Andreas Maunz

1.1k total citations · 1 hit paper
25 papers, 626 citations indexed

About

Andreas Maunz is a scholar working on Radiology, Nuclear Medicine and Imaging, Ophthalmology and Computational Theory and Mathematics. According to data from OpenAlex, Andreas Maunz has authored 25 papers receiving a total of 626 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Radiology, Nuclear Medicine and Imaging, 11 papers in Ophthalmology and 9 papers in Computational Theory and Mathematics. Recurrent topics in Andreas Maunz's work include Retinal Imaging and Analysis (13 papers), Retinal Diseases and Treatments (11 papers) and Computational Drug Discovery Methods (8 papers). Andreas Maunz is often cited by papers focused on Retinal Imaging and Analysis (13 papers), Retinal Diseases and Treatments (11 papers) and Computational Drug Discovery Methods (8 papers). Andreas Maunz collaborates with scholars based in Switzerland, United States and Germany. Andreas Maunz's co-authors include Christoph Helma, Fethallah Benmansour, Marco Prunotto, Filippo Arcadu, Jeffrey R. Willis, Zdenka Hašková, Martin Gütlein, Micha Rautenberg, Felix Hammann and Anthony P. Adamis and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Investigative Ophthalmology & Visual Science.

In The Last Decade

Andreas Maunz

23 papers receiving 605 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.

Deep learning algorithm predicts diabetic retinopathy progression in individual patients

2019 188 citations Filippo Arcadu, Fethallah Benmansour et al. npj Digital Medicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Andreas Maunz Switzerland	11	225	161	160	151	59	25	626
Bin Sun China	20	258 1.1×	122 0.8×	10 0.1×	507 3.4×	21 0.4×	79	1.3k
Pan Ma China	14	140 0.6×	91 0.6×	18 0.1×	202 1.3×	32 0.5×	33	906
Haotian Li Singapore	9	81 0.4×	59 0.4×	155 1.0×	117 0.8×	12 0.2×	15	392
Tomasz Arodź United States	12	42 0.2×	3 0.0×	220 1.4×	247 1.6×	72 1.2×	27	601
Ziheng Hu United States	9	33 0.1×	4 0.0×	157 1.0×	191 1.3×	35 0.6×	16	465
Xiaofan Zhang China	13	111 0.5×	48 0.3×	2 0.0×	118 0.8×	33 0.6×	43	493
Jiayu Shen China	6	53 0.2×	5 0.0×	492 3.1×	479 3.2×	19 0.3×	27	1.3k
Xiaoping Gao China	16	26 0.1×	43 0.3×	14 0.1×	302 2.0×	16 0.3×	58	710
Kwanjeera Wanichthanarak Thailand	13	25 0.1×	3 0.0×	50 0.3×	597 4.0×	37 0.6×	29	900
Chuanbiao Wen China	15	24 0.1×	2 0.0×	61 0.4×	169 1.1×	84 1.4×	55	618

Countries citing papers authored by Andreas Maunz

Since Specialization

Citations

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

Fields of papers citing papers by Andreas Maunz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Andreas Maunz

This figure shows the co-authorship network connecting the top 25 collaborators of Andreas Maunz. A scholar is included among the top collaborators of Andreas Maunz 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 Andreas Maunz. Andreas Maunz 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

Gomariz, Álvaro, Yusuke Kikuchi, Thomas Albrecht, et al.. (2025). Joint semi-supervised and contrastive learning enables domain generalization and multi-domain segmentation. PubMed. 103. 103575–103575. 2 indexed citations

Chakravarthy, Usha, Varun Chaudhary, Srinivas R. Sadda, et al.. (2025). Effect of Faricimab versus Aflibercept on Hyperreflective Foci in Patients with Diabetic Macular Edema from the YOSEMITE/RHINE Trials. Ophthalmology Science. 5(5). 100798–100798. 1 indexed citations

Maunz, Andreas, Usha Chakravarthy, Nancy M. Holekamp, et al.. (2024). Intraretinal Hyper-Reflective Foci Are Almost Universally Present and Co-Localize With Intraretinal Fluid in Diabetic Macular Edema. Investigative Ophthalmology & Visual Science. 65(5). 26–26. 8 indexed citations

Jones, Ian L., Andreas Maunz, Thomas Albrecht, et al.. (2024). Artificial intelligence-based analysis of retinal fluid volume dynamics in neovascular age-related macular degeneration and association with vision and atrophy. Eye. 39(1). 154–161. 1 indexed citations

