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The application of artificial intelligence (AI) is often associated with the use of large amounts of data for the construction of AI models and algorithms. This data should ideally comply with the FAIR Data principles, i.e. being findable, accessible, interoperable and reusable. However, the handling of health data poses a particular challenge in this context. In this article, we highlight the challenges of the data usage for AI in medicine using the example of anaesthesia and intensive care medicine. We discuss the current situation but also the obstacles for a wider application of AI in medicine in Europe and give suggestions how to solve the different issues. The article covers different subjects like data protection, research data infrastructures and approval of medical products. Finally, this article shows how it can nevertheless be possible to establish a secure and at the same time effective handling of data for use in AI at the European level despite its unneglectable difficulties.

Diagnostics 13(12), 2098 (2023). doi:10.3390/diagnostics13122098 special issue: "AI-Driven Intelligent Health Care Diagnostic Solutions: A Machine Learning Approach", Acute Respiratory Distress Syndrome (ARDS) is a condition that endangers the lives of many Intensive Care Unit patients through gradual reduction of lung function. Due to its heterogeneity, this condition has been difficult to diagnose and treat, although it has been the subject of continuous research, leading to the development of several tools for modeling disease progression on the one hand, and guidelines for diagnosis on the other, mainly the “Berlin Definition”. This paper describes the development of a deep learning-based surrogate model of one such tool for modeling ARDS onset in a virtual patient: the Nottingham Physiology Simulator. The model-development process takes advantage of current machine learning and data-analysis techniques, as well as efficient hyperparameter-tuning methods, within a high-performance computing-enabled data science platform. The lightweight models developed through this process present comparable accuracy to the original simulator (per-parameter R2 > 0.90). The experimental process described herein serves as a proof of concept for the rapid development and dissemination of specialised diagnosis support systems based on pre-existing generalised mechanistic models, making use of supercomputing infrastructure for the development and testing processes and supported by open-source software for streamlined implementation in clinical routines., Published by MDPI, Basel

16. Interdisziplinäres Symposium AUTOMED - Automatisierungstechnische Verfahren für die Medizintechnik, AUTOMED 2023, Gießen, Germany, 30 Mar 2023 - 31 Mar 2023; Proceedings on automation in medical engineering 2(1), 742 (2023). special issue: "Contributions to the symposium on automation in medical engineering", Published by Infinite Science GmbH, Lübeck

Supplemental material, sj-docx-1-dhj-10.1177_20552076221116772 for Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients by Sebastian J Fritsch, Andrea Blankenheim, Alina Wahl, Petra Hetfeld, Oliver Maassen, Saskia Deffge, Julian Kunze, Rolf Rossaint, Morris Riedel, Gernot Marx and Johannes Bickenbach in Digital Health

Supplemental material, sj-docx-2-dhj-10.1177_20552076221116772 for Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients by Sebastian J Fritsch, Andrea Blankenheim, Alina Wahl, Petra Hetfeld, Oliver Maassen, Saskia Deffge, Julian Kunze, Rolf Rossaint, Morris Riedel, Gernot Marx and Johannes Bickenbach in Digital Health

Additional file 1: Table of preexisting comorbidities and diagnostic baseline value parameters. A table containing preexisting comorbidities and 54 diagnostic, baseline value parameters assessed in the ICU, i.e. the first available measurements of respective parameters, which were used to identify risk factors for a certain outcome.

Additional file 4: Enrichment analysis of comorbidities in non-MV patients compared to MV patients. Text showing the Enrichment analysis of comorbidities in non-MV patients compared to MV patients.

Additional file 3: Influence of comorbidities. Text showing different comorbidities, which are enriched/suppressed within a respective subcohort.

Additional file 2: Influence of baseline values. Text showing different baseline values, which are increased/decreased within a respective subcohort.

