Kenneth A. Philbrick

3.0k total citations
50 papers, 2.0k citations indexed

About

Kenneth A. Philbrick is a scholar working on Epidemiology, Physiology and Surgery. According to data from OpenAlex, Kenneth A. Philbrick has authored 50 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Epidemiology, 11 papers in Physiology and 9 papers in Surgery. Recurrent topics in Kenneth A. Philbrick's work include Adipokines, Inflammation, and Metabolic Diseases (11 papers), Adipose Tissue and Metabolism (9 papers) and Regulation of Appetite and Obesity (8 papers). Kenneth A. Philbrick is often cited by papers focused on Adipokines, Inflammation, and Metabolic Diseases (11 papers), Adipose Tissue and Metabolism (9 papers) and Regulation of Appetite and Obesity (8 papers). Kenneth A. Philbrick collaborates with scholars based in United States, Thailand and Belarus. Kenneth A. Philbrick's co-authors include Bradley J. Erickson, Alexander D. Weston, Urszula T. Iwaniec, Zeynettin Akkus, Russell T. Turner, Panagiotis Korfiatis, Timothy L. Kline, Carmen P. Wong, Adam J. Branscum and Jason Cai and has published in prestigious journals such as PLoS ONE, Scientific Reports and Radiology.

In The Last Decade

Kenneth A. Philbrick

46 papers receiving 2.0k citations

Peers

Kenneth A. Philbrick
Kimerly Powell United States
Qiang Wu China
Evrim Türkbey United States
Dong Wook Kim South Korea
Calum Gray United Kingdom
Gabriela Czanner United Kingdom
Hechmi Toumi France
Zhijian Wang China
Kimerly Powell United States
Kenneth A. Philbrick
Citations per year, relative to Kenneth A. Philbrick Kenneth A. Philbrick (= 1×) peers Kimerly Powell

Countries citing papers authored by Kenneth A. Philbrick

Since Specialization
Citations

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

Fields of papers citing papers by Kenneth A. Philbrick

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kenneth A. Philbrick. 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 Kenneth A. Philbrick. The network helps show where Kenneth A. Philbrick may publish in the future.

