Artificial Intelligence and Big Data

• PD Dr. Sebastian Bickelhaupt
• Prof. Dr. Matthias Dietzel
• PD Dr. Stephan Ellmann
• Dr. med. Konstantin Hellwig
• Dr. med. Lorenz Kapsner
• PD Dr. med. Matthias May

Technological progress is increasingly enabling translational research and development of artificial intelligence (AI) and machine learning methods for the purpose of improving and augmenting diagnostic imaging in clinical routine. These methods make it possible to decipher patterns and correlations within medical imaging data and linking the data of different medical sources beyond the existing capabilities, which can be used to gain medical knowledge to improve imaging techniques, diagnostics and patient care. The further development and research of these technologies is one of the central tasks of translational clinical research in the 21st century, especially for medical imaging, that is of pivotal role in the digitalization of hospitals. In close interdisciplinary collaboration, especially with the fields of medical data science, the working group "Artificial Intelligence and Big Data" evaluates and develops new approaches from a clinical understanding of medical data analytics. The aim is to use machine learning and artificial intelligence methods to further improve diagnosis, therapy control and prognostics for patients and to further support the development of personalized precision medicine.

Recent publications (selection):

1. Ellmann S, Seyler L, Gillmann C, Popp V, Treutlein C, Bozec A, Uder M, Bäuerle T.
   Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model. J Vis Exp. 2020 Aug 16;(162). doi: 10.3791/61235. PMID: 32865533..
2. Ellmann S, Schlicht M, Dietzel M, Janka R, Hammon M, Saake M, Ganslandt T, Hartmann A, Kunath F, Wullich B, Uder M, Bäuerle T.
   Computer-Aided Diagnosis in Multiparametric MRI of the Prostate: An Open-Access Online Tool for Lesion Classification with High Accuracy.
   Cancers (Basel). 2020 Aug 21;12(9):2366. doi: 10.3390/cancers12092366. PMID: 32825612
3. Ellmann S, Wenkel E, Dietzel M, Bielowski C, Vesal S, Maier A, Hammon M, Janka R, Fasching PA, Beckmann MW, Schulz Wendtland R, Uder M, Bäuerle T.
   Implementation of machine learning into clinical breast MRI: Potential for objective and accurate decision-making in suspicious breast masses. PLoS One. 2020 Jan 30;15(1):e0228446. doi: 10.1371/journal.pone.0228446. PMID: 31999755; PMCID: PMC6992224.
4. Bickelhaupt S, Jaeger PF, Laun FB, Lederer W, Daniel H, Kuder TA, Wuesthof L, Paech D, Bonekamp D, Radbruch A, Delorme S, Schlemmer HP, Steudle FH, Maier-Hein KH. Radiomics Based on Adapted Diffusion Kurtosis Imaging Helps to Clarify Most Mammographic Findings Suspicious for Cancer. Radiology. 2018 Jun;287(3):761-770. doi: 10.1148/radiol.2017170273. Epub 2018 Feb 20. PMID: 29461172.
5. Dreher C, Kuder TA, König F, Mlynarska-Bujny A, Tenconi C, Paech D, Schlemmer HP, Ladd ME, Bickelhaupt S. Radiomics in diffusion data: a test-retest, inter- and intra-reader DWI phantom study. Clin Radiol. 2020 Oct;75(10):798.e13-798.e22. doi: 10.1016/j.crad.2020.06.024. Epub 2020 Jul 25. PMID: 32723501.