一个经过临床验证的AI框架用于癌检测和表征.
Bohdan Petryshak1,2,3, Mikhail Iljin2, Alina Denissova2,4
1Institute of Computer Science, Tartu University, Tartu, Estonia.
Communications medicine
|November 27, 2025
概括
癌的AI工具BMVision显著减少了放射科医生的报告时间,并提高了病变的诊断灵敏度. 这种人工智能解决方案提高了癌症诊断的准确性和效率.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 细胞癌是一种常见的尿路癌症,通过CT扫描诊断.
- 随着对放射学服务需求的不断增长,癌症的及时和准确诊断变得越来越困难.
- 自动化工具可以提高放射科医生的效率和诊断准确度.
研究的目的:
- 开发和评估BMVision,这是一种用于癌检测和表征的深度学习工具.
- 评估人工智能协助对放射科医生的诊断性能和工作流程效率的影响.
主要方法:
- 开发了BMVision,这是一个带有基于Web的浏览器的深度学习工具.
- 一项两阶段的回顾性读者研究涉及六名放射科医生审查200张扫描图.
- 人工智能辅助和无辅助的工作流程在诊断灵敏度,病变测量,报告效率和放射科医生间协议方面进行了比较.
主要成果:
- BMVision将放射科医生的报告时间平均减少了33% (高达52%).
- 该工具提高了检测良性病变的灵敏度,从79.9%提高到86.3%.
- BMVision导致放射科医生之间的协议大幅增加,并提供了结构化的自动生成报告.
结论:
- BMVision是第一个经过临床验证的商业人工智能工具,用于癌检测和表征.
- 该工具有可能通过提高诊断准确性和报告效率来提高患者护理.
- BMVision可以帮助放射科医生管理对高质量癌症诊断的日益增长的需求.
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