Deep Learning Models for Early Detection of Ovarian Cancer: A Systematic Review of Ultrasound-Based Diagnostic Tools
DOI:
https://doi.org/10.70112/ajeat-2024.13.2.4246Keywords:
Ovarian Cancer (OC), Deep Learning, Early Detection, Ultrasound Imaging, Artificial Intelligence (AI)Abstract
Ovarian Cancer (OC) remains one of the most lethal gynecological malignancies, with a global 5-year survival rate of only 45%. This project aimed to assess the potential of deep learning-based diagnostic tools in improving early detection of ovarian cancer, particularly through ultrasound imaging. The primary research problem lies in the challenge of accurately diagnosing OC at an early stage, as traditional imaging techniques often rely on subjective interpretations, leading to inconsistent results. To address this issue, a systematic review was conducted following PRISMA guidelines, evaluating studies published between January 2018 and May 2023 in the Scopus and PubMed databases. These studies employed deep learning or artificial intelligence models for the diagnosis, prognosis, or identification of OC from ultrasound images. Of the 101 studies screened, 9 met the inclusion criteria. The included studies reported diagnostic accuracies of deep learning models ranging from 75% to 100%, with sensitivities between 85% and 99%. The conclusions indicate that deep learning models significantly enhance the diagnostic accuracy of ovarian cancer, offering a promising non-invasive tool for early detection. This research underscores the importance of integrating AI technologies into clinical practice to improve survival outcomes for OC patients.
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