Tang Chenxin, Xu Zhenbin, Duan Hongpeng, Zhang Shengmin
Health Science Center, Ningbo University, Ningbo, China.
Department of Ultrasound Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, China.
Front Oncol. 2025 Jun 12;15:1581157. doi: 10.3389/fonc.2025.1581157. eCollection 2025.
Ovarian cancer, as a common gynecological malignancy, is often found at an advanced stage clinically. Thus, improving the early diagnosis of ovarian cancer is crucial for the survival rate of patients. Ultrasound examination is the main method for ovarian cancer screening, but it is greatly influenced by the operator's experience and technique, increasing the risk of misdiagnosis and missed diagnosis. Artificial intelligence uses computers to learn from input data and has already made significant progress in image recognition. Applying artificial intelligence to ultrasound diagnosis of ovarian cancer can enhance diagnostic accuracy, providing earlier treatment for patients. This article reviews the current application of artificial intelligence in the ultrasound diagnosis of ovarian cancer, in order to provide a reference for subsequent clinical diagnosis and treatment.
卵巢癌作为一种常见的妇科恶性肿瘤，临床上往往在晚期才被发现。因此，提高卵巢癌的早期诊断对患者的生存率至关重要。超声检查是卵巢癌筛查的主要方法，但它受操作者经验和技术的影响很大，增加了误诊和漏诊的风险。人工智能利用计算机从输入数据中学习，在图像识别方面已经取得了显著进展。将人工智能应用于卵巢癌的超声诊断可以提高诊断准确性，为患者提供更早的治疗。本文综述了人工智能在卵巢癌超声诊断中的当前应用，以便为后续的临床诊断和治疗提供参考。
