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Analysis of institutional authors

Iglesias, GuillermoCorresponding AuthorTalavera, EdgarAuthorDiaz-Alvarez, AlbertoAuthorGarcia-Remesal, MiguelAuthor

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June 24, 2024
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Artificial intelligence model for tumoral clinical decision support systems

Publicated to: Computer Methods And Programs In Biomedicine. 253 108228- - 2024-08-01 253(), DOI: 10.1016/j.cmpb.2024.108228

Authors:

Iglesias, G; Talavera, E; Troya, J; Díaz-Alvarez, A; García-Remesal, M
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Affiliations

Infanta Leonor Univ Hosp, Madrid, Spain - Author
Univ Politecn Madrid, Escuela Tecn Super Ingn Informat, Dept Inteligencia Artificial, Biomed Informat Grp, Madrid, Spain - Author
Univ Politecn Madrid, Escuela Tecn Super Ingn Sistemas Informat, Dept Sistemas Informat, Madrid, Spain - Author
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Abstract

Background and Objective Comparative diagnostic in brain tumor evaluation makes possible to use the available information of a medical center to compare similar cases when a new patient is evaluated. By leveraging Artificial Intelligence models, the proposed system is able of retrieving the most similar cases of brain tumors for a given query. The primary objective is to enhance the diagnostic process by generating more accurate representations of medical images, with a particular focus on patient -specific normal features and pathologies. A key distinction from previous models lies in its ability to produce enriched image descriptors solely from binary information, eliminating the need for costly and difficult to obtain tumor segmentation. Methods The proposed model uses Artificial Intelligence to detect patient features to recommend the most similar cases from a database. The system not only suggests similar cases but also balances the representation of healthy and abnormal features in its design. This not only encourages the generalization of its use but also aids clinicians in their decision -making processes. This generalization makes possible for future research in different medical diagnosis areas with almost not any change in the system. Results We conducted a comparative analysis of our approach in relation to similar studies. The proposed architecture obtains a Dice coefficient of 0.474 in both tumoral and healthy regions of the patients, which outperforms previous literature. Our proposed model excels at extracting and combining anatomical and pathological features from brain Magnetic Resonances (MRs), achieving state-of-the-art results while relying on less expensive label information. This substantially reduces the overall cost of the training process. Our findings highlight the significant potential for improving the efficiency and accuracy of comparative diagnostics and the treatment of tumoral pathologies. Conclusions This paper provides substantial grounds for further exploration of the broader applicability and optimization of the proposed architecture to enhance clinical decision -making. The novel approach presented in this work marks a significant advancement in the field of medical diagnosis, particularly in the context of Artificial Intelligence -assisted image retrieval, and promises to reduce costs and improve the quality of patient care using Artificial Intelligence as a support tool instead of a black box system.
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Keywords

AlgorithmAlgorithmsArticleArtificial intelligenceBrainBrain neoplasmsBrain tumorBrain tumorsClinical decision support systemClinical decision support systemsClinical featureComparative diagnosticContent based retrievalContent-based image retrievalContents-based image retrievalsControlled studyCost reductionDatabases, factualDecision makingDecision support systemsDecision support systems, clinicalDeep learningDiagnosisDiagnostic accuracyDiagnostic imagingFactual databaseFeature extractionFeatures extractionGeneralisationHumanHumansImage enhancementImage retrievalImage segmentationImplementatioIntelligence modelsMagnetic resonance imaginMagnetic resonance imagingMedical imagingNuclear magnetic resonance imagingPathologyQuery processingSearch enginesSimilar caseTumors

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Computer Methods And Programs In Biomedicine 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, 2024 there are still no calculated indicators, but in 2023, it was in position 20/147, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Theory & Methods.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-12-21:

  • Scopus: 2
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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 2025-12-21:

  • 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: 54.
  • 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: 48 (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: 21.
  • The number of mentions on the social network X (formerly Twitter): 8 (Altmetric).
  • The number of mentions in news outlets: 2 (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.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/84407/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 144
  • Downloads: 25
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (IGLESIAS HERNANDEZ, GUILLERMO) and Last Author (GARCIA REMESAL, MIGUEL).

the author responsible for correspondence tasks has been IGLESIAS HERNANDEZ, GUILLERMO.

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