Co-authorship network of co-authors of Kenneth A. Philbrick

This figure shows the co-authorship network connecting the top 25 collaborators of Kenneth A. Philbrick. A scholar is included among the top collaborators of Kenneth A. Philbrick 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 Kenneth A. Philbrick. Kenneth A. Philbrick 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.
Philbrick, Kenneth A., et al.. (2025). Maintaining and broadening DICOM adoption in digital pathology: A response to “wearing a fur coat in the summertime”. Journal of Pathology Informatics. 19. 100517–100517.
2.
Horibe, Masayasu, Naoki Takahashi, Alexander D. Weston, et al.. (2022). Association between computerized tomography (CT) study of body composition and severity of acute pancreatitis: Use of a novel Z-score supports obesity paradox. Clinical Nutrition. 41(8). 1676–1679. 4 indexed citations
3.
Takahashi, Hiroaki, Kotaro Yoshida, Akira Kawashima, et al.. (2022). Impact of measurement method on interobserver variability of apparent diffusion coefficient of lesions in prostate MRI. PLoS ONE. 17(5). e0268829–e0268829. 3 indexed citations
4.
Zhang, Kuan, Haoji Hu, Kenneth A. Philbrick, et al.. (2022). SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks. Tomography. 8(2). 905–919. 64 indexed citations
5.
Rouzrokh, Pouria, Taghi Ramazanian, Cody C. Wyles, et al.. (2021). Deep Learning Artificial Intelligence Model for Assessment of Hip Dislocation Risk Following Primary Total Hip Arthroplasty From Postoperative Radiographs. The Journal of Arthroplasty. 36(6). 2197–2203.e3. 48 indexed citations
6.
Rouzrokh, Pouria, Cody C. Wyles, Kenneth A. Philbrick, et al.. (2021). A Deep Learning Tool for Automated Radiographic Measurement of Acetabular Component Inclination and Version After Total Hip Arthroplasty. The Journal of Arthroplasty. 36(7). 2510–2517.e6. 70 indexed citations
7.
Conte, Gian Marco, Alexander D. Weston, Kenneth A. Philbrick, et al.. (2021). Generative Adversarial Networks to Synthesize Missing T1 and FLAIR MRI Sequences for Use in a Multisequence Brain Tumor Segmentation Model. Radiology. 299(2). 313–323. 76 indexed citations
8.
9.
Boonrod, Arunnit, Zeynettin Akkus, M. Regina Castro, et al.. (2021). Thyroid Nodule Size as a Predictor of Malignancy in Follicular and Hurthle Neoplasms. Asian Pacific Journal of Cancer Prevention. 22(8). 2597–2602. 10 indexed citations
10.
Cai, Jason, Zeynettin Akkus, Kenneth A. Philbrick, et al.. (2020). Fully Automated Segmentation of Head CT Neuroanatomy Using Deep Learning. Radiology Artificial Intelligence. 2(5). e190183–e190183. 29 indexed citations
11.
Akkus, Zeynettin, Jason Cai, Arunnit Boonrod, et al.. (2019). A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence–Powered Ultrasound for Improving Clinical Workflow. Journal of the American College of Radiology. 16(9). 1318–1328. 206 indexed citations
12.
Philbrick, Kenneth A., Alexander D. Weston, Zeynettin Akkus, et al.. (2019). RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning. Journal of Digital Imaging. 32(4). 571–581. 78 indexed citations
13.
Philbrick, Kenneth A., et al.. (2018). Polyethylene particles inserted over calvarium induce cancellous bone loss in femur in female mice. Bone Reports. 9. 84–92. 7 indexed citations
14.
Philbrick, Kenneth A., Adam J. Branscum, Carmen P. Wong, Russell T. Turner, & Urszula T. Iwaniec. (2018). Leptin Increases Particle-Induced Osteolysis in Female ob/ob Mice. Scientific Reports. 8(1). 14790–14790. 5 indexed citations
15.
Poinar, George O., et al.. (2017). X-ray microcomputed tomography reveals putative trematode metacercaria in a 100 million year-old lizard (Squamata: Agamidae). Cretaceous Research. 80. 27–30. 5 indexed citations
16.
Wang, Wendan, Kenneth A. Philbrick, Xujuan Yang, et al.. (2017). Low calcium diet increases 4T1 mammary tumor carcinoma cell burden and bone pathology in mice. PLoS ONE. 12(7). e0180886–e0180886. 2 indexed citations
17.
Erickson, Bradley J., Panagiotis Korfiatis, Zeynettin Akkus, Timothy L. Kline, & Kenneth A. Philbrick. (2017). Toolkits and Libraries for Deep Learning. Journal of Digital Imaging. 30(4). 400–405. 119 indexed citations
18.
Iwaniec, Urszula T., Kenneth A. Philbrick, Carmen P. Wong, et al.. (2016). Room temperature housing results in premature cancellous bone loss in growing female mice: implications for the mouse as a preclinical model for age-related bone loss. Osteoporosis International. 27(10). 3091–3101. 55 indexed citations
19.
Depner, Christopher M., Kenneth A. Philbrick, & Donald Β. Jump. (2013). Docosahexaenoic Acid Attenuates Hepatic Inflammation, Oxidative Stress, and Fibrosis without Decreasing Hepatosteatosis in a Ldlr Mouse Model of Western Diet-Induced Nonalcoholic Steatohepatitis. Journal of Nutrition. 143(3). 315–323. 120 indexed citations
20.
Philbrick, Kenneth A. & Michael J. Pavol. (2007). Spatial Requirements for an Accessible Aircraft Lavatory.

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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