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Analysis of institutional authors

Bermejo-Pelaez DCorresponding AuthorLin LCorresponding AuthorLuengo-Oroz MCorresponding Author

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October 22, 2021
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Article

Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection

Publicated to:Plos Neglected Tropical Diseases. 15 (9): e0009677- - 2021-09-01 15(9), DOI: 10.1371/journal.pntd.0009677

Authors: Dacal, Elena; Bermejo-Pelaez, David; Lin, Lin; Alamo, Elisa; Cuadrado, Daniel; Martinez, Alvaro; Mousa, Adriana; Postigo, Maria; Soto, Alicia; Sukosd, Endre; Vladimirov, Alexander; Mwandawiro, Charles; Gichuki, Paul; Williams, Nana Aba; Munoz, Jose; Kepha, Stella; Luengo-Oroz, Miguel

Affiliations

Hosp Clin Univ Barcelona, Barcelona Inst Global Hlth ISGlobal, Barcelona, Spain - Author
Kenya Med Res Inst KEMRI, Eastern & Southern Africa Ctr Int Parasite Contro, Nairobi, Kenya - Author
Spotlab, Madrid, Spain - Author
Univ Politecn Madrid, ETSI Telecomunicac, Biomed Image Technol, Madrid, Spain - Author

Abstract

Soil-transmitted helminths (STH) are the most prevalent pathogens among the group of neglected tropical diseases (NTDs). The Kato-Katz technique is the diagnosis method recommended by the World Health Organization (WHO) although it often presents a decreased sensitivity in low transmission settings and it is labour intensive. Visual reading of Kato-Katz preparations requires the samples to be analyzed in a short period of time since its preparation. Digitizing the samples could provide a solution which allows to store the samples in a digital database and perform remote analysis. Artificial intelligence (AI) methods based on digitized samples can support diagnosis by performing an objective and automatic quantification of disease infection. In this work, we propose an end-to-end pipeline for microscopy image digitization and automatic analysis of digitized images of STH. Our solution includes (a) a digitization system based on a mobile app that digitizes microscope samples using a 3D printed microscope adapter, (b) a telemedicine platform for remote analysis and labelling, and (c) novel deep learning algorithms for automatic assessment and quantification of parasitological infections by STH. The deep learning algorithm has been trained and tested on 51 slides of stool samples containing 949 Trichuris spp. eggs from 6 different subjects. The algorithm evaluation was performed using a cross-validation strategy, obtaining a mean precision of 98.44% and a mean recall of 80.94%. The results also proved the potential of generalization capability of the method at identifying different types of helminth eggs. Additionally, the AI-assisted quantification of STH based on digitized samples has been compared to the one performed using conventional microscopy, showing a good agreement between measurements. In conclusion, this work has presented a comprehensive pipeline using smartphone-assisted microscopy. It is integrated with a telemedicine platform for automatic image analysis and quantification of STH infection using AI models.

Keywords

AlgorithmsAnimalsDeep learningHumansMicroscopyTelemedicineTrichuriasisTrichuris

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Plos Neglected Tropical Diseases due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2021, it was in position 2/24, thus managing to position itself as a Q1 (Primer Cuartil), in the category Tropical Medicine. Notably, the journal is positioned above the 90th percentile.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.84. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 1.61 (source consulted: FECYT Feb 2024)
  • Field Citation Ratio (FCR) from Dimensions: 15.12 (source consulted: Dimensions Jul 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-08, the following number of citations:

  • WoS: 16
  • Scopus: 25
  • Europe PMC: 11

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-08:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 98.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 137 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 9.
  • The number of mentions on the social network X (formerly Twitter): 14 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Kenya.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: Last Author (Luengo Oroz, Miguel Angel).

the authors responsible for correspondence tasks have been BERMEJO PELAEZ, DAVID, LIN, LIN and Luengo Oroz, Miguel Angel.