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    Classification of laryngeal lesions using advanced endoscopic imaging and real-time evaluation by AI
    Tumors of the upper aerodigestive tract (UADT) are the sixth most common tumor entity in humans, with laryngeal cancer accounting for 30-40%. Thereby, an earlier diagnosis goes along with a better prognosis for the patients concerned. As a visual endoscopic assessment is currently not able to replace histopathological examinations, current standard of care in newly detected laryngeal lesions comprises a so-called microlaryngoscopy under general anesthesia combined with invasive tissue sampling, which is costly as well as time consuming and prone to sampling errors. An optical method enabling an on-site distinction between healthy tissue, dysplastic tissue and cancerous tissue in an outpatient setting would therefore have the potential to revolutionize early laryngeal cancer detection. Various optical techniques providing different information on the tissues investigated have been tested for classification of mucosal lesions of the larynx in (pre-) clinical studies and shown promising results, but they do have their individual limitations as standalone procedures. Therefore, multimodal imaging using a combination of techniques has been suggested and trialed, but – mostly owing to its complexity in data acquisition and interpretation – has not yet progressed into clinically meaningful applications. In a new and promising attempt, we aim to combine optical coherence tomography (OCT) and narrow band imaging (NBI) using a flexible endoscope and thus combining depth and spatial information of the suspicious tissue. To overcome the current limitations of multimodal image interpretation, we aim to study different artificial intelligence (AI) methods to fuse information and assist with image analysis following a spatially and temporally correct registration of the multimodal imaging data. Our first goal in this project is the development of a single outpatient endoscopic procedure that allows for an onsite tissue classification in the larynx (“optical biopsy”). As a second goal, the performance of this automated multimodal endoscopic imaging will be evaluated against the current standard-of-care. To achieve these goals, we will design and manufacture biocompatible OCT fiber probes. The probes will fit into the working channel of a flexible NBI-ready nasopharyngolaryngoscope. To evaluate the multimodal imaging, we will perform a study on 60 awake patients with flat mucosal lesions of the endolarynx and 60 healthy volunteers. State-of-the-art deep learning methods will be applied for an automated tissue classification based on both individual imaging modalities and the multimodal data set. This will make our system for computer-assisted diagnosis (CAD) independent of the experience of the physician, reducing the problem of interrater variability.
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    GRK 3144: Multiscale Imaging and Analytics of Interfaces in Musculoskeletal Health
    The interdisciplinary Research Training Group (RTG) program entitled "Multiscale Imaging and Analytics of Interfaces in Musculoskeletal Health" extends beyond traditional assessments of bone quantity and density to investigate the complexities of musculoskeletal integrity. This program seeks to understand the interactions between bones, muscles, tendons, fat, and vessels, including their interfaces. By integrating biomedical and technological insights, the program aims to drive innovation in diagnostics and preventive healthcare. Its research objectives include exploring the muscle-fat-bone axis, the tendon-bone interface, the role of vascularization in skeletal health, advanced imaging techniques, models for tissue interfaces, and the use of posture and motion as health indicators. Graduates will gain proficiency in state-of-the-art imaging, disease modeling, and machine learning techniques focused on interfaces, equipping them for addressing global challenges in medicine and technology. The Qualification Program is designed to train a new generation of interdisciplinary STEM scientists in musculoskeletal research. Spanning three cohorts over nine years, it emphasizes skill development in interdisciplinary project management, research ethics, and foundational musculoskeletal research knowledge. Networking opportunities are facilitated through workshops, industry excursions, and international conferences. The inclusion of a Mercator fellow position and the involvement of physicians promote international collaboration and research profile development. This program prepares students for careers in research, teaching, and industry, with a strong emphasis on ethical conduct, collaborative skills, and independence. Located in Hamburg, the RTG benefits from the Interdisciplinary Competence Center for Interface Research (ICCIR) and operates across five laboratory campuses in Hamburg-Eppendorf, Hamburg-Harburg, and Hamburg’s Science City Bahrenfeld. The University Medical Center Hamburg (UKE) provides an interdisciplinary research environment, laboratory facilities, and high-resolution imaging tools. The Hamburg University of Technology (TUHH) specializes in engineering sciences, with a focus on artificial intelligence, data analysis, and computational modeling. Hereon, a member of the Helmholtz Association, offers large-scale facilities for synchrotron science and bone interface research. Hamburg's strategic positioning, robust research infrastructure, and collaborative culture create an optimal setting for musculoskeletal research. The RTG is committed to promoting diversity and gender equality, providing support and initiatives for female scientists. Our interdisciplinary approach not only strengthens healthcare and technological innovation but also prepares graduates for significant roles in academic and corporate research sectors, benefiting both the local and global research communities.
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    Aufbau einer norddeuschen KI-Med-Kollaborationsplattform
    The KiMeKo partners have acquired many skills in previous KI-Med projects - in particular KI-SIGS funded by the BMWK and KI-Lab funded by the BMBF - but these are decentralized, unsystematized and in need of supplementation. This is precisely the aim of the five KIMKO sub-projects: to bundle and deepen expertise in such a way that the KI-Med ecosystem can offer standardized services, i.e. methods, tools and workflows, as far as possible. On this basis, the AI-Med collaboration platform is intended to ensure that, on the one hand, the collaboration between medical professionals, IT specialists and companies (the stakeholder groups), which is essential for AI-Med applications, is made significantly more efficient (faster and qualitatively improved); in order to implement larger - possibly also disruptive - strategic AI-Med applications with truly broad usage potential in healthcare with a higher chance of success. And on the other hand, the aim is to accelerate the development of such AI-Med innovations to such an extent that AI-Med medical products can actually be developed with regulatory approval, which alone find their way into healthcare and thus enable economic value creation in the healthcare system and for companies. The five sub-projects are all being carried out by the universities' IT institutes with the advisory involvement of clinics and companies, as the intention is not yet to develop AI-Med applications as such, but to create the strong conditions that will enable them to scale in the context of the AI-Med ecosystem in the future.
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    Centre of Excellence of Al for Sustainable Living and Working
    SustAInLivWork – a Centre of Excellence (CoE) for Sustainable Living and Working (SustAInLivWork), specialised in the development and application of R&I solutions based on Artificial Intelligence (AI) in the manufacturing, energy, health and transport sectors. This CoE will act as a lighthouse with far reaching impact strengthening R&I in the national and international scope, nurturing from a cooperation between key national partners – the four leading universities of the country – (KTU, VILNIUS TECH, VMU, LSMU), and advanced international ones – (TAU, TUHH), that will contribute with their knowledge to boost value creation from advanced technologies. It will have an impact on the country and on the whole Baltic Sea Region and beyond, by the operation of 4 HUBs that will derive into new educational and training programmes, an enhanced cooperation with the private sector and public authorities and the creation of a Lithuanian AI Cluster to foster the transition towards sustainability with a particular focus on the S3 priorities areas. CoE will improve the Lithuanian position in EU Innovation scoreboards, increase international collaborations, mobilise national, European and other international funding programmes by creating service packages, AI labs and different collaborations both national and international. Goal: The project aims to create a joint Centre of Excellence of Artificial Intelligence for Sustainable Living and Working in Lithuania, which would act as a Lighthouse, making a significant impact on strengthening the research and innovation ecosystem nationally and internationally
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