Quality-dependent Deep Learning for Safe Autonomous Guidewire Navigation

Cardiovascular diseases are the main cause ofdeath worldwide. State-of-the-art treatment often includes theprocess of navigating endovascular instruments through thevasculature. Automation of the procedure receives much at-tention lately and may increase treatment quality and unburdenclinicians. However, in order to ensure the patient’s safety theendovascular device needs to be steered carefully through thebody. In this work, we present a collection of medical criteriathat are considered by physicians during an intervention andthat can be evaluated automatically enabling a highly objectiveassessment. Additionally, we trained an autonomous controllerwith deep reinforcement learning to gently navigate within a2D simulation of an aortic arch. Among others, the controllerreduced the maximum and mean contact force applied to thevessel walls by 43% and 29%, respectively

Respiration parameter determination with non-obstructive methods

Measuring respiratory parameters like the breathing frequency or the tidal volume is essential in intensive care to ensure an optimal and lung protecting ventilation. A common practice in artificial ventilation of sensitive patients like infants or neonates is the use of uncuffed endotracheal tubes in combination with continuous positive airway pressure (CPAP). This comes with the disadvantage of an unknown leakage making it difficult to detect spontaneous breathing or to measure the tidal volume reliable. A novel non-obstructive method to determine respiratory parameters as well as dynamic changes of thoracic parameters has recently been presented and uses a pair of coupled UHF (ultra high frequency) antennae. In this paper, a respective setup is investigated numerically using finite difference time domain method and experimentally using an artificial lung phantom. Both approaches show that the investigated method seems capable of allowing a contactless triggering to synchronize natural and artificial breathing. The results are compared to derive a better understanding of influencing factors and opportunities for an optimisation

Learning-based autonomous vascular guidewire navigation without human demonstration in the venous system of a porcine liver

Purpose The navigation of endovascular guidewires is a dexterous task where physicians and patients can benefit from automation. Machine learning-based controllers are promising to help master this task. However, human-generated training data are scarce and resource-intensive to generate. We investigate if a neural network-based controller trained without human-generated data can learn human-like behaviors. Methods We trained and evaluated a neural network-based controller via deep reinforcement learning in a finite element simulation to navigate the venous system of a porcine liver without human-generated data. The behavior is compared to manual expert navigation, and real-world transferability is evaluated. Results The controller achieves a success rate of 100% in simulation. The controller applies a wiggling behavior, where the guidewire tip is continuously rotated alternately clockwise and counterclockwise like the human expert applies. In the ex vivo porcine liver, the success rate drops to 30%, because either the wrong branch is probed, or the guidewire becomes entangled. Conclusion In this work, we prove that a learning-based controller is capable of learning human-like guidewire navigation behavior without human-generated data, therefore, mitigating the requirement to produce resource-intensive human-generated training data. Limitations are the restriction to one vessel geometry, the neglected safeness of navigation, and the reduced transferability to the real world

TissueGrinder, a novel technology for rapid generation of patient-derived single cell suspensions from solid tumors by mechanical tissue dissociation

IntroductionRecent advances hold promise of making personalized medicine a step closer to implementation in clinical settings. However, traditional sample preparation methods are not robust and reproducible. In this study, the TissueGrinder, a novel mechanical semi-automated benchtop device, which can isolate cells from tissue in a very fast and enzyme-free way is tested for cell isolation from surgically resected tumor tissues.MethodsThirty-three surgically resected tumor tissues from various but mainly pancreatic, liver or colorectal origins were processed by both novel TissueGrinder and explant method. An optimized processing program for tumors from pancreatic, liver or colorectal cancer was developed. The viability and morphological characteristics of the isolated cells were evaluated microscopically. Expression of pancreatic cancer markers was evaluated in cells isolated from pancreatic tumors. Finally, the effect of mechanical stress on the cells was evaluated by assessing apoptosis markers via western blotting.ResultsTissueGinder was more efficient in isolating cells from tumor tissue with a success rate of 75% when compared to explant method 45% in terms of cell outgrowth six weeks after processing. Cells isolated with TissueGinder had a higher abundance and were more heterogeneous in composition as compared to explant method. Mechanical processing of the cells with TissueGrinder does not lead to apoptosis but causes slight stress to the cells.DiscussionOur results show that TissueGrinder can process solid tumor tissues more rapidly and efficiently and with higher success rate compared to the conventionally used explant method. The results of the study suggest that the TissueGrinder might be a suitable method for obtaining cells, which is important for its application in individualized therapy. Due to the great variance in different tumor entities and the associated individual tissue characteristics, a further development of the dissociation protocol for other types of tumors and normal tissue will be targeted

Entwicklung und Realisierung kompakter Messsysteme zur quantitativen Detektion von organischen Spurengasen in Luft

[no abstract

A step towards enzyme-free tissue dissociation

The future of personalized diagnostics commences on the single cell level. Even high-end technologies like Next Generation Sequencing can be improved if applied on pure single cell populations (e.g., tumor cells without contaminating stromal cells) or on a single cell level (DNA/RNA sequencing). The vast majority of these technologies need individual and preferably undistorted cells for the analytical process. Thus, decisive prerequisite for high-end analytics is to remove cells from their tissue matrix as gently as possible. This can be accomplished by an enzyme-free, fast and reproducible approach of generating pure and individual single cells from tissue samples. In this study we demonstrate the utility of a semi-automated Tissue Grinder that is compatible with standard 50 ml centrifuge tubes and standard cell strainer for mechanically, nonenzymatic and parallel processing of tissue samples. We show that without enzymatic treatment viable single-cell yields match or exceed reference enzymatic methods, while reducing processing time by at least 80%

A novel non-invasive, non-conductive method for measuring respiration

Accompanying datasets to the article "A novel non-invasive, non-conductive method for measuring Respiration" published in the Journal of Sensors and Sensor Systems by the same authors as this data sets

A benchmarking protocol for breath analysis: The peppermint experiment

International audienceSampling of volatile organic compounds (VOCs) has shown promise for detection of a range of diseases but results have proved hard to replicate due to a lack of standardization. In this work we introduce the 'Peppermint Initiative'. The initiative seeks to disseminate a standardized experiment that allows comparison of breath sampling and data analysis methods. Further, it seeks to share a set of benchmark values for the measurement of VOCs in breath. Pilot data are presented to illustrate the standardized approach to the interpretation of results obtained from the Peppermint experiment. This pilot study was conducted to determine the washout profile of peppermint compounds in breath, identify appropriate sampling time points, and formalise the data analysis. Five and ten participants were recruited to undertake a standardized intervention by ingesting a peppermint oil capsule that engenders a predictable and controlled change in the VOC profile in exhaled breath. After collecting a pre-ingestion breath sample, five further samples are taken at 2, 4, 6, 8, and 10 h after ingestion. Samples were analysed using ion mobility spectrometry coupled to multi-capillary column and thermal desorption gas chromatography mass spectrometry. A regression analysis of the washout data was used to determine sampling times for the final peppermint protocol, and the time for the compound measurement to return to baseline levels was selected as a benchmark value. A measure of the quality of the data generated from a given technique is proposed by comparing data fidelity. This study protocol has been used for all subsequent measurements by the Peppermint Consortium (16 partners from seven countries). So far 1200 breath samples from 200 participants using a range of sampling and analytical techniques have been collected. The data from the consortium will be disseminated in subsequent technical notes focussing on results from individual platforms