Introduction: Though xenogeneic acellular scaffolds are frequently used for surgical reconstruction, knowledge of their mechanical properties is lacking. This study compared the mechanical, histological and ultrastructural properties of various native and acellular specimens. Materials and methods: Porcine esophagi, ureters and skin were tested mechanically in a native or acellular condition, focusing on the elastic modulus, ultimate tensile stress and maximum strain. The testing protocol for soft tissues was standardized, including the adaption of the tissue’s water content and partial plastination to minimize material slippage as well as templates for normed sample dimensions and precise cross-section measurements. The native and acellular tissues were compared at the microscopic and ultrastructural level with a focus on type I collagens. Results: Increased elastic modulus and ultimate tensile stress values were quantified in acellular esophagi and ureters compared to the native condition. In contrast, these values were strongly decreased in the skin after acellularization. Acellularization-related decreases in maximum strain were found in all tissues. Type I collagens were well-preserved in these samples; however, clotting and a loss of cross-linking type I collagens was observed ultrastructurally. Elastins and fibronectins were preserved in the esophagi and ureters. A loss of the epidermal layer and decreased fibronectin content was present in the skin. Discussion: Acellularization induces changes in the tensile properties of soft tissues. Some of these changes appear to be organ specific. Loss of cross-linking type I collagen may indicate increased mechanical strength due to decreasing transverse forces acting upon the scaffolds, whereas fibronectin loss may be related to decreased load-bearing capacity. Potentially, the alterations in tissue mechanics are linked to organ function and to the interplay of cells and the extracellular matrix, which is different in hollow organs when compared to skin.

Hans-Jörg Rheinberger: Rekurrenzen: Texte zu Althusser. Berlin: Merve 2014. 9783883963556

Introduction: Acellular scaffolds are increasingly used for the surgical repair of tendon injury and ligament tears. Despite this increased use, very little data exist directly comparing acellular scaffolds and their native counterparts. Such a comparison would help establish the effectiveness of the acellularization procedure of human tissues. Furthermore, such a comparison would help estimate the influence of cells in ligament and tendon stability and give insight into the effects of acellularization on collagen. Material and Methods: Eighteen human iliotibial tract samples were obtained from nine body donors. Nine samples were acellularized with sodium dodecyl sulphate (SDS), while nine counterparts from the same donors remained in the native condition. The ends of all samples were plastinated to minimize material slippage. Their water content was adjusted to 69%, using the osmotic stress technique to exclude water content-related alterations of the mechanical properties. Uniaxial tensile testing was performed to obtain the elastic modulus, ultimate stress and maximum strain. The effectiveness of the acellularization procedure was histologically verified by means of a DNA assay. Results: The histology samples showed a complete removal of the cells, an extensive, yet incomplete removal of the DNA content and alterations to the extracellular collagen. Tensile properties of the tract samples such as elastic modulus and ultimate stress were unaffected by acellularization with the exception of maximum strain. Discussion: The data indicate that cells influence the mechanical properties of ligaments and tendons in vitro to a negligible extent. Moreover, acellularization with SDS alters material properties to a minor extent, indicating that this method provides a biomechanical match in ligament and tendon reconstruction. However, the given protocol insufficiently removes DNA. This may increase the potential for transplant rejection when acellular tract scaffolds are used in soft tissue repair. Further research will help optimize the SDS-protocol for clinical application.

