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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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    DEvelopMent Of methods for manufacturing Small animal phanToms for preclinical RAdiation Research
    Recent developments in the field of small-animal irradiation are enabling a faster translation of experimental results into the clinic, since radiobiology experiments can be reproduced in a more accurate way. In such experiments, mice or rats are often used, as their complex, human-like anatomy is a suitable model for in-vivo analysis. An important technical advancement in preclinical studies is the implementation of image guidance for treatment planning and delivery, resulting in a precise irradiation of the tumour. For an accurate determination of radiation dose, tissue inhomogeneities need to be accounted for during treatment planning and dose calculation. However, typical phantoms for dosimetry and quality assurance are made of a homogeneous material and designed with a simple geometry (such as cylinders). To tackle this issue, phantoms based on realistic anatomy and tissue-mimicking materials were created in the last few years by using additive manufacturing (AM), commonly known as 3D printing. This technique allows for the production of complex models from a three-dimensional data set, and offer high flexibility with respect to both the anatomical details to be included (body shape and internal structure) and the material choice for mimicking tissue properties with respect to x-ray attenuation. However, so far, no systematic approach to phantom development has been described, despite the need for models that are suitable as a standard for characterisation of small-animal imaging and radiation equipment. All proposed models, in fact, are built for specific purposes and therefore not adaptable for further tests in an inexpensive and sustainable way. With this project, we intend to develop a systematic approach for flexible and modular phantom development and qualification in small-animal medical imaging and radiotherapy. To this end, we will focus on (i) the definition of a general creation process for sustainable and inexpensive phantoms, from the construction of a 3D model to the choice of the AM technique, (ii) the generation of a database for 3D printable materials with tissue-equivalent x-ray absorption properties and (iii) the development of 3D models which can be used for Monte Carlo simulations as well as for other purposes, such as image registration verification. By implementing this approach and making the results available to the community, we will contribute to data harmonisation and effective training in the radiobiology community without the use of small animals, which complies with the principle of the “3R” (Replace, Reduce, Refine) in animal research.
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    Integration von Gesundheit in die Stadt- und Verkehrsplanung – Neue Governancestrukturen in stadtregionalen Verkehrskorridoren
    Stadtregionen zeichnen sich durch wirtschaftliche Stärke, leistungsfähige Infrastruktur sowie großem Bevölkerungspotenzial aus. Sie sind in ihrer Zusammensetzung komplexe sozio-politische Gebilde, die durch eine hohe Anzahl an Akteuren, Institutionen und Zuständigkeiten sowie einem Mehrebenensystem (Multilevel-Governance) charakterisiert sind. Das politische Ziel einer hohen Wettbewerbsfähigkeit setzt eine intensive verkehrsinfrastrukturelle Anbindung und Vernetzung voraus. Daraus ergeben sich jedoch Zielkonflikte, vorrangig zwischen Verkehrsinfrastruktur, Naturschutz sowie vor allem Gesundheitsaspekten der lokalen Bevölkerung. Häufige Ursache dafür ist, dass “Gesundheit” in der Abwägung planerischer Belange nur unzureichend berücksichtigt wird. Am Beispiel der Stadtregion „Hamburger Südwesten“ sollen diese Konflikte detailliert untersucht werden. Die Region zeichnet sich durch eine hohe Dichte an Verkehrsachsen sowie durch eine markante Heterogenität in Siedlungsstruktur, Flächennutzung und Bevölkerungszusammensetzung aus. Im Rahmen des Vorhabens werden auf Basis des multi-source Ansatzes Sozialraum- und Gesundheitsanalysen für den Untersuchungsraum durchgeführt. Daneben werden u.a. spezifische Analysen zum Mobilitätsverhalten oder auch zur Akzeptanz der bestehenden und geplanten Verkehrsachsen umgesetzt. Aus diesen Erkenntnissen werden Strategien und Handlungsempfehlungen zur Optimierung von Prozessen auf verschiedenen Planungsebenen entwickelt, um eine Integration von Gesundheit in die Stadt- und Verkehrsplanung zu fördern.
      10
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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.
      37
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