Michael Shifrin

522 total citations
60 papers, 340 citations indexed

About

Michael Shifrin is a scholar working on Radiology, Nuclear Medicine and Imaging, Epidemiology and Electrical and Electronic Engineering. According to data from OpenAlex, Michael Shifrin has authored 60 papers receiving a total of 340 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Radiology, Nuclear Medicine and Imaging, 9 papers in Epidemiology and 9 papers in Electrical and Electronic Engineering. Recurrent topics in Michael Shifrin's work include Radiomics and Machine Learning in Medical Imaging (10 papers), Artificial Intelligence in Healthcare and Education (8 papers) and Advancements in Photolithography Techniques (6 papers). Michael Shifrin is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (10 papers), Artificial Intelligence in Healthcare and Education (8 papers) and Advancements in Photolithography Techniques (6 papers). Michael Shifrin collaborates with scholars based in Russia, United States and Israel. Michael Shifrin's co-authors include Gleb Danilov, Potapov Aa, Vladimir Zelman, О Н Ершова, Alexander Kulikov, Yu. N. Orlov, Peeter Ross, Igor Pronin, Brigitte Séroussi and Simon de Lusignan and has published in prestigious journals such as SHILAP Revista de lepidopterología, Marine Biology and Neuro-Oncology.

In The Last Decade

Michael Shifrin

46 papers receiving 322 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael Shifrin Russia 11 59 57 56 55 43 60 340
Lionel Tim‐Ee Cheng Singapore 10 79 1.3× 77 1.4× 69 1.2× 50 0.9× 164 3.8× 31 453
Devon Livingstone Canada 12 13 0.2× 31 0.5× 20 0.4× 21 0.4× 21 0.5× 16 446
Prathit A. Kulkarni United States 12 34 0.6× 23 0.4× 15 0.3× 141 2.6× 20 0.5× 33 753
Jesse M. Smith United States 12 47 0.8× 11 0.2× 81 1.4× 41 0.7× 255 5.9× 31 628
Yahan Yang China 12 22 0.4× 22 0.4× 38 0.7× 12 0.2× 269 6.3× 52 554
Walaa Alsharif Saudi Arabia 8 54 0.9× 30 0.5× 31 0.6× 26 0.5× 126 2.9× 35 279
Daniel Rodriguez Gutierrez United States 14 20 0.3× 23 0.4× 64 1.1× 18 0.3× 220 5.1× 41 576
Gamuchirai Tavaziva Canada 4 30 0.5× 146 2.6× 29 0.5× 34 0.6× 177 4.1× 7 706
Abdulaziz A. Qurashi Saudi Arabia 9 53 0.9× 75 1.3× 31 0.6× 20 0.4× 175 4.1× 33 342
Chunhua Yang China 3 16 0.3× 78 1.4× 45 0.8× 44 0.8× 182 4.2× 4 509

Countries citing papers authored by Michael Shifrin

Since Specialization
Citations

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

Fields of papers citing papers by Michael Shifrin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Shifrin

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Shifrin. A scholar is included among the top collaborators of Michael Shifrin 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 Michael Shifrin. Michael Shifrin 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, Il-Hwan, et al.. (2024). SEM overlay target design optimization by e-beam simulation. 50–50.
2.
Danilov, Gleb, et al.. (2023). The Assessment of Glioblastoma Metabolic Activity via 11C-Methionine PET and Radiomics. Studies in health technology and informatics. 302. 972–976. 2 indexed citations
3.
Kirby, Patrick, Michael Shifrin, Jean-Marie Cuillerot, et al.. (2023). An optimized IL-12-Fc expands its therapeutic window, achieving strong activity against mouse tumors at tolerable drug doses. Med. 4(5). 326–340.e5. 10 indexed citations
4.
Danilov, Gleb, et al.. (2023). Data Quality Estimation Via Model Performance: Machine Learning as a Validation Tool. Studies in health technology and informatics. 305. 369–372. 1 indexed citations
5.
Danilov, Gleb, et al.. (2022). MR-guided non-invasive typing of brain gliomas using machine learning. Burdenko s Journal of Neurosurgery. 86(6). 36–36. 1 indexed citations
6.
Danilov, Gleb, et al.. (2022). Artificial intelligence technologies in clinical neurooncology. Burdenko s Journal of Neurosurgery. 86(6). 127–127. 1 indexed citations
7.
Usachev, D Yu, et al.. (2021). Nosocomial meningitis laboratory criteria in ICU patients: 5-year surveillance. SHILAP Revista de lepidopterología. 18(5). 47–56. 1 indexed citations
8.
Ершова, О Н, et al.. (2021). Risk factors of nosocomial meningitis in neurological intensive care unit. Results of a five-year prospective study. Burdenko s Journal of Neurosurgery. 85(6). 83–83. 2 indexed citations
9.
Danilov, Gleb, et al.. (2020). Predicting Postoperative Hospital Stay in Neurosurgery with Recurrent Neural Networks Based on Operative Reports. Studies in health technology and informatics. 270. 382–386. 8 indexed citations
10.
Danilov, Gleb, et al.. (2020). Classification of Intracranial Hemorrhage Subtypes Using Deep Learning on CT Scans. Studies in health technology and informatics. 272. 370–373. 31 indexed citations
11.
Danilov, Gleb, et al.. (2020). Semiautomated Approach for Muscle Weakness Detection in Clinical Texts. Studies in health technology and informatics. 272. 55–58. 1 indexed citations
12.
Danilov, Gleb, et al.. (2020). Artificial Intelligence in Neurosurgery: a Systematic Review Using Topic Modeling. Part I: Major Research Areas. Sovremennye tehnologii v medicine. 12(5). 106–106. 24 indexed citations
13.
Danilov, Gleb, et al.. (2020). Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives. Sovremennye tehnologii v medicine. 12(6). 111–111. 14 indexed citations
14.
Kozlov, Andrew, et al.. (2020). Radiation-induced meningiomas: analysis of 33 cases. Burdenko s Journal of Neurosurgery. 84(3). 53–53.
15.
Gol'bin, D A, Michael Shifrin, A. V. Revishchin, et al.. (2020). Specialized biorepository for human brain glioma: project development and operational experience. 9(4). 39–49. 4 indexed citations
16.
Danilov, Gleb, et al.. (2019). An Information Extraction Algorithm for Detecting Adverse Events in Neurosurgery Using Documents Written in a Natural Rich-in-Morphology Language. Studies in health technology and informatics. 262. 194–197. 4 indexed citations
17.
Ершова, О Н, et al.. (2018). Healthcare-associated ventriculitis and meningitis in a neuro-ICU: Incidence and risk factors selected by machine learning approach. Journal of Critical Care. 45. 95–104. 41 indexed citations
18.
Shifrin, Michael, et al.. (2018). Neurological semiotics of benign craniofacial tumors. S S Korsakov Journal of Neurology and Psychiatry. 118(4). 13–13.
19.
Wong, Darren, Gleb Danilov, Michael Shifrin, et al.. (2018). Implementing an infection control and prevention program decreases the incidence of healthcare-associated infections and antibiotic resistance in a Russian neuro-ICU. Antimicrobial Resistance and Infection Control. 7(1). 94–94. 35 indexed citations
20.
Shifrin, Michael, et al.. (2015). Life quality of patients with benign tumors of the anterior and middle part of the skull base after surgery and during follow-up. Burdenko s Journal of Neurosurgery. 79(2). 44–44.

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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2026