can answer these needs, as we have demonstrated in the Protocure project'. The Protocure II project will aim at integrating formal methods in the life cycle of guidelines.