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Pilot Study of an Open-source Image Analysis Software for Automated Screening of Conventional Cervical Smears

Overview
Journal J Cytol
Specialty Cell Biology
Date 2018 Apr 13
PMID 29643651
Citations 2
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Abstract

Introduction: The Pap stained cervical smear is a screening tool for cervical cancer. Commercial systems are used for automated screening of liquid based cervical smears. However, there is no image analysis software used for conventional cervical smears. The aim of this study was to develop and test the diagnostic accuracy of a software for analysis of conventional smears.

Materials And Methods: The software was developed using Python programming language and open source libraries. It was standardized with images from Bethesda Interobserver Reproducibility Project. One hundred and thirty images from smears which were reported Negative for Intraepithelial Lesion or Malignancy (NILM), and 45 images where some abnormality has been reported, were collected from the archives of the hospital. The software was then tested on the images.

Results: The software was able to segregate images based on overall nuclear: cytoplasmic ratio, coefficient of variation (CV) in nuclear size, nuclear membrane irregularity, and clustering. 68.88% of abnormal images were flagged by the software, as well as 19.23% of NILM images. The major difficulties faced were segmentation of overlapping cell clusters and separation of neutrophils.

Conclusion: The software shows potential as a screening tool for conventional cervical smears; however, further refinement in technique is required.

Citing Articles

Performance characteristics of an artificial intelligence based on convolutional neural network for screening conventional Papanicolaou-stained cervical smears.

Sanyal P, Ganguli P, Barui S Med J Armed Forces India. 2020; 76(4):418-424.

PMID: 33162650 PMC: 7606095. DOI: 10.1016/j.mjafi.2019.08.001.


An evaluation of the construction of the device along with the software for digital archiving, sending the data, and supporting the diagnosis of cervical cancer.

Lasyk L, Barbasz J, Zuk P, Prusaczyk A, Wlodarczyk T, Prokurat E Contemp Oncol (Pozn). 2019; 23(3):174-177.

PMID: 31798334 PMC: 6883966. DOI: 10.5114/wo.2019.85617.

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