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基于人工智能的磁共振成像阅读支持程序 (AMP) 的开发,用于深层子宫内膜异位症诊断

Rie Shiokawa¹, Junichiro Iwasawa², Yumiko Oishi Tanaka³

¹Chugai Pharmaceutical Co., Ltd., 1-1 Nihonbashi-Muromachi 2-chome, Nihonbashi Mitsui Tower (Reception12F), Chuo-ku, Tokyo, 103-8324, Japan. shiokaware@chugai-pharm.co.jp.

Scientific reports

|December 8, 2025

概括

一个人工智能驱动的MRI工具 (AMP) 通过改善病变检测和减少延迟来增强深层子宫内膜异位症的诊断. 这项非侵入性计划支持放射科医生,有可能改善患者的治疗结果.

科学领域:

医疗成像医学成像
人工智能的人工智能
妇女健康妇女健康

背景情况:

子宫内膜异位症的诊断是具有挑战性的,经常延迟,并依赖于侵入性方法.
由于复杂的成像特征,难以非侵入性地诊断深层子宫内膜异位症 (DE).

研究的目的:

开发和评估基于AI的MRI阅读支持程序 (AMP) 以改善深层子宫内膜异位症诊断.
为了提高放射科医生的诊断准确度和在检测DE病变和粘附时的效率.

主要方法:

AMP集成了三个AI模型:nnU-Net用于子宫内膜板块细分,LightGBM用于粘附检测,nnU-Net用于卵巢子宫内膜囊 (OEC) 检测/定量.
使用交叉验证和与放射科医生的初步临床实用性研究来评估模型性能.

主要成果:

AMP实现了0.293的斑块细分和0.580的OEC细分的Dice平均相似系数.
在子宫粘附检测方面观察到高性能 (F1分数>0.6).
在一项临床实用性研究中,AMP从0.73提高了放射科医生对斑块检测的平均回忆力,从0.73提高到0.91.

结论:

AMP显示出作为深层子宫内膜异位症诊断的可靠非侵入性工具的潜力.
人工智能计划可以帮助放射科医生识别微妙的DE病变和粘附,从而最大限度地减少诊断延迟并改善患者的治疗结果.

关键词:

人工智能的人工智能是人工智能.深度子宫内膜异位症是什么诊断诊断诊断诊断诊断这就是为什么MRI是MRI.机器学习是机器学习.放射科医生放射科医生

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