Federico Mento

2.6k total citations · 2 hit papers
47 papers, 1.2k citations indexed

About

Federico Mento is a scholar working on Critical Care and Intensive Care Medicine, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Federico Mento has authored 47 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Critical Care and Intensive Care Medicine, 27 papers in Radiology, Nuclear Medicine and Imaging and 17 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Federico Mento's work include Ultrasound in Clinical Applications (44 papers), Radiology practices and education (16 papers) and Phonocardiography and Auscultation Techniques (13 papers). Federico Mento is often cited by papers focused on Ultrasound in Clinical Applications (44 papers), Radiology practices and education (16 papers) and Phonocardiography and Auscultation Techniques (13 papers). Federico Mento collaborates with scholars based in Italy, United States and Israel. Federico Mento's co-authors include Libertario Demi, Andrea Smargiassi, Riccardo Inchingolo, Gino Soldati, Tiziano Perrone, Elena Torri, Francesco Tursi, Danilo Buonsenso, Elisa Eleonora Mossolani and Domenica Federica Briganti and has published in prestigious journals such as The Journal of the Acoustical Society of America, IEEE Transactions on Medical Imaging and Applied Soft Computing.

In The Last Decade

Federico Mento

41 papers receiving 1.2k citations

Hit Papers

Proposal for International Standardization of the Use of ... 2020 2026 2022 2024 2020 2020 100 200 300 400
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.

  1. Proposal for International Standardization of the Use of Lung Ultrasound for Patients With COVID‐19
    2020 415 citations Gino Soldati, Andrea Smargiassi et al. Journal of Ultrasound in Medicine profile →
  2. Is There a Role for Lung Ultrasound During the COVID‐19 Pandemic?
    2020 311 citations Gino Soldati, Andrea Smargiassi et al. Journal of Ultrasound in Medicine profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Federico Mento Italy 17 1.1k 759 534 180 114 47 1.2k
Tiziano Perrone Italy 16 999 0.9× 712 0.9× 524 1.0× 132 0.7× 65 0.6× 53 1.3k
Elena Torri Italy 14 821 0.8× 610 0.8× 454 0.9× 99 0.6× 50 0.4× 40 1.2k
Andrea Smargiassi Italy 24 1.9k 1.8× 1.1k 1.5× 1.1k 2.1× 312 1.7× 163 1.4× 96 2.4k
Elisa Eleonora Mossolani Italy 6 713 0.7× 506 0.7× 378 0.7× 75 0.4× 26 0.2× 9 836
Gebhard Mathis Germany 15 1.0k 1.0× 557 0.7× 574 1.1× 112 0.6× 48 0.4× 38 1.2k
Domenica Federica Briganti Italy 7 615 0.6× 443 0.6× 353 0.7× 69 0.4× 21 0.2× 21 823
Charlotte Arbelot France 13 1.5k 1.4× 449 0.6× 938 1.8× 147 0.8× 22 0.2× 29 1.9k
Maurizio Bartolucci Italy 18 380 0.4× 336 0.4× 712 1.3× 71 0.4× 11 0.1× 45 1.2k
Mauro F. Frascisco Italy 14 1.1k 1.0× 504 0.7× 428 0.8× 81 0.5× 21 0.2× 23 1.3k
Giovanni Volpicelli Italy 11 602 0.6× 336 0.4× 222 0.4× 55 0.3× 22 0.2× 17 655

