通过深度学习检测骨骨折:全面审查
Zhihao Su1, Afzan Adam1, Mohammad Faidzul Nasrudin1
1Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.
Diagnostics (Basel, Switzerland)
|October 28, 2023
概括
本综述阐明了从X射线诊断骨折骨折的深度学习任务. 它分析了40篇论文,定义了识别,分类,检测和本地化,以改善AI开发和临床信任.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 深度学习显示了从X射线诊断骨折的潜力.
- 目前的研究面临的挑战是由于任务定义不清楚和缺乏可解释性.
- 现有的审查往往缺乏技术深度或范围.
研究的目的:
- 建立骨折诊断中的深度学习任务的精确定义 (识别,分类,检测,定位).
- 分析和总结最近的研究方法,数据集和结果.
- 确定人工智能驱动骨折诊断未来研究的关键领域.
主要方法:
- 从WOS,Scopus和EI的337篇论文进行了全面的文献审查.
- 对40项精选的近期研究进行了深入分析和评估.
- 为骨折诊断中的深度学习开发一个通用处理框架.
主要成果:
- 为骨折识别,分类,检测和定位任务提供了明确的定义.
- 40项研究的摘要详细介绍了骨,目标,数据集,方法和结果.
- 确定关键的未来研究方向,包括可解释性和多式联网数据集成.
结论:
- 本综述探讨了在骨折诊断的深度学习中对标准化任务定义的需求.
- 这些发现为推进放射学人工智能的基础提供了基础,改善了解释性和临床决策支持.
- 未来的工作应该专注于可解释的人工智能,多式联络数据和治疗建议.
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