Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Ulcerative colitis (UC) is an intractable disease that affects young adults. Histological findings are essential for its diagnosis; however, the number of diagnostic pathologists is limited. Herein, we used a no-code artificial intelligence (AI) platform "Teachable Machine" to train a model that could distinguish between histological images of UC, non-UC coloproctitis, adenocarcinoma, and control. A total of 5100 histological images for training and 900 histological images for testing were prepared by pathologists. Our model showed accuracies of 0.99, 1.00, 0.99, and 0.99, for UC, non-UC coloproctitis, adenocarcinoma, and control, respectively. This is the first report in which a no-code easy AI platform has been able to comprehensively recognize the distinctive histologic patterns of UC.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1177/10668969231204955 | DOI Listing |
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