Cric searchable image database as a public platform for conventional pap smear cytology data

Amidst the current health crisis and social distancing, telemedicine has become an important part of mainstream of healthcare, and building and deploying computational tools to support screening more efficiently is an increasing medical priority. The early identification of cervical cancer precursor lesions by Pap smear test can identify candidates for subsequent treatment. However, one of the main challenges is the accuracy of the conventional method, often subject to high rates of false negative. While machine learning has been highlighted to reduce the limitations of the test, the absence of high-quality curated datasets has prevented strategies development to improve cervical cancer screening. The Center for Recognition and Inspection of Cells (CRIC) platform enables the creation of CRIC Cervix collection, currently with 400 images (1,376 x 1,020 pixels) curated from conventional Pap smears, with manual classification of 11,534 cells. This collection has the potential to advance current efforts in training and testing machine learning algorithms for the automation of tasks as part of the cytopathological analysis in the routine work of laboratories.

Automated summary

Amidst the current health crisis and social distancing measures, telemedicine has become increasingly important in healthcare, with the development of computational tools to support more efficient screening being a top priority. While the early identification of cervical cancer precursor lesions through Pap smear tests is crucial, the challenge lies in the accuracy of the traditional method and the lack of high-quality datasets for improving screening strategies. The CRIC platform's collection of cervical images has the potential to advance the automation of cytopathological analysis tasks in laboratories through machine learning algorithms.