动态的人工智能超声波智能辅助诊断系统的诊断价值,用于哈西莫托甲状腺炎患者的良性和恶性甲状腺结节
Bing Wang1, Zheng Wan1, Mingbo Zhang2
1Senior Department of General Surgery, the First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China.
Quantitative imaging in medicine and surgery
|June 7, 2023
概括
一个动态的人工智能 (AI) 系统改善了哈希莫托甲状腺炎 (HT) 患者的甲状腺结节诊断. 与传统方法相比,这种人工智能工具提供了更高的准确性和更低的误诊率,有助于手术决策.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 哈西莫托甲状腺炎 (HT) 对甲状腺结节的诊断提出了挑战.
- 准确区分良性和恶性结节对于患者管理至关重要.
研究的目的:
- 评估动态人工智能 (AI) 超声波智能助理诊断系统 (动态AI) 对哈西莫托甲状腺炎 (HT) 患者良性和恶性甲状腺结节的诊断价值.
- 评估动态AI在指导HT患者甲状腺结节的手术治疗策略中的重要性.
主要方法:
- 收集了487名患者的数据 (154名有HT,333名没有HT) 患有829个经过手术的甲状腺结节.
- 利用动态AI进行结节分化和评估诊断效应 (特异性,敏感性,准确性等). ) 的情况.
- 对手术前超声波 (ACR TI-RADS) 和细针吸入细胞学 (FNAC) 的动态AI诊断疗效进行比较.
主要成果:
- 动态AI实现了88.06%的准确度,80.19%的特异性和90.68%的灵敏度,显示了与术后病理学 (κ=0.690) 的一致性.
- 动态AI的诊断疗效在HT患者和无HT患者之间是可比的.
- 在HT患者中,动态AI的特异性优越,误诊率低于ACR TI-RADS超声波,敏感性更高,误诊率低于FNAC.
结论:
- 动态AI为HT患者的良性和恶性甲状腺结节提供了增强的诊断价值.
- 这种人工智能系统提供了一种新的方法和有价值的信息,用于诊断和管理HT的甲状腺节块.
- 动态AI有助于指导HT患者甲状腺结节的手术治疗策略.
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