开发一种人工智能模型,以在患有家族腺瘤多重症的患者中识别十二指腺息肉
Daniel Schupack1, Shubham Sood1, Jeffrey Fetzer1
1Mayo Clinic Division of Gastroenterology and Hepatology, Rochester, Minnesota, USA.
iGIE : innovation, investigation and insights
|February 6, 2026
概括
一个AI模型被开发用于检测家族腺瘤多样性 (FAP) 患者的微妙十二指甲状腺息肉. 该模型实现了超过75%的聚检测,为胃肠道癌症监测提供了一个有前途的工具.
科学领域:
- 胃肠病学 胃肠病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 亲属腺瘤多重症 (FAP) 患者患有癌前十二指甲状腺多重症的风险很高.
- 这些息肉在标准内镜监测中很难检测到.
- 人工智能 (AI) 显示出识别胃肠道病理的潜力.
研究的目的:
- 开发和评估一种人工智能模型,用于在FAP患者中自动检测和细分十二指甲状腺息肉.
- 与手动计数相比,评估模型在识别息肉方面的表现.
主要方法:
- 利用了FAP患者的上部肠道内镜图像.
- 手动注释多体,并使用nnU-Net框架进行自动细分.
- 将数据分为培训 (80%),验证 (10%) 和测试 (10%) 集.
- 通过使用子系数和聚数计数指标评估性能.
主要成果:
- 人工智能模型识别了78.6%的息肉,平均每张图像错过了0.9个息肉.
- 观察到一个低的假阳性率 (平均每张图像0.8).
- 聚合物细分的子系数为0.73,其中37.9%的图像具有完全匹配.
结论:
- 一种原型的人工智能模型成功地在FAP患者中识别了十二指腺.
- 尽管在十二指肠解剖学方面存在挑战,但该模型实现了超过75%的多体检测率.
- 需要进一步的前性研究来完善FAP监测中临床使用的算法.
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