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Background: Artificial intelligence (AI) systems have showed promising results in digital pathology, including digital nephropathology and specifically also kidney transplant pathology., Aim: Summarize the current state of research and limitations in the field of AI in kidney transplant pathology diagnostics and provide a future outlook., Materials and Methods: Literature search in PubMed and Web of Science using the search terms "deep learning", "transplant", and "kidney". Based on these results and studies cited in the identified literature, a selection was made of studies that have a histopathological focus and use AI to improve kidney transplant diagnostics., Results and Conclusion: Many studies have already made important contributions, particularly to the automation of the quantification of some histopathological lesions in nephropathology. This likely can be extended to automatically quantify all relevant lesions for a kidney transplant, such as Banff lesions. Important limitations and challenges exist in the collection of representative data sets and the updates of Banff classification, making large-scale studies challenging. The already positive study results make future AI support in kidney transplant pathology appear likely., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Background: Autopsies have long been considered the gold standard for quality assurance in medicine, yet their significance in basic research has been relatively overlooked. The COVID-19 pandemic underscored the potential of autopsies in understanding pathophysiology, therapy, and disease management. In response, the German Registry for COVID-19 Autopsies (DeRegCOVID) was established in April 2020, followed by the DEFEAT PANDEMIcs consortium (2020-2021), which evolved into the National Autopsy Network (NATON)., Deregcovid: DeRegCOVID collected and analyzed autopsy data from COVID-19 deceased in Germany over three years, serving as the largest national multicenter autopsy study. Results identified crucial factors in severe/fatal cases, such as pulmonary vascular thromboemboli and the intricate virus-immune interplay. DeRegCOVID served as a central hub for data analysis, research inquiries, and public communication, playing a vital role in informing policy changes and responding to health authorities., Naton: Initiated by the Network University Medicine (NUM), NATON emerged as a sustainable infrastructure for autopsy-based research. NATON aims to provide a data and method platform, fostering collaboration across pathology, neuropathology, and legal medicine. Its structure supports a swift feedback loop between research, patient care, and pandemic management., Conclusion: DeRegCOVID has significantly contributed to understanding COVID-19 pathophysiology, leading to the establishment of NATON. The National Autopsy Registry (NAREG), as its successor, embodies a modular and adaptable approach, aiming to enhance autopsy-based research collaboration nationally and, potentially, internationally., (© 2024. The Author(s).)

Background: Semiquantitative histological scoring systems are frequently used in nephropathology. In computational nephropathology, the focus is on generating quantitative data from histology (so-called pathomics). Several recent studies have collected such data using next-generation morphometry (NGM) based on segmentations by artificial neural networks and investigated their usability for various clinical or diagnostic purposes., Aim: To present an overview of the current state of studies regarding renal pathomics and to identify current challenges and potential solutions., Materials and Methods: Due to the literature restriction (maximum of 30 references), studies were selected based on a database search that processed as much data as possible, used innovative methodologies, and/or were ideally multicentric in design., Results and Discussion: Pathomics studies in the kidney have impressively demonstrated that morphometric data are useful clinically (for example, for prognosis assessment) and translationally. Further development of NGM requires overcoming some challenges, including better standardization and generation of prospective evidence., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Background: Autopsy is an important tool for understanding the pathogenesis of diseases, including COVID-19., Material and Methods: On 15 April 2020, together with the German Society of Pathology and the Federal Association of German Pathologists, the German Registry of COVID-19 Autopsies (DeRegCOVID) was launched ( www.DeRegCOVID.ukaachen.de ). Building on this, the German Network for Autopsies in Pandemics (DEFEAT PANDEMIcs) was established on 1 September 2020., Results: The main goal of DeRegCOVID is to collect and distribute de facto anonymized data on potentially all autopsies of people who have died from COVID-19 in Germany in order to meet the need for centralized, coordinated, and structured data collection and reporting during the pandemic. The success of the registry strongly depends on the willingness of the respective centers to report the data, which has developed very positively so far and requires special thanks to all participating centers. The rights to own data and biomaterials (stored decentrally) remain with each respective center. The DEFEAT PANDEMIcs network expands on this and aims to strengthen harmonization and standardization as well as nationwide implementation and cooperation in the field of pandemic autopsies., Conclusions: The extraordinary cooperation in the field of autopsies in Germany during the COVID-19 pandemic is impressively demonstrated by the establishment of DeRegCOVID, the merger of the registry of neuropathology (CNS-COVID19) with DeRegCOVID and the establishment of the autopsy network DEFEAT PANDEMIcs. It gives a strong signal for the necessity, readiness, and expertise to jointly help manage current and future pandemics by autopsy-derived knowledge.