病理学和内镜图像的基础模型:用于胃炎的应用.
Hamideh Kerdegari1, Kyle Higgins1,2, Dennis Veselkov1
1Division of Cancer, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK.
Diagnostics (Basel, Switzerland)
|September 14, 2024
概括
基础模型 (FMs) 增强人工智能 (AI),用于早期检测上部胃肠道癌症. 这些先进的AI模型改善了内镜和病理图像分析,有助于改善患者的治疗结果.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 人工智能的人工智能
- 胃肠病学 胃肠病学
背景情况:
- 上部胃肠道 (GI) 癌症,特别是胃癌 (GC),是全球癌症死亡的主要原因.
- 慢性炎症驱动粘膜的变化,从缩和肠道代塑性 (IM) 进展到发育不良和癌症.
- 通过内镜监测进行早期检测对于改善患者存活率至关重要.
研究的目的:
- 审查基础模型 (FMs) 在内镜和上肠道癌症病理成像方面的最新进展,应用和挑战.
- 阐明医疗诊断中FMs的原则,架构和培训方法.
- 为将FM纳入临床实践提供路线图,以预防和管理GC.
主要方法:
- 探索基础模型 (FMs) 的核心原则和架构.
- 分析培训方法和大数据在 FM 发展中的作用.
- 审查目前在内镜和病理学图像分析中的应用.
主要成果:
- 基金会模型在提高上部肠道癌症内镜和病理图像分析的准确性方面表现有希望.
- FMs利用大规模,多样化的数据来实现强大的预测能力.
- 该审查强调了FMs在改善早期检测和管理策略方面的潜力.
结论:
- 基金会模型代表了人工智能驱动的上部肠道癌症医学诊断的重大进步.
- 将FM集成到临床工作流程中可以提高癌症检测的准确性和效率.
- 未来的研究应该专注于多式联运数据集成和开发公平的实时诊断工具.
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