Manuel Stritt

464 citations
13 papers · 320 · h-index 10

Impact in

Papers in

Manuel Stritt

12 papers receiving 309 citations

Peers

Manuel Stritt
Comparison fields: 5 of 82
  • Biophysics 33
  • Internal Medicine 13
  • Pulmonary and Respiratory Medicine 74
  • Artificial Intelligence 63
  • Epidemiology 46
Replace Tetsuhiro Kakimoto with:
Tetsuhiro Kakimoto Japan
Sally J. O’Shea Ireland
Jiehua Li China
M. Bhattacharjee India
Xuguo Sun China
Michael Passeri United States
Laleh Soltan Ghoraie Canada
K. Takayama Japan
Eva Bozsaky Austria
Linyan Wang China
Manuel Stritt relative to Tetsuhiro Kakimoto Japan Tetsuhiro Kakimoto's profile →
Citations per field
00.5×6.5×
Tetsuhiro Kakimoto · 1×
Citations per year

Countries citing papers authored by Manuel Stritt

Since Specialization
Citations

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

Fields of papers citing papers by Manuel Stritt

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Manuel Stritt, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Manuel Stritt Line = papers co-authored together Manuel Stritt links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1 202098
2 201954
3 201840
4 202225
5 201825
6 201620
7 200617
8 201115
9 20159
10 20079
11
Supervised Machine Learning Methods for Quantification of Pulmonary Fibrosis.
20115
12 20102
13 20071

About Manuel Stritt

Manuel Stritt is a scholar working on Pulmonary and Respiratory Medicine, Artificial Intelligence, Molecular Biology, Information Systems and Epidemiology, having authored 13 papers that have together received 320 indexed citations. Recurring topics across this work include Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (4 papers), Inhalation and Respiratory Drug Delivery (2 papers), Venous Thromboembolism Diagnosis and Management (1 paper), Wikis in Education and Collaboration (1 paper), Medical Imaging Techniques and Applications (1 paper), Service-Oriented Architecture and Web Services (1 paper), Cancer Cells and Metastasis (1 paper) and Renin-Angiotensin System Studies (1 paper). The work is most often cited by research in Biophysics (33 citations), Internal Medicine (13 citations), Pulmonary and Respiratory Medicine (74 citations), Artificial Intelligence (63 citations) and Epidemiology (46 citations). Manuel Stritt has collaborated with scholars based in Switzerland, Germany and United States. Frequent co-authors include Anna K. Stalder, Enrico Vezzali, Oliver Nayler, Lars Schmidt-Thieme, Patrick Hess, Peter Groenen, Urs Lüthi, David F. Kallmes, Waleed Brinjikji and Patrick Sieber. Their work appears in journals such as PLoS ONE, Histopathology, Expert Opinion on Drug Discovery, PLoS Computational Biology and Oncotarget.

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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