@article{ShamsWangRoineetal.2022,
  author    = {Shams, Boshra and Wang, Ziqian and Roine, Timo and Aydogan, Dogu Baran and Vajkoczy, Peter and Lippert, Christoph and Picht, Thomas and Fekonja, Lucius Samo},
  title     = {Machine learning-based prediction of motor status in glioma patients using diffusion MRI metrics along the corticospinal tract},
  series = {Brain communications},
  volume    = {4},
  journal   = {Brain communications},
  number    = {3},
  publisher = {Oxford University Press},
  address   = {Oxford},
  issn      = {2632-1297},
  doi       = {10.1093/braincomms/fcac141},
  pages     = {17},
  year      = {2022},
  abstract  = {Shams et al. report that glioma patients' motor status is predicted accurately by diffusion MRI metrics along the corticospinal tract based on support vector machine method, reaching an overall accuracy of 77\%. They show that these metrics are more effective than demographic and clinical variables. Along tract statistics enables white matter characterization using various diffusion MRI metrics. These diffusion models reveal detailed insights into white matter microstructural changes with development, pathology and function. Here, we aim at assessing the clinical utility of diffusion MRI metrics along the corticospinal tract, investigating whether motor glioma patients can be classified with respect to their motor status. We retrospectively included 116 brain tumour patients suffering from either left or right supratentorial, unilateral World Health Organization Grades II, III and IV gliomas with a mean age of 53.51 +/- 16.32 years. Around 37\% of patients presented with preoperative motor function deficits according to the Medical Research Council scale. At group level comparison, the highest non-overlapping diffusion MRI differences were detected in the superior portion of the tracts' profiles. Fractional anisotropy and fibre density decrease, apparent diffusion coefficient axial diffusivity and radial diffusivity increase. To predict motor deficits, we developed a method based on a support vector machine using histogram-based features of diffusion MRI tract profiles (e.g. mean, standard deviation, kurtosis and skewness), following a recursive feature elimination method. Our model achieved high performance (74\% sensitivity, 75\% specificity, 74\% overall accuracy and 77\% area under the curve). We found that apparent diffusion coefficient, fractional anisotropy and radial diffusivity contributed more than other features to the model. Incorporating the patient demographics and clinical features such as age, tumour World Health Organization grade, tumour location, gender and resting motor threshold did not affect the model's performance, revealing that these features were not as effective as microstructural measures. These results shed light on the potential patterns of tumour-related microstructural white matter changes in the prediction of functional deficits.},
  language  = {en}
}
@article{KirchlerKonigorskiNordenetal.2022,
  author    = {Kirchler, Matthias and Konigorski, Stefan and Norden, Matthias and Meltendorf, Christian and Kloft, Marius and Schurmann, Claudia and Lippert, Christoph},
  title     = {transferGWAS},
  series = {Bioinformatics},
  volume    = {38},
  journal   = {Bioinformatics},
  number    = {14},
  publisher = {Oxford Univ. Press},
  address   = {Oxford},
  issn      = {1367-4803},
  doi       = {10.1093/bioinformatics/btac369},
  pages     = {3621 -- 3628},
  year      = {2022},
  abstract  = {Motivation: Medical images can provide rich information about diseases and their biology. However, investigating their association with genetic variation requires non-standard methods. We propose transferGWAS, a novel approach to perform genome-wide association studies directly on full medical images. First, we learn semantically meaningful representations of the images based on a transfer learning task, during which a deep neural network is trained on independent but similar data. Then, we perform genetic association tests with these representations. Results: We validate the type I error rates and power of transferGWAS in simulation studies of synthetic images. Then we apply transferGWAS in a genome-wide association study of retinal fundus images from the UK Biobank. This first-of-a-kind GWAS of full imaging data yielded 60 genomic regions associated with retinal fundus images, of which 7 are novel candidate loci for eye-related traits and diseases.},
  language  = {en}
}
@book{MeinelDoellnerWeskeetal.2021,
  author    = {Meinel, Christoph and D{\"o}llner, J{\"u}rgen Roland Friedrich and Weske, Mathias and Polze, Andreas and Hirschfeld, Robert and Naumann, Felix and Giese, Holger and Baudisch, Patrick and Friedrich, Tobias and B{\"o}ttinger, Erwin and Lippert, Christoph and D{\"o}rr, Christian and Lehmann, Anja and Renard, Bernhard and Rabl, Tilmann and Uebernickel, Falk and Arnrich, Bert and H{\"o}lzle, Katharina},
  title     = {Proceedings of the HPI Research School on Service-oriented Systems Engineering 2020 Fall Retreat},
  number    = {138},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  isbn      = {978-3-86956-513-2},
  issn      = {1613-5652},
  doi       = {10.25932/publishup-50413},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-504132},
  publisher      = {Universit{\"a}t Potsdam},
  pages     = {vi, 144},
  year      = {2021},
