Valentina Pedoia

4.6k total citations · 1 hit paper
171 papers, 3.1k citations indexed

About

Valentina Pedoia is a scholar working on Surgery, Biomedical Engineering and Rheumatology. According to data from OpenAlex, Valentina Pedoia has authored 171 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 89 papers in Surgery, 87 papers in Biomedical Engineering and 86 papers in Rheumatology. Recurrent topics in Valentina Pedoia's work include Osteoarthritis Treatment and Mechanisms (79 papers), Lower Extremity Biomechanics and Pathologies (60 papers) and Knee injuries and reconstruction techniques (52 papers). Valentina Pedoia is often cited by papers focused on Osteoarthritis Treatment and Mechanisms (79 papers), Lower Extremity Biomechanics and Pathologies (60 papers) and Knee injuries and reconstruction techniques (52 papers). Valentina Pedoia collaborates with scholars based in United States, Italy and Germany. Valentina Pedoia's co-authors include Sharmila Majumdar, Berk Norman, Richard B. Souza, Thomas M. Link, C. Benjamin, Thomas M. Link, Xiaojuan Li, Favian Su, Francesco Calivá and Drew A. Lansdown and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Scientific Reports.

In The Last Decade

Valentina Pedoia

163 papers receiving 3.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Use of 2D U-Net Convolutional Neural Networks for Automated Cartilage and Meniscus Segmentation of Knee MR Imaging Data to Determine Relaxometry and Morphometry

2018 284 citations Valentina Pedoia, Sharmila Majumdar et al. Radiology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Valentina Pedoia United States	33	1.6k	1.4k	1.2k	689	545	171	3.1k
Richard Kijowski United States	41	2.6k 1.6×	1.8k 1.3×	1.4k 1.2×	1.7k 2.5×	1.2k 2.1×	121	5.3k
Sven Nebelung Germany	24	635 0.4×	567 0.4×	494 0.4×	635 0.9×	214 0.4×	149	2.1k
Jérôme Thevenot Finland	15	514 0.3×	623 0.5×	353 0.3×	250 0.4×	283 0.5×	52	1.4k
Christopher F. Beaulieu United States	30	1.0k 0.6×	380 0.3×	391 0.3×	724 1.1×	384 0.7×	62	2.2k
Salvatore Gitto Italy	23	659 0.4×	272 0.2×	410 0.4×	651 0.9×	391 0.7×	106	1.9k
V. Bousson France	33	1.3k 0.8×	451 0.3×	578 0.5×	495 0.7×	1.2k 2.2×	106	3.1k
Julio Carballido‐Gamio United States	38	1.9k 1.2×	1.7k 1.3×	1.4k 1.2×	1.0k 1.5×	1.3k 2.4×	96	4.1k
Jan Borggrefe Germany	35	368 0.2×	160 0.1×	1.3k 1.1×	1.6k 2.3×	233 0.4×	192	3.6k
Sam-Sun Lee South Korea	22	318 0.2×	249 0.2×	431 0.4×	300 0.4×	84 0.2×	119	2.0k
Alain Lalande France	27	387 0.2×	183 0.1×	420 0.4×	1.0k 1.5×	140 0.3×	146	2.7k

Countries citing papers authored by Valentina Pedoia

Since Specialization

Citations

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

Fields of papers citing papers by Valentina Pedoia

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Valentina Pedoia

This figure shows the co-authorship network connecting the top 25 collaborators of Valentina Pedoia. A scholar is included among the top collaborators of Valentina Pedoia 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 Valentina Pedoia. Valentina Pedoia is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Hammond, Eric, et al.. (2024). HIGHER PEAK HIP FLEXION DURING STAIR AMBULATION IS ASSOCIATED WITH HIP OSTEOARTHRITIS PROGRESSION. Osteoarthritis and Cartilage. 32. S249–S250.

