Development of an image-based deep learning algorithm to predict ovarian torsion.

Stephanie L Lim¹, Haoyu Dong², Alice J Darling³, Julia Moyett³, Anthony Swartz³, Maciej A Mazurowski⁴, Arleen Song⁵

¹Division of Minimally Invasive Gynecologic Surgery, Department of Obstetrics & Gynecology, Duke University, Durham, NC, United States. Electronic address: Stephanie.l.lim@duke.edu.

²Department of Electrical & Computer Engineering, Duke University, Durham, NC, United States.

³Department of Obstetrics & Gynecology, Duke University, Durham, NC, United States.

⁴Department of Electrical & Computer Engineering, Duke University, Durham, NC, United States; Department of Computer Science, Duke University, Durham, NC, United States; Department of Radiology, Duke University, Durham, NC, United States.

⁵Division of Minimally Invasive Gynecologic Surgery, Department of Obstetrics & Gynecology, Duke University, Durham, NC, United States.

PubMed DOI

Abstract

BACKGROUND: The aim of this study was to assess the feasibility of developing a deep learning-based algorithm for diagnosing adnexal torsion that incorporates clinical features, sonographic findings, and sonographic images. METHODS: For the development of the machine learning algorithms, retrospective data from 2013 to 2022 were obtained for patients who underwent operative management for concern…

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