[使用鼻内镜检测鼻癌的综合多任务视觉引导系统:一项回顾性多中心研究]
1ENT Institute and Department of Otolaryngology, Eye & ENT Hospital, Fudan University, Endoscopic Skull Base Tumor Surgery Technology Innovation Unit, Chinese Academy of Medical Sciences, Shanghai 200031, China.
概括
这项研究开发了一个用于鼻癌 (NPC) 诊断的AI系统,集成查,细分和活检定位. 人工智能表现出高灵敏度,有助于早期检测和补充医生专业知识.
科学领域:
- 人工智能在医学中的应用
- 在瘤学瘤学.
- 医疗成像医学成像
背景情况:
- 鼻癌 (NPC) 的诊断严重依赖于内镜检查和活检.
- 准确和早期发现NPC对于患者的治疗结果至关重要.
- 现有的诊断工具可能受到观察者之间的可变性和可访问性的限制.
研究的目的:
- 开发和验证一个可解释的内镜智能系统用于NPC诊断.
- 通过综合的"查-细分-活检定位"模型提供临床决策支持.
- 为了提高诊断准确度,从病变检测到精确的组织采样.
主要方法:
- 一项多中心研究收集了12323张鼻内镜图像 (2018-2022年).
- 使用了一个编码器-多头解码器架构与多任务关节损失函数.
- 模型性能通过单中心训练和多中心测试得到验证,并通过统计学意义的 permutation 和 DeLong 测试进行验证.
主要成果:
- 内部测试显示了高性能:AUC 0.998,灵敏度 91.56%,特异性 99.61%.
- 外部验证证明了概括性:AUC为0.957,灵敏度为97.63%,特异性为76.79%.
- 人工智能灵敏度 (92.00%) 优于大多数医生 (78.40%),而高级医生具有更高的特异性 (95.00%). 细分子系数为0.86,活检部位准确率为67.7%.
结论:
- 成功开发了一种高度敏感的多任务人工智能系统,用于NPC诊断.
- 人工智能系统有效地协助病变检测,细分和活检部位定位.
- 这种人工智能工具补充了医生的诊断能力,潜在地改善了NPC管理.
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