Pathologist-trained machine learning classifiers developed to quantitate celiac disease features differentiate endoscopic biopsies according to modified marsh score and dietary intervention response.
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Full Title: Diagn Pathol
Abbreviation: Diagn Pathol
Country: Unknown
Publisher: Unknown
Language: N/A
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"Declarations Ethics approval and consent to participateThe Institutional Review Board of Wake Forest University School of Medicine (IRB #00074626) approved the use of archival samples. Consent for publicationNot applicable. Competing interestsAMG, ADF, MDS, DB, JCH, AES, KG and KMC are employees and shareholders of Eli Lilly and Company. HL, XZ, and EDH are employees of Wake Forest School of Medicine. No other competing interests exist. Competing interests AMG, ADF, MDS, DB, JCH, AES, KG and KMC are employees and shareholders of Eli Lilly and Company. HL, XZ, and EDH are employees of Wake Forest School of Medicine. No other competing interests exist."
"Funding This work was supported by Eli Lilly and Co."
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Last Updated: Aug 05, 2025