Automated reporting of cervical biopsies using artificial intelligence

Mahnaz Mohammadi, Christina Fell, David Morrison, Sheeba Syed, Prakash Konanahalli, Sarah Bell, Gareth Bryson, Ognjen Arandjelović, David J Harrison and David Harris-Birtill

PLOS Digital Health, 2024, vol. 3, issue 4, 1-27

Abstract: When detected at an early stage, the 5-year survival rate for people with invasive cervical cancer is 92%. Being aware of signs and symptoms of cervical cancer and early detection greatly improve the chances of successful treatment. We have developed an Artificial Intelligence (AI) algorithm, trained and evaluated on cervical biopsies for automated reporting of digital diagnostics. The aim is to increase overall efficiency of pathological diagnosis and to have the performance tuned to high sensitivity for malignant cases. Having a tool for triage/identifying cancer and high grade lesions may potentially reduce reporting time by identifying areas of interest in a slide for the pathologist and therefore improving efficiency. We trained and validated our algorithm on 1738 cervical WSIs with one WSI per patient. On the independent test set of 811 WSIs, we achieved 93.4% malignant sensitivity for classifying slides. Recognising a WSI, with our algorithm, takes approximately 1.5 minutes on the NVIDIA Tesla V100 GPU. Whole slide images of different formats (TIFF, iSyntax, and CZI) can be processed using this code, and it is easily extendable to other formats.Author summary: The majority of biopsies received by pathologists for reporting, do not contain invasive cancer. This yields opportunities for Artificial Intelligence (AI) development in identifying cancerous and high grade lesions in a slide and reduce the necessity of pathologist having to review the whole slide and negative biopsies. We have developed an Artificial Intelligence (AI) algorithm, trained and evaluated on cervical biopsies for automated reporting of digital diagnostics with the aim to increase overall efficiency of pathological diagnosis and to have the performance tuned to high sensitivity for malignant cases. This can potentially reduce reporting time and improve overall efficiency of pathological diagnosis.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0000381

DOI: 10.1371/journal.pdig.0000381

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