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Accepted for/Published in: JMIR Formative Research

Date Submitted: Jun 23, 2025
Date Accepted: Jul 1, 2026

The final, peer-reviewed published version of this preprint can be found here:

Supporting the Learning of Visual Perception Skills in Cytology Through a Computational Tool: Development and Evaluation Study

Keller BNdS, Rezende MT, Oliveira RRR, Costa CRP, Carneiro CM, Campos AG

Supporting the Learning of Visual Perception Skills in Cytology Through a Computational Tool: Development and Evaluation Study

JMIR Form Res 2026;10:e79508

DOI: 10.2196/79508

PMID: 42721484

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Evaluating a Proof of Concept for Support the Learning of Visual Perception Skills on Cytology

  • Breno Nunes de Sena Keller; 
  • Mariana T. Rezende; 
  • Renata R. R. Oliveira; 
  • Cristina R. P. Costa; 
  • Claudia M. Carneiro; 
  • Andrea G. Campos

ABSTRACT

Background:

Advances in hardware and software have transformed activities across various domains by integrating technology to enhance task execution. In education, these processes enable innovative teaching-learning approaches through diverse interaction models, such as web systems, augmented or virtual reality, and mobile devices.

Objective:

This work introduces a computational framework to support theteaching-learning process of visual perception skills, with cervical cytology as a case study.

Methods:

The framework described in the study provides interactive, adaptive exercises that simulate practical laboratory experiences, allowing students to engage with specific diagnoses and rare scenarios. A proof of concept system was implemented and evaluated with undergraduate students and professionals in the cytology area.

Results:

The results indicated no significant difference in student performance compared to the similar approach, suggesting that the new system offers similar if not improved performance while offering better usability and overall user experience. Also, demonstrate the system's effectiveness in supporting cytology learning while minimizing its impact on user routine.

Conclusions:

The study highlights the framework's potential for scalability and adaptation to other disciplines requiring visual analysis, offering a promising avenue for enhancing education through technology.


 Citation

Please cite as:

Keller BNdS, Rezende MT, Oliveira RRR, Costa CRP, Carneiro CM, Campos AG

Supporting the Learning of Visual Perception Skills in Cytology Through a Computational Tool: Development and Evaluation Study

JMIR Form Res 2026;10:e79508

DOI: 10.2196/79508

PMID: 42721484

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