基于研究的人工智能算法用于前列腺MRI的临床部署
Stephanie A Harmon1, Jesse Tetreault2, Omer Tarik Esengur3
1National Institutes of Health, Bethesda, USA. stephanie.harmon@nih.gov.
Abdominal radiology (New York)
|May 26, 2025
概括
将人工智能 (AI) 集成到前列腺癌成像图像存档和通信系统 (PACS) 中是可行的. 这种人工智能管道使得护理点的利用成为可能,提高了诊断准确性和活检向性.
科学领域:
- 放射学和医学成像学 医学成像学
- 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) 算法的临床集成到图片存档和通信系统 (PACS) 仍然是一个重大挑战.
- 人工智能驱动的细分工具对于提高放射学解释的效率和准确性至关重要.
研究的目的:
- 在临床PACS环境中整合基于人工智能的前列腺器官和前列腺内病变细分管道.
- 为了使人工智能工具用于前列腺癌诊断和活检规划.
主要方法:
- 之前训练过的前列腺MRI细分的AI模型使用MONAI Deploy Express进行了容器化.
- 建立了一个推断服务器和PACS工作流程,用于实时AI算法评估.
- 在两个阶段进行前性评估:诊断成像队列和基于活检的队列.
主要成果:
- 在一分钟内,在57/58个案例 (第一阶段) 和40/40个案例 (第二阶段) 中成功部署PACS.
- 与独立验证研究相比,AI模型的性能稳定.
- 当人工智能与放射科医生解释一起使用时,改善了癌症检测率.
结论:
- 将多参数AI算法集成到临床PACS中是可行的.
- 人工智能产生的输出可以有效地用于下游临床任务,如活检向.
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