Irrigated agriculture in the Khorezm region in the arid inner Aral Sea Basin faces enormous challenges due to a legacy of cotton monoculture and non-sustainable water use. Regional crop growth monitoring and yield estimation continuously gain in importance, especially with regard to climate change and food security issues. Remote sensing is the ideal tool for regional-scale analysis, especially in regions where ground-truth data collection is difficult and data availability is scarce. New satellite systems promise higher spatial and temporal resolutions. So-called light use efficiency (LUE) models are based on the fraction of photosynthetic active radiation absorbed by vegetation (FPAR), a biophysical parameter that can be derived from satellite measurements. The general objective of this thesis was to use satellite data, in conjunction with an adapted LUE model, for inferring crop yield of cotton and rice at field (6.5 m) and regional (250 m) scale for multiple years (2003-2009), in order to assess crop yield variations in the study area. Intensive field measurements of FPAR were conducted in the Khorezm region during the growing season 2009. RapidEye imagery was acquired approximately bi-weekly during this time. The normalized difference vegetation index (NDVI) was calculated for all images. Linear regression between image-based NDVI and field-based FPAR was conducted. The analyses resulted in high correlations, and the resulting regression equations were used to generate time series of FPAR at the RapidEye level. RapidEye-based FPAR was subsequently aggregated to the MODIS scale and used to validate the existing MODIS FPAR product. This step was carried out to evaluate the applicability of MODIS FPAR for regional vegetation monitoring. The validation revealed that the MODIS product generally overestimates RapidEye FPAR by about 6 to 15 %. Mixture of crop types was found to be a problem at the 1 km scale, but less severe at the 250 m scale. Consequently, high resolution FPAR was used to calibrate 8-day, 250 m MODIS NDVI data, this time by linear regression of RapidEye-based FPAR against MODIS-based NDVI. The established FPAR datasets, for both RapidEye and MODIS, were subsequently assimilated into a LUE model as the driving variable. This model operated at both satellite scales, and both required an estimation of further parameters like the photosynthetic active radiation (PAR) or the actual light use efficiency (LUEact). The latter is influenced by crop stress factors like temperature or water stress, which were taken account of in the model. Water stress was especially important, and calculated via the ratio of the actual (ETact) to the potential, crop-specific evapotranspiration (ETc). Results showed that water stress typically occurred between the beginning of May and mid-September and beginning of May and end of July for cotton and rice crops, respectively. The mean water stress showed only minor differences between years. Exceptions occurred in 2008 and 2009, where the mean water stress was higher and lower, respectively. In 2008, this was likely caused by generally reduced water availability in the whole region. Model estimations were evaluated using field-based harvest information (RapidEye) and statistical information at district level (MODIS). The results showed that the model at both the RapidEye and the MODIS scale can estimate regional crop yield with acceptable accuracy. The RMSE for the RapidEye scale amounted to 29.1 % for cotton and 30.4 % for rice, respectively. At the MODIS scale, depending on the year and evaluated at Oblast level, the RMSE ranged from 10.5 % to 23.8 % for cotton and from -0.4 % to -19.4 % for rice. Altogether, the RapidEye scale model slightly underestimated cotton (bias = 0.22) and rice yield (bias = 0.11). The MODIS-scale model, on the other hand, also underestimated official rice yield (bias from 0.01 to 0.87), but overestimated official cotton yield (bias from -0.28 to -0.6). Evaluation of the MODIS scale revealed that predictions were very accurate for some districts, but less for others. The produced crop yield maps indicated that crop yield generally decreases with distance to the river. The lowest yields can be found in the southern districts, close to the desert. From a temporal point of view, there were areas characterized by low crop yields over the span of the seven years investigated. The study at hand showed that light use efficiency-based modeling, based on remote sensing data, is a viable way for regional crop yield prediction. The found accuracies were good within the boundaries of related research. From a methodological viewpoint, the work carried out made several improvements to the existing LUE models reported in the literature, e.g. the calibration of FPAR for the study region using in situ and high resolution RapidEye imagery and the incorporation of crop-specific water stress in the calculation., Die vorliegende Arbeit beschäftigt sich mit der Modellierung regionaler Erntemengen von Baumwolle und Reis in der usbekischen Region