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Smartpathk: a platform for teaching glomerulopathies using machine learning

Authors
  • Aldeman, Nayze Lucena Sangreman1
  • de Sá Urtiga Aita, Keylla Maria1
  • Machado, Vinícius Ponte1
  • da Mata Sousa, Luiz Claudio Demes1
  • Coelho, Antonio Gilberto Borges1
  • da Silva, Adalberto Socorro1
  • da Silva Mendes, Ana Paula2
  • de Oliveira Neres, Francisco Jair2
  • do Monte, Semíramis Jamil Hadad1
  • 1 Federal University of Piauí, Teresina, PI, Brazil , Teresina (Brazil)
  • 2 Student of the Computing course at Federal University of Piauí, Teresina, PI, Brazil , Teresina (Brazil)
Type
Published Article
Journal
BMC Medical Education
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Apr 29, 2021
Volume
21
Issue
1
Identifiers
DOI: 10.1186/s12909-021-02680-1
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundWith the emergence of the new coronavirus pandemic (COVID-19), distance learning, especially that mediated by information and digital communication technologies, has been adopted in all areas of knowledge and at all levels, including medical education. Imminently practical areas, such as pathology, have made traditional teaching based on conventional microscopy more flexible through the synergies of computational tools and image digitization, not only to improve teaching-learning but also to offer alternatives to repetitive and exhaustive histopathological analyzes. In this context, machine learning algorithms capable of recognizing histological patterns in kidney biopsy slides have been developed and validated with a view to building computational models capable of accurately identifying renal pathologies. In practice, the use of such algorithms can contribute to the universalization of teaching, allowing quality training even in regions where there is a lack of good nephropathologists. The purpose of this work is to describe and test the functionality of SmartPathk, a tool to support teaching of glomerulopathies using machine learning. The training for knowledge acquisition was performed automatically by machine learning methods using the J48 algorithm to create a computational model of an appropriate decision tree.ResultsAn intelligent system, SmartPathk, was developed as a complementary remote tool in the teaching-learning process for pathology teachers and their students (undergraduate and graduate students), showing 89,47% accuracy using machine learning algorithms based on decision trees.ConclusionThis artificial intelligence system can assist in teaching renal pathology to increase the training capacity of new medical professionals in this area.

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