Discriminative diagnosis of ovarian endometriosis cysts and benign mucinous cystadenomas based on the ConvNeXt algorithm.

Journal: European journal of obstetrics, gynecology, and reproductive biology

PMID: 38756053

Abstract

PURPOSE: The objective of this study was to develop a deep learning model, using the ConvNeXt algorithm, that can effectively differentiate between ovarian endometriosis cysts (OEC) and benign mucinous cystadenomas (MC) by analyzing ultrasound images. The performance of the model in the diagnostic differentiation of these two conditions was also evaluated.

Authors

Kuo Miao

Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
Qian Lv

Robotic Minimally Invasive Surgery Center, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.
Liwei Zhang
Ning Zhao
Xiaoqiu Dong

Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China. Electronic address: Dongxq0451@163.com.

Keywords

Adult Algorithms Cystadenoma, Mucinous Deep Learning Diagnosis, Differential Endometriosis Female Humans Middle Aged Ovarian Cysts Ovarian Neoplasms Retrospective Studies Sensitivity and Specificity Ultrasonography

External Resources

View on PubMed Access via DOI PubMed (38756053)

Discriminative diagnosis of ovarian endometriosis cysts and benign mucinous cystadenomas based on the ConvNeXt algorithm.

Abstract

Authors

Keywords

External Resources

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