Youngjun Kim

1.7k total citations
54 papers, 1.1k citations indexed

About

Youngjun Kim is a scholar working on Surgery, Biomedical Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Youngjun Kim has authored 54 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Surgery, 20 papers in Biomedical Engineering and 17 papers in Computer Vision and Pattern Recognition. Recurrent topics in Youngjun Kim's work include Augmented Reality Applications (8 papers), Surgical Simulation and Training (7 papers) and Knee injuries and reconstruction techniques (6 papers). Youngjun Kim is often cited by papers focused on Augmented Reality Applications (8 papers), Surgical Simulation and Training (7 papers) and Knee injuries and reconstruction techniques (6 papers). Youngjun Kim collaborates with scholars based in South Korea, United States and Japan. Youngjun Kim's co-authors include Hannah Kim, Yong Oock Kim, Seok Won Chung, Kyung-Soo Oh, Jong Pil Yoon, Joon Yub Kim, Na Ra Kim, Young-Min Noh, Seung Seog Han and Hyo Jin Lee and has published in prestigious journals such as Scientific Reports, Spine and Journal of Business Research.

In The Last Decade

Youngjun Kim

50 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Youngjun Kim South Korea 14 466 406 207 194 175 54 1.1k
Ramin Shahidi United States 16 467 1.0× 329 0.8× 33 0.2× 185 1.0× 334 1.9× 65 1.2k
Greg Osgood United States 22 968 2.1× 663 1.6× 21 0.1× 164 0.8× 418 2.4× 92 1.5k
Philipp Fürnstahl Switzerland 28 2.2k 4.6× 933 2.3× 73 0.4× 96 0.5× 449 2.6× 158 2.6k
Antony J. Hodgson Canada 25 1.0k 2.2× 795 2.0× 16 0.1× 235 1.2× 249 1.4× 119 2.0k
Yoshito Otake Japan 27 826 1.8× 993 2.4× 55 0.3× 642 3.3× 364 2.1× 158 2.0k
Giulio Dagnino United Kingdom 17 422 0.9× 471 1.2× 34 0.2× 57 0.3× 103 0.6× 50 960
Ekkehard Euler Germany 24 1.2k 2.6× 474 1.2× 11 0.1× 151 0.8× 326 1.9× 90 1.7k
Justin D. Opfermann United States 16 574 1.2× 649 1.6× 172 0.8× 118 0.6× 176 1.0× 60 1.3k
Sara Condino Italy 19 662 1.4× 468 1.2× 18 0.1× 54 0.3× 534 3.1× 74 1.2k
Kanako Harada Japan 22 605 1.3× 837 2.1× 18 0.1× 137 0.7× 192 1.1× 129 1.5k

Countries citing papers authored by Youngjun Kim

Since Specialization
Citations

This map shows the geographic impact of Youngjun Kim'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 Youngjun Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Youngjun Kim more than expected).

Fields of papers citing papers by Youngjun Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Youngjun Kim. 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 Youngjun Kim. The network helps show where Youngjun Kim may publish in the future.

Co-authorship network of co-authors of Youngjun Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Youngjun Kim. A scholar is included among the top collaborators of Youngjun Kim 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 Youngjun Kim. Youngjun Kim 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.
Kim, Youngjun, et al.. (2025). From visuals to value: leveraging generative AI to explore the economic implications of movie poster. Journal of Business Research. 198. 115498–115498. 1 indexed citations
2.
Kim, Youngjun, et al.. (2024). Clinical validity and precision of deep learning-based cone-beam computed tomography automatic landmarking algorithm. Imaging Science in Dentistry. 54(3). 240–240. 1 indexed citations
3.
Lee, Jun Young, et al.. (2024). Shallow trochlear groove and narrow medial trochlear width at the proximal trochlea in patients with trochlear dysplasia: A three‐dimensional computed tomography analysis. Knee Surgery Sports Traumatology Arthroscopy. 32(6). 1434–1445. 2 indexed citations
4.
Kim, Hannah, et al.. (2022). Deep Learning-Based Automatic Segmentation of Mandible and Maxilla in Multi-Center CT Images. Applied Sciences. 12(3). 1358–1358. 15 indexed citations
5.
6.
Kim, Hannah, Laehyun Kim, Jin‐Kuk Kim, et al.. (2021). Artificial intelligence-based nomogram for small-incision lenticule extraction. BioMedical Engineering OnLine. 20(1). 38–38. 12 indexed citations
7.
Kim, Joon Yub, et al.. (2021). Author Correction: Automated rotator cuff tear classification using 3D convolutional neural network. Scientific Reports. 11(1). 15996–15996. 3 indexed citations
8.
Kim, Joon Yub, et al.. (2020). Automated rotator cuff tear classification using 3D convolutional neural network. Scientific Reports. 10(1). 15632–15632. 40 indexed citations
9.
Kim, Hannah, Jeonghwan Lee, Cho Hyun-Chul, et al.. (2019). Three-dimensional orbital wall modeling using paranasal sinus segmentation. Journal of Cranio-Maxillofacial Surgery. 47(6). 959–967. 9 indexed citations
10.
Kim, Youngjun, Byung Hoon Lee, Cho Hyun-Chul, et al.. (2017). Registration accuracy enhancement of a surgical navigation system for anterior cruciate ligament reconstruction: A phantom and cadaveric study. The Knee. 24(2). 329–339. 14 indexed citations
11.
Kim, Youngjun, et al.. (2017). 3D Boolean operations in virtual surgical planning. International Journal of Computer Assisted Radiology and Surgery. 12(10). 1697–1709. 3 indexed citations
12.
Moon, Sang Won, Byung Hoon Lee, Sehyung Park, et al.. (2016). Arthroscopically blind anatomical anterior cruciate ligament reconstruction using only navigation guidance: a cadaveric study. The Knee. 23(5). 813–819. 2 indexed citations
13.
Kim, Sun‐Hee, Deukhee Lee, Sehyung Park, et al.. (2016). Automatic segmentation of supraspinatus from MRI by internal shape fitting and autocorrection. Computer Methods and Programs in Biomedicine. 140. 165–174. 23 indexed citations
14.
Kim, Youngjun, et al.. (2016). Automatic deformable surface registration for medical applications by radial basis function-based robust point-matching. Computers in Biology and Medicine. 77. 173–181. 8 indexed citations
15.
Kim, Youngjun, et al.. (2016). Safety of simultaneous bilateral total knee arthroplasty using an extramedullary referencing system: results from 2098 consecutive patients. Archives of Orthopaedic and Trauma Surgery. 136(11). 1615–1621. 5 indexed citations
16.
Kim, Youngjun, et al.. (2015). Effects of Extensor Fatigue Levels of Both Knee Joints and Genders on Vertical Jump Height and Joint Motions of Lower Extremities. The Korean Journal of Physical Education. 54(5). 815–828. 1 indexed citations
17.
18.
Kim, Youngjun, et al.. (2013). Gallbladder Removal Simulation for Laparoscopic Surgery Training: A Hybrid Modeling Method. Journal of Computer Science and Technology. 28(3). 499–507. 4 indexed citations
19.
Lee, Mi‐Young, Seungyeon Lee, Ahra Lee, et al.. (2012). Comparison of echocardiography with dual-source computed tomography for assessment of left ventricular volume in healthy Beagles. American Journal of Veterinary Research. 74(1). 62–69. 8 indexed citations
20.
Kim, Youngjun, et al.. (2005). The Effect of Heat Therapy on Cutaneous Blood Flow and Skin Temperature at Pre-auricular Region. Journal of oral medicine. 30(4). 401–410. 3 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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