A Machine- (Deep-) Learning Approach to Fully Automated Regional Bone, Cartilage, and Phenotype Analysis in Knee Osteoarthritis –Development, Validation, Application and Qualification

Wirth, Wolfgang (PI)
Kemnitz, Jana (CoPI)

Institute of Anatomy and Cell Biology Salzburg

Project: Research

Status	Finished
Effective start/end date	1/10/18 → 1/01/20

2 Original Article

Detection of Differences in Longitudinal Cartilage Thickness Loss Using a Deep-Learning Automated Segmentation Algorithm: Data From the Foundation for the National Institutes of Health Biomarkers Study of the Osteoarthritis Initiative.
Eckstein, F. (First author), Chaudhari, A. S., Fuerst, D. (Co-author), Gaisberger, M. (Co-author), Kemnitz, J. (Co-author), Baumgartner, C. F., Konukoglu, E., Hunter, D. J. & Wirth, W. (Last author), 2022, In: ARTHRITIS CARE & RESEARCH. 74, 6, p. 929-936
Research output: Contribution to journal › Original Article › peer-review

17 Citations (Web of Science)
Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort.
Wirth, W. (First author), Eckstein, F. (Co-author), Kemnitz, J. (Co-author), Baumgartner, C. F., Konukoglu, E., Fuerst, D. (Co-author) & Chaudhari, A. S., 2021, In: MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE. 34, 3, p. 337-354
Research output: Contribution to journal › Original Article › peer-review

27 Citations (Web of Science)