Countries citing papers authored by Federico Mento

Since Specialization
Citations

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

Fields of papers citing papers by Federico Mento

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Federico Mento

This figure shows the co-authorship network connecting the top 25 collaborators of Federico Mento. A scholar is included among the top collaborators of Federico Mento 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 Federico Mento. Federico Mento 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.
Afrakhteh, Sajjad, et al.. (2025). Lung ultrasound video scoring using a novel motion-aware segmentation technique: Toward automated neonatal LUS scoring. Computers in Biology and Medicine. 198(Pt B). 111244–111244.
2.
Mento, Federico, Sajjad Afrakhteh, Tiziano Perrone, et al.. (2025). Synthetic Lung Ultrasound Data Generation Using Autoencoder With Generative Adversarial Network. IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control. 72(5). 624–635. 1 indexed citations
3.
Mento, Federico, G. A. Pierro, Tiziano Perrone, et al.. (2025). Fully automated Quantitative Lung Ultrasound spectroscopy for the differential diagnosis of lung diseases: The first multicenter in-vivo clinical study. Computers in Biology and Medicine. 200. 111365–111365.
4.
Mento, Federico, et al.. (2024). Lung Ultrasound Spectroscopy Applied to the Differential Diagnosis of Pulmonary Diseases: An In Vivo Multicenter Clinical Study. IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control. 71(10). 1217–1232. 2 indexed citations
5.
Mento, Federico, et al.. (2024). Novel Quantitative Lung Ultrasound Spectroscopy Approach for Diseases Classification. Institutional Research Information System (Università degli Studi di Trento). 1–4.
6.
Mento, Federico, et al.. (2023). Ultrasound multifrequency strategy applied to the estimation of lung surface roughness. The Journal of the Acoustical Society of America. 153(3_supplement). A188–A188. 1 indexed citations
7.
Afrakhteh, Sajjad, Federico Mento, Andrea Smargiassi, et al.. (2023). Coronavirus disease 2019 patients prognostic stratification based on low complex lung ultrasound video compression. The Journal of the Acoustical Society of America. 153(3_supplement). A189–A189. 1 indexed citations
8.
Afrakhteh, Sajjad, Federico Mento, Andrea Smargiassi, et al.. (2023). Low-complexity lung ultrasound video scoring by means of intensity projection-based video compression. Computers in Biology and Medicine. 169. 107885–107885. 8 indexed citations
9.
Afrakhteh, Sajjad, Federico Mento, Elena Torri, et al.. (2023). Benchmark methodological approach for the application of artificial intelligence to lung ultrasound data from COVID-19 patients: From frame to prognostic-level. Ultrasonics. 132. 106994–106994. 21 indexed citations
10.
Mento, Federico, et al.. (2023). Ultrasound multifrequency strategy to estimate the lung surface roughness, in silico and in vitro results. Ultrasonics. 135. 107143–107143. 5 indexed citations
11.
Mento, Federico, Francesco Tursi, Andrea Smargiassi, et al.. (2022). Multi-objective automatic analysis of lung ultrasound data from COVID-19 patients by means of deep learning and decision trees. Applied Soft Computing. 133. 109926–109926. 18 indexed citations
12.
Mento, Federico, Francesco Faita, Andrea Smargiassi, et al.. (2022). State of the Art in Lung Ultrasound, Shifting from Qualitative to Quantitative Analyses. Ultrasound in Medicine & Biology. 48(12). 2398–2416. 27 indexed citations
13.
Afrakhteh, Sajjad, Federico Mento, Francesco Tursi, et al.. (2022). Automatic Scoring of COVID-19 LUS Videos Using Cross-correlation-Based Features Aggregated from Frame-Level Confidence Levels Obtained by a Pre-trained Deep Neural Network. 2022 IEEE International Ultrasonics Symposium (IUS). 1–3. 1 indexed citations
14.
Mento, Federico, Antonio Di Sabatino, Anna Fiengo, et al.. (2022). Automatically Scoring Lung Ultrasound Videos of COVID-19 and post-COVID-19 Patients. 2022 IEEE International Ultrasonics Symposium (IUS). 1–4. 1 indexed citations
15.
Peschiera, Emanuele, Federico Mento, & Libertario Demi. (2021). Numerical study on lung ultrasound B-line formation as a function of imaging frequency and alveolar geometries. The Journal of the Acoustical Society of America. 149(4). 2304–2311. 16 indexed citations
16.
Soldati, Gino, Andrea Smargiassi, Tiziano Perrone, et al.. (2021). There is a Validated Acquisition Protocol for Lung Ultrasonography in COVID‐19 Pneumonia. Journal of Ultrasound in Medicine. 40(12). 2783–2783. 4 indexed citations
17.
Carrer, Leonardo, Elena Donini, Daniele Marinelli, et al.. (2020). Automatic Pleural Line Extraction and COVID-19 Scoring From Lung Ultrasound Data. IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control. 67(11). 2207–2217. 60 indexed citations
18.
Mento, Federico, Tiziano Perrone, Francesco Tursi, et al.. (2020). On the Impact of Different Lung Ultrasound Imaging Protocols in the Evaluation of Patients Affected by Coronavirus Disease 2019. Journal of Ultrasound in Medicine. 40(10). 2235–2238. 36 indexed citations
19.
Mento, Federico, Gino Soldati, Andrea Smargiassi, et al.. (2020). Automated segmentation and scoring of lung ultrasound images of COVID-19 patients. The Journal of the Acoustical Society of America. 148(4_Supplement). 2735–2735.
20.
Soldati, Gino, Andrea Smargiassi, Riccardo Inchingolo, et al.. (2020). Time for a new international evidence‐based recommendations for point‐of‐care lung ultrasound. Journal of Ultrasound in Medicine. 40(2). 433–434. 5 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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