  abstract  = {Design and Implementation of service-oriented architectures imposes a huge number of research questions from the fields of software engineering, system analysis and modeling, adaptability, and application integration. Component orientation and web services are two approaches for design and realization of complex web-based system. Both approaches allow for dynamic application adaptation as well as integration of enterprise application. Service-Oriented Systems Engineering represents a symbiosis of best practices in object-orientation, component-based development, distributed computing, and business process management. It provides integration of business and IT concerns. The annual Ph.D. Retreat of the Research School provides each member the opportunity to present his/her current state of their research and to give an outline of a prospective Ph.D. thesis. Due to the interdisciplinary structure of the research school, this technical report covers a wide range of topics. These include but are not limited to: Human Computer Interaction and Computer Vision as Service; Service-oriented Geovisualization Systems; Algorithm Engineering for Service-oriented Systems; Modeling and Verification of Self-adaptive Service-oriented Systems; Tools and Methods for Software Engineering in Service-oriented Systems; Security Engineering of Service-based IT Systems; Service-oriented Information Systems; Evolutionary Transition of Enterprise Applications to Service Orientation; Operating System Abstractions for Service-oriented Computing; and Services Specification, Composition, and Enactment.},
  language  = {en}
}
@misc{MontiRautenstrauchGhanbarietal.2022,
  author    = {Monti, Remo and Rautenstrauch, Pia and Ghanbari, Mahsa and Rani James, Alva and Kirchler, Matthias and Ohler, Uwe and Konigorski, Stefan and Lippert, Christoph},
  title     = {Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000 exomes},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t},
  number    = {16},
  doi       = {10.25932/publishup-58607},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-586078},
  pages     = {16},
  year      = {2022},
  abstract  = {Here we present an exome-wide rare genetic variant association study for 30 blood biomarkers in 191,971 individuals in the UK Biobank. We compare gene- based association tests for separate functional variant categories to increase interpretability and identify 193 significant gene-biomarker associations. Genes associated with biomarkers were ~ 4.5-fold enriched for conferring Mendelian disorders. In addition to performing weighted gene-based variant collapsing tests, we design and apply variant-category-specific kernel-based tests that integrate quantitative functional variant effect predictions for mis- sense variants, splicing and the binding of RNA-binding proteins. For these tests, we present a computationally efficient combination of the likelihood- ratio and score tests that found 36\% more associations than the score test alone while also controlling the type-1 error. Kernel-based tests identified 13\% more associations than their gene-based collapsing counterparts and had advantages in the presence of gain of function missense variants. We introduce local collapsing by amino acid position for missense variants and use it to interpret associations and identify potential novel gain of function variants in PIEZO1. Our results show the benefits of investigating different functional mechanisms when performing rare-variant association tests, and demonstrate pervasive rare-variant contribution to biomarker variability.},
  language  = {en}
}
@article{MontiRautenstrauchGhanbarietal.2022,
  author    = {Monti, Remo and Rautenstrauch, Pia and Ghanbari, Mahsa and Rani James, Alva and Kirchler, Matthias and Ohler, Uwe and Konigorski, Stefan and Lippert, Christoph},
  title     = {Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000 exomes},
  series = {Nature Communications},
  volume    = {13},
  journal   = {Nature Communications},
  publisher = {Nature Publishing Group UK},
  address   = {London},
  issn      = {2041-1723},
  doi       = {10.1038/s41467-022-32864-2},
  pages     = {16},
  year      = {2022},
  abstract  = {Here we present an exome-wide rare genetic variant association study for 30 blood biomarkers in 191,971 individuals in the UK Biobank. We compare gene- based association tests for separate functional variant categories to increase interpretability and identify 193 significant gene-biomarker associations. Genes associated with biomarkers were ~ 4.5-fold enriched for conferring Mendelian disorders. In addition to performing weighted gene-based variant collapsing tests, we design and apply variant-category-specific kernel-based tests that integrate quantitative functional variant effect predictions for mis- sense variants, splicing and the binding of RNA-binding proteins. For these tests, we present a computationally efficient combination of the likelihood- ratio and score tests that found 36\% more associations than the score test alone while also controlling the type-1 error. Kernel-based tests identified 13\% more associations than their gene-based collapsing counterparts and had advantages in the presence of gain of function missense variants. We introduce local collapsing by amino acid position for missense variants and use it to interpret associations and identify potential novel gain of function variants in PIEZO1. Our results show the benefits of investigating different functional mechanisms when performing rare-variant association tests, and demonstrate pervasive rare-variant contribution to biomarker variability.},
  language  = {en}
}
@misc{FehrJaramilloGutierrezOalaetal.2022,
  author    = {Fehr, Jana and Jaramillo-Gutierrez, Giovanna and Oala, Luis and Gr{\"o}schel, Matthias I. and Bierwirth, Manuel and Balachandran, Pradeep and Werneck-Leite, Alixandro and Lippert, Christoph},
