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Using An Artificial Intelligence Pipeline For Digital Pathology | 98866
ISSN: 2161-0681

Journal of Clinical & Experimental Pathology
Open Access

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Using an artificial intelligence pipeline for digital pathology

Joint Event on 33rd International Conference on Oncology Nursing and Cancer Care and 16th Asia Pacific Pathology Congress

Jia-Chi Wang

Quest Diagnostics, USA

Keynote: J Clin Exp Pathol

DOI: 10.4172/2161-0681-C4-053

Abstract
The utility of an artificial intelligence (AI) pipeline for digital pathology is advancing rapidly as computational capabilities and information technology improve. The AI pipeline involves complicated algorithm design along with harmonized preanalytical image processing to achieve satisfactory sensitivity and specificity. An AI pipeline has been adopted as a tool to assist in screening cytopathology, differential diagnosis and scoring of solid tumors, variant annotation in genomic studies, and karyotyping in cytogenomics. Challenges include the regulatory environment for screening and diagnosis, standardization of platforms and imaging sharing, harmonization of imaging quality and requirements, continual algorithm improvements and revisions, an extensive amount of data analysis and storage, and training and education of digital image readers and technologies.
Biography

Jia-Chi Wang completed his M.D. in Taiwan and his M.Sc. and Ph.D. at McGill University in Canada. He is an associate professor of National Cheng-Kung University (Taiwan) and senior director of cytogenomics and molecular genetics laboratories at Quest Diagnostics. He has published over 40 peer-reviewed papers and serves on an editorial board and reviewer for peer-reviewed journals.

E-mail: [email protected]

 

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