一种深度学习算法,用于识别时骨计算机断层扫描上的解剖学地标
Zubair Hasan1, Seraphina Key2, Michael Lee3
1University of Sydney, Faculty of Medicine and Health, New South Wales, Australia; Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, New South Wales, Australia.
The journal of international advanced otology
|October 4, 2023
概括
一个深度学习模型准确地识别了骨CT扫描中的结构. 当高级临床医生进行培训时,模型的性能得到了改善,这突显了人工智能专业知识在手术规划中的重要性.
科学领域:
- 医疗成像医学成像
- 在外科手术中使用人工智能
- 神经外科 神经外科
背景情况:
- 电脑断层扫描 (CBCT) 对于诊断和识别骨和骨手术中的外科标志至关重要.
- 在CBCT扫描中准确识别解剖结构对于成功的外科手术结果至关重要.
- 深度学习,特别是卷积神经网络 (CNN),为医疗诊断中的图像分析提供了自动化和增强的潜力.
研究的目的:
- 评估深度学习CNN算法的准确性,以识别状骨CBCT扫描中的关键结构.
- 为了比较CNN在经过不同级别经验 (高级与初级) 的临床医生培训时的表现.
主要方法:
- 分析了129个石头骨CBCT扫描.
- 在68次扫描中,由耳鼻喉科注册医生和经过董事会认证的耳鼻喉科医生手动标记了关键的手术期间的里程碑.
- 一个CNN (微软自定义视觉) 被训练在标记的数据上,并用于剩余的61个扫描的自动结构识别,结果由耳鼻喉科医生验证.
主要成果:
- 在轴向 (0.958) 和冠状 (0.924) CBCT切片 (P < .001) 上,CNN 在自动结构识别中实现了高精度.
- CNN的准确性与提供培训数据的临床医生的资历有积极的相关性.
- 更复杂的结构,如带,前庭和带通道,在高级临床医生培训中显示出更大的精度改进.
结论:
- 在骨骨CBCT扫描中,CNN显示了自动结构识别的高准确性.
- 培训临床医生的专业知识显著影响CNN的表现,高级临床医生产生了卓越的结果.
- 建议使用最有经验的临床医生的数据来训练CNN,以最大限度地提高外科应用的识别准确性.
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