自动识别和测量与上鼻床的升高相关的解剖结构,通过在形束计算机断层扫描上进行深度学习
Bin Xuan1, Qiang Ding2, Weili Wang1
1Department of Stomatology, Aerospace Center Hospital, No.15 Yu-quan Road, Haidian District, Beijing, 100049, China.
BMC oral health
|January 6, 2026
概括
一个深度学习模型准确地识别了上鼻升高的关键解剖结构,有助于精确的牙科植入物手术前规划. 这种人工智能工具可以提高尾后牙植入手术的诊断性能.
科学领域:
- 牙科 牙科是指牙科的专业.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 对解剖结构的准确识别对于成功的上鼻腔升高和牙植入手术至关重要.
- 后牙植入术前规划需要精确测量与 sinus,后上膜动脉 (PSAA) 和膜脊相关的参数.
研究的目的:
- 开发和评估一种深度学习模型,用于在CBCT图像中识别上鼻,PSAA和气囊脊.
- 通过测量相关的解剖学参数,实现尾后牙植入的智能手术前设计.
主要方法:
- 使用了2400个CBCT切片,这些切片来自上后牙损失的患者.
- 采用了增强的YOLOv11架构,用于对上鼻,PSAA和气泡脊的细分.
- 评估的细分精度使用交叉在欧盟 (IoU),平均精度 (AP),平均回忆 (AR) 和欧几里德距离.
主要成果:
- 在上鼻腔 (0.945) 和PSAA (0.991) 分段方面取得了高的IOU.
- 对于PSAA细分,已经证明了高平均精度 (0.902) 和回忆 (0.937) .
- 展示了精确的气泡峰定位,平均欧几里德距离误差低于0.50毫米,并精确测量了手术参数 (A1-A5).
结论:
- 增强的YOLOv11框架可靠地识别了上鼻升高的关键解剖结构.
- 该模型有助于获取精确的临床参数,用于智能辅助手术前评估.
- 这种由人工智能驱动的方法显示了改善尾后牙植入术设计的巨大潜力.
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