Maunz, Andreas, Michael Kawczynski, Jian S. Dai, et al.. (2023). Machine Learning to Predict Response to Ranibizumab in Neovascular Age-Related Macular Degeneration. SHILAP Revista de lepidopterología. 3(4). 100319–100319. 10 indexed citations

Zarbin, Marco A., Lauren Hill, Andreas Maunz, Martin Gliem, & Ivaylo Stoilov. (2021). Anti-VEGF-resistant subretinal fluid is associated with better vision and reduced risk of macular atrophy. British Journal of Ophthalmology. 106(11). 1561–1566. 13 indexed citations

Maunz, Andreas, Fethallah Benmansour, Thomas Albrecht, et al.. (2021). Accuracy of a Machine-Learning Algorithm for Detecting and Classifying Choroidal Neovascularization on Spectral-Domain Optical Coherence Tomography. Journal of Personalized Medicine. 11(6). 524–524. 6 indexed citations

Jones, Ian L., Andreas Maunz, Thomas Albrecht, et al.. (2020). Development and External Validation of a Machine Learning Model for Predicting Response to anti-VEGF Treatment in Patients with neovascular AMD. Investigative Ophthalmology & Visual Science. 61(9). 1 indexed citations

Maunz, Andreas, Fethallah Benmansour, Yun Li, et al.. (2020). Diagnostic accuracy of a machine-learning algorithm to detect and classify choroidal neovascularization based on SD-OCT in neovascular age-related macular degeneration (nAMD). Investigative Ophthalmology & Visual Science. 61(7). 2649–2649.

10.

Arcadu, Filippo, Fethallah Benmansour, Andreas Maunz, et al.. (2020). Author Correction: Deep learning algorithm predicts diabetic retinopathy progression in individual patients. npj Digital Medicine. 3(1). 160–160. 10 indexed citations

11.

Sahni, Jayashree, et al.. (2019). A machine learning approach to predict response to anti-VEGF treatment in patients with neovascular age-related macular degeneration using SD-OCT. Investigative Ophthalmology & Visual Science. 60(11). 1 indexed citations

12.

Arcadu, Filippo, Fethallah Benmansour, Andreas Maunz, et al.. (2019). Deep learning algorithm for patient-level prediction of diabetic retinopathy (DR) response to vascular endothelial growth factor (VEGF) inhibition. Investigative Ophthalmology & Visual Science. 60(9). 2806–2806. 2 indexed citations

13.

Arcadu, Filippo, Fethallah Benmansour, Andreas Maunz, et al.. (2019). Deep learning algorithm predicts diabetic retinopathy progression in individual patients. npj Digital Medicine. 2(1). 92–92. 188 indexed citations breakdown →

14.

Zoffmann, Sannah, Maarten Vercruysse, Fethallah Benmansour, et al.. (2019). Machine learning-powered antibiotics phenotypic drug discovery. Scientific Reports. 9(1). 5013–5013. 67 indexed citations

15.

Moisan, Annie, Marcel Gubler, Jitao David Zhang, et al.. (2016). Inhibition of EGF Uptake by Nephrotoxic Antisense Drugs In Vitro and Implications for Preclinical Safety Profiling. Molecular Therapy — Nucleic Acids. 6. 89–105. 24 indexed citations

16.

Batke, Monika, Martin Gütlein, Falko Partosch, et al.. (2016). Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties. Frontiers in Pharmacology. 7. 321–321. 5 indexed citations

17.

Piparo, Elena Lo, et al.. (2014). Automated and reproducible read-across like models for predicting carcinogenic potency. Regulatory Toxicology and Pharmacology. 70(1). 370–378. 12 indexed citations

18.

Maunz, Andreas, et al.. (2013). lazar: a modular predictive toxicology framework. Frontiers in Pharmacology. 4. 38–38. 132 indexed citations

19.

Hammann, Felix, et al.. (2009). Development of Decision Tree Models for Substrates, Inhibitors, and Inducers of P-Glycoprotein. Current Drug Metabolism. 10(4). 339–346. 18 indexed citations

20.

Maunz, Andreas & Christoph Helma. (2008). Prediction of chemical toxicity with local support vector regression and activity-specific kernels. SAR and QSAR in environmental research. 19(5-6). 413–431. 29 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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