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High‐throughput image analysis in the diagnosis of papillary thyroid carcinoma
Author(s) -
Hosken Bruno,
Coutinho Endringer Denise,
Prandi Campagnaro Bianca,
Uggere de Andrade Tadeu,
Lenz Dominik
Publication year - 2016
Publication title -
diagnostic cytopathology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.417
H-Index - 65
eISSN - 1097-0339
pISSN - 8755-1039
DOI - 10.1002/dc.23481
Subject(s) - medicine , workflow , thyroid , thyroid carcinoma , thyroid cancer , digital image analysis , incidence (geometry) , carcinoma , throughput , software , papillary carcinoma , artificial intelligence , radiology , pathology , computer science , computer vision , database , telecommunications , physics , optics , wireless , programming language
Background Thyroid cancer is the most common endocrine cancer, and its incidence has been increasing worldwide in the past decades. The increasing demand in medicine for rapid and accurate diagnosis enabled the application of digital imaging analysis in order to increase workflow efficiency and accurate analyses. The present study aimed to automatically differentiate papillary thyroid carcinoma from normal thyroid cells using high‐throughput image analysis. Methods Images of cellular specimens were taken with a digital camera and were subsequently analyzed. Other software was used for machine‐learning‐based cellular diagnostics. Results The two different classes were correctly identified with high sensitivity and specificity. Conclusion The data created offers great potential for an automated diagnosis. Diagn. Cytopathol. 2016;44:574–577. © 2016 Wiley Periodicals, Inc.