Hamid Ghaednia

1.1k total citations
33 papers, 753 citations indexed

About

Hamid Ghaednia is a scholar working on Biomedical Engineering, Mechanics of Materials and Surgery. According to data from OpenAlex, Hamid Ghaednia has authored 33 papers receiving a total of 753 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Biomedical Engineering, 13 papers in Mechanics of Materials and 7 papers in Surgery. Recurrent topics in Hamid Ghaednia's work include Adhesion, Friction, and Surface Interactions (11 papers), Mechanical stress and fatigue analysis (5 papers) and Gear and Bearing Dynamics Analysis (4 papers). Hamid Ghaednia is often cited by papers focused on Adhesion, Friction, and Surface Interactions (11 papers), Mechanical stress and fatigue analysis (5 papers) and Gear and Bearing Dynamics Analysis (4 papers). Hamid Ghaednia collaborates with scholars based in United States, Netherlands and South Korea. Hamid Ghaednia's co-authors include Robert L. Jackson, Dan B. Marghitu, Aman Sharma, Yang Xu, Swarna Saha, Xianzhang Wang, Joseph H. Schwab, Jacobien H. F. Oosterhoff, Amanda Lans and Mitchell S. Fourman and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Scientific Reports.

In The Last Decade

Hamid Ghaednia

30 papers receiving 739 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hamid Ghaednia United States 12 398 271 189 135 65 33 753
Sang-Youl Lee South Korea 19 415 1.0× 144 0.5× 90 0.5× 110 0.8× 130 2.0× 109 989
E.B. Pires Portugal 16 527 1.3× 210 0.8× 300 1.6× 216 1.6× 138 2.1× 30 1.1k
Eduardo Alberto Fancello Brazil 18 642 1.6× 144 0.5× 232 1.2× 94 0.7× 56 0.9× 84 1.1k
Soo‐Won Chae South Korea 19 94 0.2× 295 1.1× 138 0.7× 170 1.3× 45 0.7× 71 783
Melih Eriten United States 19 533 1.3× 480 1.8× 113 0.6× 57 0.4× 210 3.2× 69 1.1k
Philippe Rouch France 20 172 0.4× 62 0.2× 402 2.1× 324 2.4× 38 0.6× 76 983
Kenji AMAYA Japan 13 144 0.4× 179 0.7× 88 0.5× 15 0.1× 97 1.5× 91 659
Bradley N. Maker United States 11 380 1.0× 220 0.8× 544 2.9× 212 1.6× 48 0.7× 15 979
G. L. Kinzel United States 15 120 0.3× 207 0.8× 234 1.2× 202 1.5× 83 1.3× 33 701
Matthew Oldfield United Kingdom 11 113 0.3× 141 0.5× 243 1.3× 84 0.6× 127 2.0× 24 534

Countries citing papers authored by Hamid Ghaednia

Since Specialization
Citations

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

Fields of papers citing papers by Hamid Ghaednia

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hamid Ghaednia

This figure shows the co-authorship network connecting the top 25 collaborators of Hamid Ghaednia. A scholar is included among the top collaborators of Hamid Ghaednia 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 Hamid Ghaednia. Hamid Ghaednia 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.
Ghaednia, Hamid, et al.. (2025). The Impact of AI on the Development of Multimodal Wearable Devices in Musculoskeletal Medicine. HSS Journal® The Musculoskeletal Journal of Hospital for Special Surgery. 21(3). 314–324. 1 indexed citations
2.
Schwab, Joseph H., et al.. (2025). Development of a dynamic acoustic phantom for simulating human tissue. The Journal of the Acoustical Society of America. 157(4_Supplement). A307–A307.
3.
Oosterhoff, Jacobien H. F., David Shin, Daniel G. Tobert, et al.. (2024). A deep learning approach using an ensemble model to autocreate an image-based hip fracture registry. SHILAP Revista de lepidopterología. 7(1S). e283–e283. 3 indexed citations
4.
Hughes, Elizabeth, Jonathan Norton, C. X. F. Lam, et al.. (2024). A novel concept of an acoustic ultrasound wearable for early detection of implant failure. Scientific Reports. 14(1). 31326–31326. 1 indexed citations
5.
Kosa, Mehmet, et al.. (2024). Stairway to Heaven: A Gamified VR Journey for Breath Awareness. 1–19. 6 indexed citations
6.
Ranganathan, Noopur, David Shin, Hamid Ghaednia, et al.. (2023). Using machine learning in the prediction of symptomatic venous thromboembolism following ankle fracture. Foot and Ankle Surgery. 30(2). 110–116. 5 indexed citations
7.
Ghaednia, Hamid, et al.. (2023). Distinguishing enchondroma from low-grade chondrosarcoma using a multi-stage deep learning model for patient radiographs and histopathology.. Journal of Clinical Oncology. 41(16_suppl). e23500–e23500.
8.
Owens, Crystal E., Kartik M. Varadarajan, A. John Hart, et al.. (2023). Comparing machine learning algorithms for non-invasive detection and classification of failure in piezoresistive bone cement via electrical impedance tomography. Review of Scientific Instruments. 94(12). 2 indexed citations
9.
Ghaednia, Hamid, et al.. (2023). Generating synthetic samples of chondrosarcoma histopathology with a denoising diffusion probabilistic model.. Journal of Clinical Oncology. 41(16_suppl). e13592–e13592. 1 indexed citations
10.
Ashkani‐Esfahani, Soheil, et al.. (2022). Unmonitored Patient Demographic Data Changes can Lead to Bias in Reported Outcomes and Data Registry Development. SHILAP Revista de lepidopterología. 10(1). 216–217. 1 indexed citations
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13.
Karhade, Aditya V., Ophélie Lavoie-Gagné, Nicole Agaronnik, et al.. (2021). Natural language processing for prediction of readmission in posterior lumbar fusion patients: which free-text notes have the most utility?. The Spine Journal. 22(2). 272–277. 25 indexed citations
14.
Ghaednia, Hamid, Mitchell S. Fourman, Amanda Lans, et al.. (2021). Augmented and virtual reality in spine surgery, current applications and future potentials. The Spine Journal. 21(10). 1617–1625. 137 indexed citations
15.
Chen, Emily, et al.. (2021). EIT-kit: An Electrical Impedance Tomography Toolkit for Health and Motion Sensing. 400–413. 33 indexed citations
16.
Oosterhoff, Jacobien H. F., Quirina C. B. S. Thio, Olivier Q. Groot, et al.. (2021). Integration of automated predictive analytics into electronic health records: Can spine surgery applications lead the way using SMART on FHIR and CDS Hooks?. Seminars in Spine Surgery. 33(2). 100870–100870. 4 indexed citations
17.
Ghaednia, Hamid, et al.. (2018). Strain Hardening From Elastic–Perfectly Plastic to Perfectly Elastic Flattening Single Asperity Contact. Journal of Tribology. 141(3). 21 indexed citations
18.
Kardel, Kamran, Hamid Ghaednia, Andres L. Carrano, & Dan B. Marghitu. (2017). Experimental and theoretical modeling of behavior of 3D-printed polymers under collision with a rigid rod. Additive manufacturing. 14. 87–94. 29 indexed citations
19.
Ghaednia, Hamid, et al.. (2015). A comprehensive study of the elasto-plastic contact of a sphere and a flat. Tribology International. 93. 78–90. 91 indexed citations
20.
Ghaednia, Hamid, Dan B. Marghitu, & Robert L. Jackson. (2014). Predicting the Permanent Deformation After the Impact of a Rod With a Flat Surface. Journal of Tribology. 137(1). 43 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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