Open AccessDigital/Computational Technology for Molecular Cytology Testing: A Short Technical Note with Literature Review
Author(s) -
R. Yoshiyuki Osamura,
Naruaki Matsui,
Masato Kawashima,
Hiroyasu Saiga,
Maki Ogura,
Tomoharu Kiyuna
Publication year - 2021
Publication title -
acta cytologica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.513
H-Index - 57
eISSN - 1938-2650
pISSN - 0001-5547
DOI - 10.1159/000515379
Subject(s) - cytopathology , medicine , multiplex , automation , cytology , medical physics , pathology , computer science , bioinformatics , mechanical engineering , engineering , biology
This short article describes the method of digital cytopathology using Z-stack scanning with or without extended focusing. This technology is suitable to observe such thick clusters as adenocarcinoma on cytologic specimens. Artificial intelligence (AI) has been applied to histological images, but its application on cytologic images is still limited. This article describes our attempt to apply AI technology to cytologic digital images. For molecular analysis, cytologic materials, such as smear, LBC, and cell blocks, have been successfully used for targeted single gene detection and multiplex gene analysis with next-generation sequencing. As a future perspective, the system can be connected to full automation by combining digital cytopathology with AI application to detect target cancer cells and to perform molecular analysis. The literature review is updated according to the subjects.