Logistic models and artificial intelligence in the sonographic assessment of adnexal masses – a systematic review of the literature

Mihaela Grigore, Razvan Mihai Popovici, Dumitru Gafitanu, Loredana Himiniuc, Mara Murarasu, Romeo Micu


Adnexal masses are common, yet challenging, in gynecological practice. Making the differential diagnosis between their benign and malignant condition is essential for optimal surgical management, but reliable pre-surgical differentiation is sometimes difficult using clinical features, ultrasound examination, or tumor markers alone. A possible way to improve the diagnosis is using artificial intelligence (AI) or logistic models developed based on compiling and processing clinical, ultrasound, and tumor marker data together. Ample research has already been conducted in this regard that medical practitioners could benefit from. In this systematic review, we present logistic models and methods using AI, chosen based on their demonstrated high performance in clinical practice. Although some external validation of these models has been performed, further prospective studies are needed in order to select the best model or to create a new, more efficient, one for the pre-surgical evaluation of ovarian masses. 


artificial intelligence; deep learning; diagnosis; image interpretation; computer-assisted

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DOI: http://dx.doi.org/10.11152/mu-2538


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