乳房超声波人工智能决策支持在评估小侵袭性叶膜癌的作用

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Tali Amir¹, Kristen Coffey¹, Varadan Sevilimedu²

¹Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, United States of America.

Clinical imaging

|June 13, 2023

概括

一个人工智能决策支持系统在超声波上识别侵袭性叶膜癌 (ILC) 时表现出100%的灵敏度,帮助放射科医生. 这种人工智能工具可以提高对诊断微妙ILC病例的信心.

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:

乳腺的侵袭性叶状癌 (ILC) 在超声波上具有可变的外观,潜伏地呈现.
准确及时诊断ILC对于有效的患者管理至关重要.

研究的目的:

评估人工智能 (AI) 决策支持 (DS) 系统的诊断性能,用于使用超声波检测侵入性叶状癌 (ILC).
将AI DS系统的评估与放射科医生的评估进行比较.

主要方法:

在2017年11月至2019年11月期间诊断的83例ILC病例的回顾性审查.
AI DS输出 (损伤特征,恶性瘤可能性) 与放射科医生的评估进行了比较.
记录了ILC特征,如尺寸,形状和回声性.

主要成果:

AI DS系统实现了100%的灵敏度,识别所有ILC为可疑或可能恶性,虚假阴性率为0%.
AI DS可能在检测较小,小于厘米的ILC病变方面提供更大的实用性.
仅通过放射科医生的评估,BI-RADS 5 仅在20%的ILC病例中被分配,这突显了人工智能协助的潜力.

结论:

AI DS系统准确地将所有检测到的ILC病变描述为可疑或可能恶性.
人工智能DS显示出增强放射科医生对ILC超声波评估的信心的潜力.
人工智能可以帮助识别具有挑战性的ILC病例,特别是较小的病变.

关键词:

人工智能的人工智能乳腺癌是什么? 乳腺癌是什么乳房超声波乳房超声波是什么?支持决定的决定支持.侵袭性叶状叶状癌瘤是一种侵袭性叶状叶状癌.

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