Background: Cost-effective methods to facilitate practical medical education are in high demand and the “mixed-reality” (MR) technology seems suitable to provide students with instructions when learning a new practical task. To evaluate a step-by-step mixed reality (MR) guidance system for instructing a practical medical procedure, we conducted a randomized, single-blinded prospective trial on medical students learning bladder catheter placement. Methods: We enrolled 164 medical students. Students were randomized into 2 groups and received instructions on how to perform bladder catheter placement on a male catheterization training model. One group (107 students) were given their instructions by an instructor, while the other group (57 students) were instructed via an MR guidance system using a Microsoft HoloLens. Both groups did hands on training. A standardized questionnaire covering previous knowledge, interest in modern technologies and a self-evaluation was filled out. In addition, students were asked to evaluate the system’s usability. We assessed both groups’s learning outcome via a standardized OSCE (objective structured clinical examination). Results: Our evaluation of the learning outcome revealed an average point value of 19.96 ± 2,42 for the control group and 21.49 ± 2.27 for the MR group - the MR group’s result was significantly better (p = 0.00). The self-evaluations revealed no difference between groups, however, the control group gave higher ratings when evaluating the quality of instructions. The MR system’s assessment showed less usability, with a cumulative SUS (system usability scale) score of 56.6 (lower half) as well as a cumulative score of 24.2 ± 7.3 (n = 52) out of 100 in the NASA task load index. Conclusions: MR is a promising tool for instructing practical skills, and has the potential to enable superior learning outcomes. Advances in MR technology are necessary to improve the usability of current systems. Trial registration: German Clinical Trial Register ID: DRKS00013186

We developed a new mobile ultrasound device for long-term and automated bladder monitoring without user interaction consisting of 32 transmit and receive electronics as well as a 32-element phased array 3 MHz transducer. The device architecture is based on data digitization and rapid transfer to a consumer electronics device (e.g., a tablet) for signal reconstruction (e.g., by means of plane wave compounding algorithms) and further image processing. All reconstruction algorithms are implemented in the GPU, allowing real-time reconstruction and imaging. The system and the beamforming algorithms were evaluated with respect to the imaging performance on standard sonographical phantoms (CIRS multipurpose ultrasound phantom) by analyzing the resolution, the SNR and the CNR. Furthermore, ML-based segmentation algorithms were developed and assessed with respect to their ability to reliably segment human bladders with different filling levels. A corresponding CNN was trained with 253 B-mode data sets and 20 B-mode images were evaluated. The quantitative and qualitative results of the bladder segmentation are presented and compared to the ground truth obtained by manual segmentation.

Background: Mapping out the research landscape around a project is often time consuming and difficult. Objective: This study evaluates a commercial artificial intelligence (AI) search engine (IRIS.AI) for its applicability in an automated literature search on a specific medical topic. Methods: To evaluate the AI search engine in a standardized manner, the concept of a science hackathon was applied. Three groups of researchers were tasked with performing a literature search on a clearly defined scientific project. All participants had a high level of expertise for this specific field of research. Two groups were given access to the AI search engine IRIS.AI. All groups were given the same amount of time for their search and were instructed to document their results. Search results were summarized and ranked according to a predetermined scoring system. Results: The final scoring awarded 49 and 39 points out of 60 to AI groups 1 and 2, respectively, and the control group received 46 points. A total of 20 scientific studies with high relevance were identified, and 5 highly relevant studies (“spot on”) were reported by each group. Conclusions: AI technology is a promising approach to facilitate literature searches and the management of medical libraries. In this study, however, the application of AI technology lead to a more focused literature search without a significant improvement in the number of results.

Background: To review and discuss the literature on applying tyrosine kinase inhibitors (TKIs) in the treatment of metastasised renal cell carcinoma (mRCC). Materials and Methods: Medline, PubMed, the Cochrane database, and Embase were screened for randomised controlled trials, clinical trials, and reviews on treating renal cell carcinoma, and the role of TKI. Each substance’s results were summarised descriptively. Results: While TKI monotherapy is not currently recommended as a first-line treatment for metastasized renal cell carcinoma, TKIs are regularly applied to treat treatment-naïve patients in combination with immunotherapy. TKIs depict the first-choice alternative therapy if immunotherapy is not tolerated or inapplicable. Currently, seven different TKIs are available to treat mRCC. Conclusions: The importance of TKIs in a monotherapeutic approach has declined in the past few years. The current trend toward combination therapy for mRCC, however, includes TKIs as one significant component of treatment regimens. We found that to remain applicable to ongoing studies, both when including new substances and when testing novel combinations of established drugs. TKIs are of major importance for the treatment of renal cancer now, as well as for the foreseeable future.