Marco Toschi

648 total citations · 1 hit paper
14 papers, 382 citations indexed

About

Marco Toschi is a scholar working on Reproductive Medicine, Public Health, Environmental and Occupational Health and Molecular Biology. According to data from OpenAlex, Marco Toschi has authored 14 papers receiving a total of 382 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Reproductive Medicine, 6 papers in Public Health, Environmental and Occupational Health and 5 papers in Molecular Biology. Recurrent topics in Marco Toschi's work include Reproductive Biology and Fertility (6 papers), Assisted Reproductive Technology and Twin Pregnancy (4 papers) and Sperm and Testicular Function (3 papers). Marco Toschi is often cited by papers focused on Reproductive Biology and Fertility (6 papers), Assisted Reproductive Technology and Twin Pregnancy (4 papers) and Sperm and Testicular Function (3 papers). Marco Toschi collaborates with scholars based in United States, Spain and United Kingdom. Marco Toschi's co-authors include Zev Rosenwaks, Jonas Malmsten, Marcos Meseguer, Nikica Zaninović, Qiansheng Zhan, Cristina Hickman, Olivier Elemento, Iman Hajirasouliha, Pegah Khosravi and Ehsan Kazemi and has published in prestigious journals such as The Journal of Urology, Fertility and Sterility and Placenta.

In The Last Decade

Marco Toschi

14 papers receiving 371 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 enables robust assessment and selection of human blastocysts after in vitro fertilization

2019 272 citations Pegah Khosravi, Ehsan Kazemi et al. npj Digital Medicine profile →

Peers

Marco Toschi

PHEOH

RM

PPCH

MB

HI

Rohan Pooniwala United States

Jonas Malmsten United States

Pantelis Zisimopoulos Israel

Hemanth Kandula United States

Cristina Hickman United Kingdom

Adrian Johnston Australia

Adolfo Flores-Saiffe Farías Mexico

Leslie B. Ramirez United States

Tracey Griffiths United Kingdom

Rohan Pooniwala United States

Marco Toschi

216 ×1.0

PHEOH

124 ×0.7

RM

142 ×1.0

PPCH

27 ×0.4

MB

39 ×1.1

HI

Citations per year, relative to Marco Toschi Marco Toschi (= 1×) peers Rohan Pooniwala

Countries citing papers authored by Marco Toschi

Since Specialization

Citations

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

Fields of papers citing papers by Marco Toschi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marco Toschi

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

All Works

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14 of 14 papers shown

1.

Pellegrini, Livia, et al.. (2024). The effect of male factors on embryo morphokinetics: a retrospective analysis of 2726 blastocysts. Journal of Assisted Reproduction and Genetics. 41(12). 3467–3475. 4 indexed citations

2.

Bori, Lorena, et al.. (2024). External validation of a fully automated evaluation tool: a retrospective analysis of 68,471 scored embryos. Fertility and Sterility. 123(4). 634–643. 4 indexed citations

3.

Toschi, Marco, et al.. (2023). WHAT HAPPENS WHEN THE EMBRYOLOGIST'S OPINION DOES NOT MATCH THE DECISION MADE BY AN ARTIFICIAL INTELLIGENCE-BASED MODEL?. Fertility and Sterility. 120(4). e75–e76. 1 indexed citations

4.

Toschi, Marco, et al.. (2023). ReLight My NeRF: A Dataset for Novel View Synthesis and Relighting of Real World Objects. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 20762–20772. 14 indexed citations

5.

Khosravi, Pegah, Ehsan Kazemi, Qiansheng Zhan, et al.. (2019). Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization. npj Digital Medicine. 2(1). 21–21. 272 indexed citations breakdown →

6.

Meseguer, Marcos, et al.. (2019). Is there any room to improve embryo selection? artificial intelligence technology applied for ive birth prediction on blastocysts. Fertility and Sterility. 112(3). e77–e77. 1 indexed citations

7.

He, Chloe, Marcos Meseguer, Marco Toschi, et al.. (2019). Artificial intelligence assessment of time-lapse images can predict with 77% accuracy whether a human embryo capable of achieving a pregnancy will miscarry. Fertility and Sterility. 112(3). e38–e39. 5 indexed citations

8.

Zaninović, Nikica, José Celso Rocha, Qiansheng Zhan, et al.. (2018). Application of artificial intelligence technology to increase the efficacy of embryo selection and prediction of live birth using human blastocysts cultured in a time-lapse incubator. Fertility and Sterility. 110(4). e372–e373. 9 indexed citations

9.

Gunnala, V., Irina Matei, Marco Toschi, et al.. (2018). Characterization of human pre-implantation embryonic exosomes. Fertility and Sterility. 110(4). e81–e81. 3 indexed citations

10.

Malmsten, Jonas, Nikica Zaninović, Qiansheng Zhan, et al.. (2018). Automatic prediction of embryo cell stages using artificial intelligence convolutional neural network. Fertility and Sterility. 110(4). e360–e360. 4 indexed citations

11.

Zaninović, Nikica, Pegah Khosravi, Iman Hajirasouliha, et al.. (2018). Assessing human blastocyst quality using artificial intelligence (AI) convolutional neural network (CNN). Fertility and Sterility. 110(4). e89–e89. 4 indexed citations

12.

Navarro, Juan Carlos, Ramón Risco, Marco Toschi, & Glenn Schattman. (2008). Gene Therapy and Intracytoplasmatic Sperm Injection (ICSI) – A Review. Placenta. 29. 193–199. 6 indexed citations

13.

Bendikson, K., Q.V. Neri, Takumi Takeuchi, et al.. (2008). The Outcome of Intracytoplasmic Sperm Injection Using Occasional Spermatozoa in the Ejaculate of Men With Spermatogenic Failure. The Journal of Urology. 180(3). 1060–1064. 53 indexed citations

14.

Neri, Q.V., Noriko Tanaka, Takumi Takeuchi, et al.. (2006). O-32. Fertility and Sterility. 86(3). S14–S15. 2 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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