Rainer Burgkart

8.3k total citations · 1 hit paper
255 papers, 6.1k citations indexed

About

Rainer Burgkart is a scholar working on Surgery, Biomedical Engineering and Rheumatology. According to data from OpenAlex, Rainer Burgkart has authored 255 papers receiving a total of 6.1k indexed citations (citations by other indexed papers that have themselves been cited), including 153 papers in Surgery, 70 papers in Biomedical Engineering and 43 papers in Rheumatology. Recurrent topics in Rainer Burgkart's work include Total Knee Arthroplasty Outcomes (45 papers), Orthopaedic implants and arthroplasty (43 papers) and Knee injuries and reconstruction techniques (36 papers). Rainer Burgkart is often cited by papers focused on Total Knee Arthroplasty Outcomes (45 papers), Orthopaedic implants and arthroplasty (43 papers) and Knee injuries and reconstruction techniques (36 papers). Rainer Burgkart collaborates with scholars based in Germany, United States and Switzerland. Rainer Burgkart's co-authors include John Tedrow, Farshid Guilak, F. Eckstein, Rüdiger von Eisenhart‐Rothe, Karl‐Hans Englmeier, Maximilian F. Reiser, Hans Gollwitzer, Andreas Büttner, Christian Gläser and Andreas Obermeier and has published in prestigious journals such as Nature Materials, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Rainer Burgkart

243 papers receiving 6.0k citations

Hit Papers

The microstructure and mi... 2017 2026 2020 2023 2017 50 100 150 200 250

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Rainer Burgkart Germany 42 3.2k 1.7k 1.2k 948 565 255 6.1k
Jeffrey C. Lotz United States 63 5.1k 1.6× 2.5k 1.4× 1.2k 1.0× 1.7k 1.7× 931 1.6× 239 11.0k
Tomoyuki Saito Japan 45 4.8k 1.5× 969 0.6× 1.5k 1.3× 769 0.8× 1.1k 1.9× 285 7.7k
Matthias Schieker Germany 44 2.2k 0.7× 1.9k 1.1× 586 0.5× 1.4k 1.5× 1.1k 2.0× 147 6.0k
Dan L. Bader United Kingdom 56 2.9k 0.9× 2.1k 1.2× 2.2k 1.9× 1.8k 1.9× 738 1.3× 245 9.3k
Christoph H. Lohmann Germany 43 3.7k 1.1× 2.7k 1.6× 952 0.8× 609 0.6× 957 1.7× 192 7.2k
Dawn M. Elliott United States 62 5.1k 1.6× 3.8k 2.2× 1.2k 1.1× 2.2k 2.3× 412 0.7× 202 11.0k
Ray Vanderby United States 43 3.3k 1.0× 1.5k 0.9× 340 0.3× 2.4k 2.5× 492 0.9× 220 6.2k
Hiromu Ito Japan 48 2.7k 0.8× 955 0.5× 2.2k 1.9× 399 0.4× 1.7k 3.0× 272 8.3k
Ross Crawford Australia 54 4.0k 1.3× 2.7k 1.6× 2.3k 1.9× 528 0.6× 2.0k 3.5× 358 9.6k
Joseph M. Mansour United States 33 1.6k 0.5× 1.4k 0.8× 1.8k 1.6× 423 0.4× 250 0.4× 101 5.1k

Countries citing papers authored by Rainer Burgkart

Since Specialization
Citations

This map shows the geographic impact of Rainer Burgkart's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Rainer Burgkart with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rainer Burgkart more than expected).

Fields of papers citing papers by Rainer Burgkart

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rainer Burgkart. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Rainer Burgkart. The network helps show where Rainer Burgkart may publish in the future.

