一个全器官视觉语言模型,用于可概括的3DCT表示
Cameron Beeche1,2, Joonghyun Kim1, Hamed Tavolinejad2,3
1Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
medRxiv : the preprint server for health sciences
|July 9, 2025
概括
珀西瓦尔是一种新的AI基础模型,通过对各种数据进行训练,提高了计算机断层扫描 (CT) 医学成像的概括性. 这种人工智能工具提高了临床工作流程的效率,并揭示了疾病表型.
科学领域:
- 医疗成像中的人工智能
- 医疗保健基金会模型的基础
- 放射学人工智能的人工智能
背景情况:
- 现有的计算机断层扫描 (CT) 成像AI模型由于狭窄的培训环境而缺乏通用性.
- 有限的解剖覆盖,对比设置和临床指示限制了当前人工智能工具的现实应用.
- 存在对人工智能模型的需求,这些模型可以有效地处理广泛的体积CT成像数据.
研究的目的:
- 介绍Percival,一个新的视觉语言基础模型用于CT医学成像.
- 提高AI模型在临床放射学工作流程中的通用性.
- 评估帕西瓦尔发现的临床相关性和潜在结构.
主要方法:
- 开发了Percival,一个双编码器视觉语言基础模型.
- 培训了珀西瓦尔在超过40万张CT卷和来自宾夕法尼亚大学医学生物银行的配对放射学报告上.
- 使用基于变压器的图像编码器和BERT风格的语言编码器,通过对称对比学习对齐.
- 在超过20,000名参与者的成像数据 (100,000+ CT 卷) 上验证了 Percival.
主要成果:
- 与在有限数据上训练的模型相比,珀西瓦尔在图像-文本回忆任务中表现出卓越的表现.
- 通过关联研究和生存分析评估了珀西瓦尔的临床知识.
- 在数据中发现了丰富的潜在结构,与生理测量和疾病表型保持一致.
结论:
- 珀西瓦尔代表了计算机断层成像可通用的AI的重大进步.
- 该模型在多种CT数据中进行概括的能力提高了其提高临床工作流效率的潜力.
- 珀西瓦尔的分析揭示了对生物,表型和预后因素的临床相关见解.
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