  title     = {Piloting a Survey-Based Assessment of Transparency and Trustworthiness with Three Medical AI Tools},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t},
  number    = {15},
  doi       = {10.25932/publishup-58328},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-583281},
  pages     = {30},
  year      = {2022},
  abstract  = {Artificial intelligence (AI) offers the potential to support healthcare delivery, but poorly trained or validated algorithms bear risks of harm. Ethical guidelines stated transparency about model development and validation as a requirement for trustworthy AI. Abundant guidance exists to provide transparency through reporting, but poorly reported medical AI tools are common. To close this transparency gap, we developed and piloted a framework to quantify the transparency of medical AI tools with three use cases. Our framework comprises a survey to report on the intended use, training and validation data and processes, ethical considerations, and deployment recommendations. The transparency of each response was scored with either 0, 0.5, or 1 to reflect if the requested information was not, partially, or fully provided. Additionally, we assessed on an analogous three-point scale if the provided responses fulfilled the transparency requirement for a set of trustworthiness criteria from ethical guidelines. The degree of transparency and trustworthiness was calculated on a scale from 0\% to 100\%. Our assessment of three medical AI use cases pin-pointed reporting gaps and resulted in transparency scores of 67\% for two use cases and one with 59\%. We report anecdotal evidence that business constraints and limited information from external datasets were major obstacles to providing transparency for the three use cases. The observed transparency gaps also lowered the degree of trustworthiness, indicating compliance gaps with ethical guidelines. All three pilot use cases faced challenges to provide transparency about medical AI tools, but more studies are needed to investigate those in the wider medical AI sector. Applying this framework for an external assessment of transparency may be infeasible if business constraints prevent the disclosure of information. New strategies may be necessary to enable audits of medical AI tools while preserving business secrets.},
  language  = {en}
}
@article{FehrJaramilloGutierrezOalaetal.2022,
  author    = {Fehr, Jana and Jaramillo-Gutierrez, Giovanna and Oala, Luis and Gr{\"o}schel, Matthias I. and Bierwirth, Manuel and Balachandran, Pradeep and Werneck-Leite, Alixandro and Lippert, Christoph},
  title     = {Piloting a Survey-Based Assessment of Transparency and Trustworthiness with Three Medical AI Tools},
  series = {Healthcare},
  volume    = {10},
  journal   = {Healthcare},
  number    = {10},
  publisher = {MDPI},
  address   = {Basel, Schweiz},
  issn      = {2227-9032},
  doi       = {10.3390/healthcare10101923},
  pages     = {30},
  year      = {2022},
  abstract  = {Artificial intelligence (AI) offers the potential to support healthcare delivery, but poorly trained or validated algorithms bear risks of harm. Ethical guidelines stated transparency about model development and validation as a requirement for trustworthy AI. Abundant guidance exists to provide transparency through reporting, but poorly reported medical AI tools are common. To close this transparency gap, we developed and piloted a framework to quantify the transparency of medical AI tools with three use cases. Our framework comprises a survey to report on the intended use, training and validation data and processes, ethical considerations, and deployment recommendations. The transparency of each response was scored with either 0, 0.5, or 1 to reflect if the requested information was not, partially, or fully provided. Additionally, we assessed on an analogous three-point scale if the provided responses fulfilled the transparency requirement for a set of trustworthiness criteria from ethical guidelines. The degree of transparency and trustworthiness was calculated on a scale from 0\% to 100\%. Our assessment of three medical AI use cases pin-pointed reporting gaps and resulted in transparency scores of 67\% for two use cases and one with 59\%. We report anecdotal evidence that business constraints and limited information from external datasets were major obstacles to providing transparency for the three use cases. The observed transparency gaps also lowered the degree of trustworthiness, indicating compliance gaps with ethical guidelines. All three pilot use cases faced challenges to provide transparency about medical AI tools, but more studies are needed to investigate those in the wider medical AI sector. Applying this framework for an external assessment of transparency may be infeasible if business constraints prevent the disclosure of information. New strategies may be necessary to enable audits of medical AI tools while preserving business secrets.},
  language  = {en}
}
@techreport{DoellnerFriedrichArnrichetal.2022,
  author    = {D{\"o}llner, J{\"u}rgen Roland Friedrich and Friedrich, Tobias and Arnrich, Bert and Hirschfeld, Robert and Lippert, Christoph and Meinel, Christoph},
  title     = {Abschlussbericht KI-Labor ITSE},
  doi       = {10.25932/publishup-57860},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-578604},
  pages     = {60},
  year      = {2022},
  abstract  = {Der Abschlussbericht beschreibt Aufgaben und Ergebnisse des KI-Labors "ITSE". Gegenstand des KI-Labors bildeten Methodik, Technik und Ausbildung in der IT-Systemtechnik zur Analyse, Planung und Konstruktion KI-basierter, komplexer IT-Systeme.},
  language  = {de}
}