AI Article Synopsis
- Liquid-based cytology (LBC) is increasingly replacing traditional cervical cancer smears, enabling the digitization of samples into whole-slide images (WSIs) for AI analysis.
- * The study aimed to assess a deep learning model's ability to classify WSIs from LBC specimens as neoplastic (cancerous) or non-neoplastic, using a dataset of 1605 WSIs.
- * Results showed the model performed well, with ROC AUCs ranging from 0.89 to 0.96 across three test sets, indicating strong potential for AI to enhance cervical cancer screening efficiency.
Article Abstract
Liquid-based cytology (LBC) for cervical cancer screening is now more common than the conventional smears, which when digitised from glass slides into whole-slide images (WSIs), opens up the possibility of artificial intelligence (AI)-based automated image analysis. Since conventional screening processes by cytoscreeners and cytopathologists using microscopes is limited in terms of human resources, it is important to develop new computational techniques that can automatically and rapidly diagnose a large amount of specimens without delay, which would be of great benefit for clinical laboratories and hospitals. The goal of this study was to investigate the use of a deep learning model for the classification of WSIs of LBC specimens into neoplastic and non-neoplastic. To do so, we used a dataset of 1605 cervical WSIs. We evaluated the model on three test sets with a combined total of 1468 WSIs, achieving ROC AUCs for WSI diagnosis in the range of 0.89-0.96, demonstrating the promising potential use of such models for aiding screening processes.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8909106 | PMC |
| http://dx.doi.org/10.3390/cancers14051159 | DOI Listing |
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