在甲状腺超声波中训练诊断人工智能:深度学习有多好是真正的学习?
Oana Lozan1, Petra B Musholt1, Ann-Kathrin Lederer1
1Section of Endocrine Surgery, Department of General, Visceral and Transplantation Surgery, University Medical Center Mainz, Langenbeckstraße 1, 55131, Mainz, Germany.
Updates in surgery
|February 11, 2026
概括
人工智能 (AI) 使用超声波显著提高了甲状腺结节分类的准确性,在软件更新后达到了92.6%的协议. 虽然人工智能是一种可靠的诊断工具,但经验丰富的临床医生对于复杂的病例至关重要.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 在瘤学瘤学.
背景情况:
- 人工智能 (AI) 通过部超声波显示了通过部超声波来增强甲状腺结节的TI-RADS分类的潜力.
- 在甲状腺结节评估中评估AI的现实学习曲线至关重要.
研究的目的:
- 评估AI在甲状腺结节的ACR TI-RADS分类中的学习曲线和性能改进.
- 将人工智能驱动的TI-RADS评估与经验丰富的临床医生的评估进行比较.
主要方法:
- 利用3D超声波PIUR tUS Infinity软件对110名患者176个结节进行基于AI的TI-RADS分类.
- 通过软件更新重复了这项研究,在133名患者中分类了228个结节.
- 将AI分类与经验丰富的内分泌外科医生的评估进行了比较.
主要成果:
- 最初的AI-TI-RADS对应率为73% (128/176个节点),在软件更新后改善到92.6% (210/227个节点).
- 人工智能最初误解了20%的结节有微化;这在更新后下降到4.8%.
- 在更新后,显著差异很少见 (2.6%),主要是在复杂的病例中.
结论:
- 深度学习显著提高了AI在ACR TI-RADS分类中的准确性,特别是在集群中的节点上.
- 人工智能支持的超声波对非炎症组织中的甲状腺结节是有效的,但不能完全取代复杂病例的专家临床医生.
- 在这种应用中,人工智能的快速学习曲线对未来的临床集成非常令人鼓舞.
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