Hammond, Eric, Misung Han, Spencer C. Behr, et al.. (2024). The relationships between patellofemoral bone remodeling, cartilage composition, and vertical loading rate: PET/MRI in isolated patellofemoral osteoarthritis. Osteoarthritis and Cartilage. 32(12). 1591–1600. 3 indexed citations

Gassert, Felix G., et al.. (2023). Synthetic Inflammation Imaging with PatchGAN Deep Learning Networks. Bioengineering. 10(5). 516–516. 3 indexed citations

Han, Misung, et al.. (2023). Feasibility of Simultaneous Bilateral Hip Quantitative MRI. Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition. 1 indexed citations

Cummings, Jennifer, Kenneth T. Gao, Vincent Chen, et al.. (2023). The knee connectome: A novel tool for studying spatiotemporal change in cartilage thickness. Journal of Orthopaedic Research®. 42(1). 43–53. 2 indexed citations

Han, Misung, et al.. (2023). Effects of T_1p Characteristics of Load‐Bearing Hip Cartilage on Bilateral Knee Patellar Cartilage Subregions: Subjects With None to Moderate Radiographic Hip Osteoarthritis. Journal of Magnetic Resonance Imaging. 60(1). 186–202. 1 indexed citations

Li, Steven, Dean Chou, Valentina Pedoia, et al.. (2023). Deep learning for automated, interpretable classification of lumbar spinal stenosis and facet arthropathy from axial MRI. European Radiology. 33(5). 3435–3443. 26 indexed citations

Souza, Richard B., et al.. (2023). Local Patterns in 2‐Year T_1ρ and T₂ Changes of Hip Cartilage Are Related to Sex and Functional Data: A Prospective Evaluation on Hip Osteoarthritis Participants. Journal of Magnetic Resonance Imaging. 57(4). 1 indexed citations

Han, Misung, et al.. (2023). Synthetic Knee MRI T1p Maps as an Avenue for Clinical Translation of Quantitative Osteoarthritis Biomarkers. Bioengineering. 11(1). 17–17. 2 indexed citations

10.

Pedoia, Valentina, Atul J. Butte, Karine Louati, et al.. (2022). Use of machine learning in osteoarthritis research: a systematic literature review. RMD Open. 8(1). e001998–e001998. 43 indexed citations

11.

Joseph, Gabby B., Charles E. McCulloch, Michael C. Nevitt, et al.. (2021). Weight Cycling and Knee Joint Degeneration in Individuals with Overweight or Obesity: Four‐Year Magnetic Resonance Imaging Data from the Osteoarthritis Initiative. Obesity. 29(5). 909–918. 5 indexed citations

12.

Oeding, Jacob F., et al.. (2021). Posterior Tibial Slope, Notch Width, Condylar Morphology, Trochlear Inclination, and Tibiofemoral Mismatch Predict Outcomes Following Anterior Cruciate Ligament Reconstruction. Arthroscopy The Journal of Arthroscopic and Related Surgery. 38(5). 1689–1704.e1. 7 indexed citations

13.

Joseph, Gabby B., Charles E. McCulloch, Jae Ho Sohn, et al.. (2021). AI MSK clinical applications: cartilage and osteoarthritis. Skeletal Radiology. 51(2). 331–343. 24 indexed citations

14.

Han, Misung, et al.. (2021). Magnetization‐prepared spoiled gradient‐echo snapshot imaging for efficient measurement of R₂‐R_1ρ in knee cartilage. Magnetic Resonance in Medicine. 87(2). 733–745. 6 indexed citations

15.

Calivá, Francesco, et al.. (2020). Addressing The False Negative Problem of Deep Learning MRI Reconstruction Models by Adversarial Attacks and Robust Training. 121–135. 11 indexed citations

16.

Namiri, Nikan K., Bruno A. A. Nunes, Rutwik Shah, et al.. (2020). Deep Learning for Hierarchical Severity Staging of Anterior Cruciate Ligament Injuries from MRI. Radiology Artificial Intelligence. 2(4). e190207–e190207. 49 indexed citations

17.

Schacky, Claudio E. von, Jae Ho Sohn, Felix Liu, et al.. (2020). Development and Validation of a Multitask Deep Learning Model for Severity Grading of Hip Osteoarthritis Features on Radiographs. Radiology. 295(1). 136–145. 75 indexed citations

18.

Pedoia, Valentina, et al.. (2020). Lumbar intervertebral disc characterization through quantitative MRI analysis: An automatic voxel‐based relaxometry approach. Magnetic Resonance in Medicine. 84(3). 1376–1390. 15 indexed citations

19.

Calivá, Francesco, Andrew P. Leynes, Rutwik Shah, et al.. (2020). Breaking Speed Limits with Simultaneous Ultra-Fast MRI Reconstruction and Tissue Segmentation. 94–110. 6 indexed citations

20.

Kijowski, Richard, Fang Liu, Francesco Calivá, & Valentina Pedoia. (2019). Deep Learning for Lesion Detection, Progression, and Prediction of Musculoskeletal Disease. Journal of Magnetic Resonance Imaging. 52(6). 1607–1619. 72 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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