Open Access

Background: Wideband acoustic immittance (WAI) and wideband tympanometry (WBT) are promising approaches to improve diagnosis accuracy in middle-ear diagnosis, though due to significant interindividual difference, their analysis and interpretation remains challenging. Recent approaches have come up, implementing machine learning (ML) or deep learning classifiers trained with measured WAI or WBT data for the classification of otitis media or otosclerosis. Also, first approaches have been made in identifying important regions from the WBT data, which the classifiers used for their decision-making. Methods: Two classifiers, a convolutional neural network (CNN) and the ML algorithm extreme gradient boosting (XGB), are trained on artificial data obtained with a finite-element ear model providing the middle-ear measurements energy reflectance (ER), pressure reflectance phase, impedance amplitude and phase. The performance of both classifiers is evaluated by cross-validation on artificial test data and by classification of real measurement data from the literature using the metrics macro-recall and macro-F1 score. The feature contributions are quantified using the feature importance ‘gain’ for XGB and deep Taylor decomposition for CNN. Results: In the cross-validation with artificial data, the macro-recall and macro-F1 scores are similar, namely 91.2% for XGB and 94.5% for CNN. For the classification with real measurement data the macro-recall and macro-F1-score were 81.8% and 38.2% (XGB) and 81.0% and 54.8% (CNN), respectively. The key features identified are ER between 600–1,000 Hz together with impedance phase between 600–1,000 Hz for XGB and ER up to 1,500 Hz for CNN. Conclusions: We were able to show that the applied classifiers CNN and XGB trained with simulated data lead to a reasonably well performance on real data. We conclude that using simulation-based WAI data can be a successful strategy for classifier training and that XGB can be applied to WAI data. Furthermore, ML interpretability algorithms are useful to identify relevant key features for differential diagnosis and to increase confidence in classifier decisions. Further evaluation using more measured data, especially for pathological cases, is essential.

Aim: The primary objective of this study was to examine and explain the public panic consumption model based on the stimulus–organism–response theory, during the peak time period of the COVID-19 pandemic in China. Subject and methods: The research data were collected through the questionnaires adapted for the purpose of this survey, which included a total of 408 participants (33% female) from the global population. The stepwise regression analysis has been conducted. Results: The results have shown that both physical social networks and online social networks have a significant positive impact on infection risk perception, but the impact of physical social networks has proved to be a better predictor. Infection information obtained from physical social networks affects conformity buying and uncontrolled self-medication if the perceived risk perception is higher. When it comes to the impact of risk perception on conformity buying, health change has a significant negative regulatory effect. In the impact of risk perception on the uncontrolled self-medication, health change has no significant regulatory effect. Conclusions: During the recent epidemic states, it is noticeable that public panic consumption has aggravated the difficulty of emergency management. Especially when it comes to the management of medical materials and medicines. In order to tackle this challenge in the future, the trigger mechanisms behind panic consumption are revealed.

Einleitung: Nach jedem chirurgischen Eingriff ist die Erstellung eines individuellen OP-Berichtes Aufgabe des Operateurs. Dies geschieht häufig mit zeitlicher Verzögerung und aus der Erinnerung heraus, was zu einer fehlerhaften Dokumentation führen kann. Die semiautomatische Erfassung, systematische Speicherung und Textverarbeitung aller intraoperativen Daten würde nicht nur eine Zeitersparnis für den Operateur ermöglichen, sondern auch die Grundlage für Qualitätssicherung und zukünftige Datenanalysen schaffen. Methode: In Zusammenarbeit mit der Medizininformatik der Hochschule Reutlingen wurde ein Prototyp eines halbautomatischen Checklisten-Tools am Beispiel der Cochlea Implantation entwickelt. Grundlage hierfür stellt ein BPMN-Modell (Business Process Model and Notation) dar, das anhand einer Workflowanalyse des chirurgischen Prozesses erstellt wurde. Basierend auf diesem werden automatisch generierte, dynamische Checklisten über eine Benutzeroberfläche auf einem Android-Tablet dargestellt. Die Anwendbarkeit im operativen Setting, die Steuerung über verschiedene Eingabegeräte und Überprüfung der inhaltlichen Prozessschritte wurden an einem Phantommodell getestet. Ergebnisse: Der Prototyp ermöglicht eine intuitive Bedienung und sterile Interaktion und eignet sich damit gut für den intraoperativen Einsatz. Die Checkliste realisiert die individuelle Erfassung und Speicherung von klinischen Daten und OP-Schritten. Zusätzlich kann ein automatisierter OP-Bericht erstellt und gespeichert werden. Die dynamische Generierung der Checkliste über ein BPMN-Modell ermöglicht dabei die einfache Übertragung auf andere Anwendungsfälle. Zusammenfassung: Die Verwendung einer dynamischen Checkliste vereinfacht die Erfassung, Speicherung und Verarbeitung von chirurgischen Daten.

Cultured Meat (CM) is a growing field in cellular agriculture, driven by the environmental impact of conventional meat production, which contributes to climate change and occupies ≈70% of arable land. As demand for meat alternatives rises, research in this area expands. CM production relies on tissue engineering techniques, where a limited number of animal cells are cultured in vitro and processed to create meat-like tissue comprising muscle and adipose components. Currently, CM is primarily produced on a small scale in pilot facilities. Producing a large cell mass based on suitable cell sources and bioreactors remains challenging. Advanced manufacturing methods and innovative materials are required to subsequently process this cell mass into CM products on a large scale. Consequently, CM is closely linked with biofabrication, a suite of technologies for precisely arranging cellular aggregates and cell-material composites to construct specific structures, often using robotics. This review provides insights into contemporary biomedical biofabrication technologies, focusing on significant advancements in muscle and adipose tissue biofabrication for CM production. Novel materials for biofabricating CM are also discussed, emphasizing their edibility and incorporation of healthful components. Finally, initial studies on biofabricated CM are examined, addressing current limitations and future challenges for large-scale production.

Precise neurosurgical guidance is critical for successful brain surgeries and plays a vital role in all phases of image-guided neurosurgery (IGN). Neuronavigation software enables real-time tracking of surgical tools, ensuring their presentation with high precision in relation to a virtual patient model. Therefore, this work focuses on the development of a novel multimodal IGN system, leveraging deep learning and explainable AI to enhance brain tumor surgery outcomes. The study establishes the clinical and technical requirements of the system for brain tumor surgeries. NeuroIGN adopts a modular architecture, including brain tumor segmentation, patient registration, and explainable output prediction, and integrates open-source packages into an interactive neuronavigational display. The NeuroIGN system components underwent validation and evaluation in both laboratory and simulated operating room (OR) settings. Experimental results demonstrated its accuracy in tumor segmentation and the success of ExplainAI in increasing the trust of medical professionals in deep learning. The proposed system was successfully assembled and set up within 11 min in a pre-clinical OR setting with a tracking accuracy of 0.5 (± 0.1) mm. NeuroIGN was also evaluated as highly useful, with a high frame rate (19 FPS) and real-time ultrasound imaging capabilities. In conclusion, this paper describes not only the development of an open-source multimodal IGN system but also demonstrates the innovative application of deep learning and explainable AI algorithms in enhancing neuronavigation for brain tumor surgeries. By seamlessly integrating pre- and intra-operative patient image data with cutting-edge interventional devices, our experiments underscore the potential for deep learning models to improve the surgical treatment of brain tumors and long-term post-operative outcomes.