Co-authorship network of co-authors of Rainer Burgkart

This figure shows the co-authorship network connecting the top 25 collaborators of Rainer Burgkart. A scholar is included among the top collaborators of Rainer Burgkart based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Rainer Burgkart. Rainer Burgkart is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Burgkart, Rainer. (2025). Tendon-bone interface - Nature´s solution for a hard-soft-interface. Annals of Anatomy - Anatomischer Anzeiger. 265. 152769–152769.
2.
Hinterwimmer, Florian, et al.. (2024). The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review. JMIR mhealth and uhealth. 13. e60521–e60521. 2 indexed citations
6.
Hinterwimmer, Florian, et al.. (2022). Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies—a scoping review. European Radiology. 32(10). 7173–7184. 12 indexed citations
7.
Schacky, Claudio E. von, Yannik Leonhardt, Felix G. Gassert, et al.. (2021). Multitask Deep Learning for Segmentation and Classification of Primary Bone Tumors on Radiographs. Radiology. 301(2). 398–406. 77 indexed citations
8.
Foehr, Peter, Jochen Weitz, Constantin von Deimling, et al.. (2021). Improving mandibular reconstruction by using topology optimization, patient specific design and additive manufacturing?—A biomechanical comparison against miniplates on human specimen. PLoS ONE. 16(6). e0253002–e0253002. 18 indexed citations
9.
Huber, René, et al.. (2021). In Vitro Cartilage Regeneration with a Three-Dimensional Polyglycolic Acid (PGA) Implant in a Bovine Cartilage Punch Model. International Journal of Molecular Sciences. 22(21). 11769–11769. 3 indexed citations
10.
Banke, In go J., Peter Michael Prodinger, Jutta Tübel, et al.. (2020). Antimicrobial peptides in human synovial membrane as (low-grade) periprosthetic joint infection biomarkers. European journal of medical research. 25(1). 33–33. 7 indexed citations
11.
Knebel, Carolin, Florian Pohlig, Peter Herschbach, et al.. (2020). Peri-Prosthetic Joint Infection of the Knee Causes High Levels of Psychosocial Distress: A Prospective Cohort Study. Surgical Infections. 21(10). 877–883. 48 indexed citations
12.
Reuther, Anne U., et al.. (2020). Thickness of the Stifle Joint Articular Cartilage in Different Large Animal Models of Cartilage Repair and Regeneration. Cartilage. 13(2_suppl). 438S–452S. 13 indexed citations
13.
Theruvath, Ashok J., Hossein Nejadnik, Olga D. Lenkov, et al.. (2019). Tracking Stem Cell Implants in Cartilage Defects of Minipigs by Using Ferumoxytol-enhanced MRI. Radiology. 292(1). 129–137. 31 indexed citations
14.
Foehr, Peter, et al.. (2018). In VitroAnalysis of Cartilage Regeneration Using a Collagen Type I Hydrogel (CaReS) in the Bovine Cartilage Punch Model. Cartilage. 10(3). 346–363. 18 indexed citations
15.
Eggert, Sebastian, Peter Foehr, Lara Kuntz, et al.. (2017). Image-Based Histological Evaluation of Scaffold-Free 3D Osteoblast Cultures. Journal of Functional Morphology and Kinesiology. 2(4). 42–42. 2 indexed citations
16.
Eichhorn, Stefan, E. Steinhäuser, Hans Gollwitzer, et al.. (2014). Ceramic Macrostructured Acetabular Liner Integratingdirectly into Bone: Implant Design, Manufacturing, and In vitro Investigations. Journal of Medical and Biological Engineering. 34(1). 76–81. 2 indexed citations
17.
Burgkart, Rainer, Peter Michael Prodinger, Mihaela Culmes, et al.. (2013). Decellularized Kidney Matrix for Perfused Bone Engineering. Tissue Engineering Part C Methods. 20(7). 553–561. 36 indexed citations
18.
Riener, Robert, et al.. (2005). Elastic properties of an intact and ACL-ruptured knee joint: Measurement, mathematical modelling, and haptic rendering. Journal of Biomechanics. 39(8). 1371–1382. 8 indexed citations
19.
Sielhorst, Tobias, et al.. (2004). An Augmented Reality Delivery Simulator for Medical Training. 339. b3403–b3403. 49 indexed citations
20.
Gläser, Christian, et al.. (2003). Femoro‐tibial cartilage metrics from coronal MR image data: Technique, test–retest reproducibility, and findings in osteoarthritis. Magnetic Resonance in Medicine. 50(6). 1229–1236. 60 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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