人工智能和机器学习应用用于骨和软组织瘤的成像
Paniz Sabeghi1, Ketki K Kinkar2, Gloria Del Rosario Castaneda3
1Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
Frontiers in radiology
|September 20, 2024
概括
人工智能 (AI) 在肌肉骨放射学中为提高诊断准确性和效率提供了显著的潜力. 应对数据标准化和道德问题等挑战对于AI工具的成功临床集成至关重要.
科学领域:
- 肌肉骨放射学 肌肉骨放射学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 在肌肉骨放射学中呈现出新兴的机会.
- 人工智能工具可以帮助放射科医生完成各种任务,包括图像细分和病变检测.
研究的目的:
- 探索AI和ML在提高诊断准确性,工作流效率和肌肉骨放射学预测建模方面的潜力.
- 检查放射学和深度学习在骨和软组织瘤成像中用于恶性瘤分层,分级,预后和治疗计划的应用.
主要方法:
- 审查当前的进展和AI和ML在肌肉骨放射学中的应用.
- 在瘤成像中讨论放射学和深度学习技术.
- 确定临床翻译的挑战和机会.
主要成果:
- 人工智能工具在协助图像分析和改进诊断能力方面表现有前途.
- 放射学和深度学习为改善肌肉骨瘤的表征和管理提供了潜力.
- 关键的挑战包括标准化,数据集成,伦理考虑和罕见疾病算法开发.
结论:
- 多学科的合作和强大的,道德的方法对于成功地将AI整合到临床实践中是必不可少的.
- 克服当前的障碍是必要的,以充分利用AI在肌肉骨放射学方面的潜力,以改善患者护理.
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