Khorezm, einem Bewässerungsgebiet das geprägt ist von langjähriger Baumwoll-Monokultur und nicht-nachhaltiger Land- und Wassernutzung. Basis für die Methodik waren Satellitendaten, die durch ihre großflächige Abdeckung und Objektivität einen enormen Vorteil in solch datenarmen und schwer zugänglichen Regionen darstellen. Bei dem verwendeten Modell handelt es sich um ein sog. Lichtnutzungseffizienz-Modell (im Englischen Light Use Efficiency [LUE] Model), das auf dem Anteil der photosynthetisch aktiven Strahlung basiert, welcher von Pflanzen für das Wachstum aufgenommen wird (Fraction of Photosynthetic Active Radiation, FPAR). Dieser Parameter kann aus Satellitendaten abgeleitet werden. Das allgemeine Ziel der vorliegenden Arbeit war die Nutzung von Satellitendaten für die Ableitung der Erntemengen von Baumwolle und Reis. Dazu wurde ein Modell entwickelt, das sowohl auf der Feldebene (Auflösung von 6,5 m) als auch auf der regionalen Ebene (Auflösung von 250 m) operieren kann. Während die Ableitung der Erntemengen auf der Feldebene nur für ein Jahr erfolgte (2009), wurden sie auf der regionalen Ebene für den Zeitraum 2003 bis 2009 modelliert. Intensive Feldmessungen von FPAR wurden im Studiengebiet während der Wachstumssaison 2009 durchgeführt. Parallel dazu wurden RapidEye-Daten in ca. zweiwöchentlichem Abstand aufgezeichnet. Aus den RapidEye-Daten wurde der Normalized Difference Vegetation Index (NDVI) berechnet, der anschließend mit den im Feld gemessenen FPAR-Werten korreliert wurde. Die entstandenen Regressionsgleichungen wurden benutzt um Zeitserien von FPAR auf RapidEye-Niveau zu erstellen. Anschließend wurden diese Zeitserien auf die MODIS-Skala aggregiert um damit das MODIS FPAR-Produkt zu validieren (1 km), bzw. eine Kalibrierung des 8-tägigen 250 m NDVI-Datensatzes vorzunehmen. Der erste Schritt zeigte dass das MODIS-Produkt im Allgemeinen die RapidEye-basierten FPAR-Werte um 6 bis 15 % überschätzt. Aufgrund der besseren Auflösung wurde das kalibrierte 250 m FPAR-Produkt für die weitere Modellierung verwendet. Für die eigentliche Modellierung wurden neben den FPAR-Eingangsdaten noch weitere Daten und Parameter benötigt. Dazu gehörte z.B. die tatsächliche Lichtnutzungseffizienz (LUEact), welche von Temperatur- und Wasserstress beeinflusst wird. Wasserstress wurde berechnet aus dem Verhältnis von tatsächlicher (ETact) zu potentieller, feldfruchtspezifischer Evapotranspiration (ETc), die beide aus einer Kombination von Satelliten- und Wetterdaten abgeleitet wurden. Der durchschnittliche Wasserstress schwankte nur geringfügig von Jahr zu Jahr, mit Ausnahmen in den Jahren 2008 und 2009. Die Modellschätzungen wurden durch feldbasierte Ernteinformationen (RapidEye-Ebene) sowie regionale statistische Daten (MODIS-Ebene) evaluiert. Die Ergebnisse zeigten, dass beide Modellskalen regionale Ernteerträge mit guter Genauigkeit nachbilden können. Der Fehler für das RapidEye-basierte Modell betrug 29,1 % für Baumwolle und 30,4 % für Reis. Die Genauigkeiten für das MODIS-basierte Modell variierten, in Abhängigkeit des betrachteten Jahres, zwischen 10,5 % und 23,8 % für Baumwolle und zwischen -0,4 % und -19,4 % für Reis. Insgesamt gab es eine leichte Unterschätzung der Baumwoll- (Bias = 0,22) und Reisernte (Bias = 0,11) seitens des RapidEye-Modells. Das MODIS-Modell hingegen unterschätzte zwar auch die (offizielle) Reisernte (mit einem Bias zwischen 0,01 und 0,87), überschätzte jedoch die offiziellen Erntemengen für die Baumwolle (Bias zwischen -0,28 und -0,6). Die Evaluierung der MODIS-Skala zeigte dass die Genauigkeiten extrem zwischen den verschiedenen Distrikten schwankten. Die erstellten Erntekarten zeigten dass Erntemengen grundsätzlich mit der Distanz zum Fluss abnehmen. Die niedrigsten Erntemengen traten in den südlichsten Distrikten auf, in der Nähe der Wüste. Betrachtet man die Ergebnisse schließlich über die Zeit hinweg, gab es Gebiete die über den gesamten Zeitraum von sieben Jahren stets von niedrigen Erntemengen gekennzeichnet waren. Die vorliegende Studie zeigt, dass satellitenbasierte Lichtnutzungseffizienzmodelle ein geeignetes Werkzeug für die Ableitung und die Analyse regionaler Erntemengen in zentralasiatischen Bewässerungsregionen darstellen. Verglichen mit verwandten Studien stellten sich die ermittelten Genauigkeiten sowohl auf der RapidEye- als auch auf der MODIS-Skala als gut dar. Vom methodischen Standpunkt aus gesehen ergänzte diese Arbeit vorhanden LUE-Modelle um einige Neuerungen und Verbesserungen, wie z.B. die Validierung und Kalibrierung von FPAR für die Studienregion mittels Feld- und hochaufgelösten RapidEye-Daten und dem Einbezug von feldfrucht-spezifischem Wasserstress in die Modellierung.

Continuous land use mapping is essential for improving irrigation water distribution, identifying deficiencies in irrigation water supply, and estimating yields. Findings from the ZEF-UNESCO research project in Khorezm, Uzbekistan, demonstrate how remote sensing and Geographical Information Systems (GIS) can contribute to monitoring and improving land use in irrigation systems.