使用卷积神经网络提取的超声波图像特征,在腹腔镜卵巢囊切术中预先预测延长的手术时间
Jisoo Kim1, Hyemi Bak2, Myung Eun Jang2
1Department of Artificial Intelligence, Jeju National University, 102 Jejudaehak-ro, Jeju-si, Jeju Special Self-Governing Province, 63243, Republic of Korea.
Journal of minimally invasive gynecology
|February 1, 2026
概括
这项研究确定了临床因素和由卷积神经网络 (CNN) 衍生的超声波特征,这些特征预测了腹腔镜卵巢囊切除术中延长的手术时间. 整合这些因素可以提高外科手术规划的预测准确度.
科学领域:
- 妇科外科手术 妇科外科
- 医学成像分析 医学成像分析
- 医疗保健中的机器学习
背景情况:
- 在腹腔镜卵巢囊切除术中延长的手术时间可能会影响患者的治疗结果和资源分配.
- 预测手术持续时间延长的预测因素对于手术前规划至关重要.
研究的目的:
- 确定腹腔镜卵巢囊切除术期间手术时间延长的临床和成像预测因素.
- 评估由卷积神经网络 (CNN) 衍生的超声波特征在预测操作持续时间中的增量值.
主要方法:
- 对247名接受腹腔镜卵巢囊切除术的患者进行了回顾性队列研究.
- 使用临床变量和CNN衍生的超声波特征开发后勤回归模型.
- 具有和没有成像特征的模型之间的预测精度的比较.
主要成果:
- 机器人手术,双侧卵巢囊和CA-125水平升高与手术时间延长有关.
- 来自CNN的超声波特征也独立地预测了更长的操作持续时间.
- 组合模型显示预测准确度的非显著增加 (AUC 0.889到0.920).
结论:
- 结合临床和CNN衍生成像特征的综合模型可以预测腹腔镜卵巢囊切除术中长时间的手术时间.
- 这种方法显示了手术前风险分层和手术安排的潜力.
- 需要进一步的外部验证来确认其在手术规划和资源管理中的实用性.
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