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Performance of an Artificial Intelligence Model for Recognition and Quantitation of Histologic Features of Eosinophilic Esophagitis on Biopsy Samples.

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作者单位: Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota. [1] Aiforia Technologies, Worcester, Massachusetts. [2] Aiforia Technologies, Helsinki, Finland. [3] Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota. [4] Department of Pediatric and Adolescence Medicine, Mayo Clinic, Rochester, Minnesota; Department of Gastroenterology and Hepatology, Mayo Clinic Rochester, Minnesota. [5] Department of Pathology, Vanderbilt University Medical Center, Nashville, Tennessee. [6] Department of Gastroenterology and Hepatology, Mayo Clinic Rochester, Minnesota. [7] Department of Pathology, Allina Hospitals and Clinics, Minneapolis, Minnesota. [8] Division of Gastroenterology and Hepatology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina. [9] Department of Gastroenterology & Hepatology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands. [10] Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, Arizona. [11] Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota. Electronic address: moreira.roger@mayo.edu. [12]
DOI 10.1016/j.modpat.2023.100285
PMID 37474003
发布时间 2023-10-20
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Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc

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