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10.1186/s12909-021-02680-1 http://scihub22266oqcxt.onion/10.1186/s12909-021-02680-1 33926437!8084264!33926437     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMC+Med+Educ 2021 ; 21 (1): 248 Nephropedia Template TP {{tp|p=33926437|t=2021. Smartpath(k): a platform for teaching glomerulopathies using machine learning |pdf=|usr=}}{{33926437}} gab.com Text Smartpath(k): a platform for teaching glomerulopathies using machine learning JO: BMC Med Educ http://www.kidney.de/pget.php?&tw=1&pmid=33926437 #FOAvip 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. Twit Text FOAVip Smartpath(k): a platform for teaching glomerulopathies using machine learning ????BMC Med Educ http://www.kidney.de/pget.php?&tw=1&pmid=33926437 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33926437 #FOAvip English Wikipedia <ref>{{Cite pmid|33926437}}</ref> Smartpath(k): a platform for teaching glomerulopathies using machine learning #MMPMID33926437 Aldeman NLS; de Sa Urtiga Aita KM; Machado VP; da Mata Sousa LCD; Coelho AGB; da Silva AS; da Silva Mendes AP; de Oliveira Neres FJ; do Monte SJH BMC Med Educ 2021[Apr]; 21 (1): 248 PMID33926437show ga 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. |*COVID-19[MESH] |*Education, Distance[MESH] |Artificial Intelligence[MESH] |Humans[MESH] |Machine Learning[MESH] |SARS-CoV-2[MESH]Smartpath(k): a platform for teaching glomerulopathies using machine l(epmc).pdf DeepDyve Pubget Overpricing