Smartpath k : a platform for teaching glomerulopathies using machine learning.

Autor: Aldeman NLS; Department of Specialized Medicine, Federal University of Piauí, Teresina, PI, Brazil. nayzealdeman@gmail.com., de Sá Urtiga Aita KM; Open and distance education center and computer scientist of the Immunogenetics and Molecular Biology Laboratory (LIB - UFPI), Federal University of Piauí, Teresina, PI, Brazil., Machado VP; Department of Computing and computer scientist of the Immunogenetics and Molecular Biology Laboratory (LIB - UFPI), Federal University of Piauí, Teresina, PI, Brazil., da Mata Sousa LCD; Department of Computing and computer scientist of the Immunogenetics and Molecular Biology Laboratory (LIB - UFPI), Federal University of Piauí, Teresina, PI, Brazil., Coelho AGB; Systems analyst at the Immunogenetics and Molecular Biology Laboratory, Federal University of Piauí, Teresina, PI, Brazil., da Silva AS; Department of Biology and vice coordinator of the Immunogenetics and Molecular Biology Laboratory (LIB - UFPI), Federal University of Piauí, Teresina, PI, Brazil., da Silva Mendes AP; Student of the Computing course at Federal University of Piauí, Teresina, PI, Brazil., de Oliveira Neres FJ; Student of the Computing course at Federal University of Piauí, Teresina, PI, Brazil., do Monte SJH; Department of General Clinic and coordinator of the Immunogenetics and Molecular Biology Laboratory (LIB - UFPI), Federal University of Piauí, Teresina, PI, Brazil.
Jazyk: angličtina
Zdroj: BMC medical education [BMC Med Educ] 2021 Apr 29; Vol. 21 (1), pp. 248. Date of Electronic Publication: 2021 Apr 29.
DOI: 10.1186/s12909-021-02680-1
Abstrakt: Background: With 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 SmartPath k , 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.
Results: An intelligent system, SmartPath k , 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.
Conclusion: This artificial intelligence system can assist in teaching renal pathology to increase the training capacity of new medical professionals in this area.
Databáze: MEDLINE
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