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卷积神经网络能否识别出外动脉化?

John Nelson¹, Anusha Vaddi¹, Aditya Tadinada¹

¹Section of Oral and Maxillofacial Radiology, Division of Oral and Maxillofacial Diagnostic Sciences, UConn School of Dental Medicine, UConn Health, Farmington, CT, USA.

Oral surgery, oral medicine, oral pathology and oral radiology

|August 26, 2023

概括

一个新的卷积神经网络 (CNN) 在CT扫描中可靠地检测出外动脉化 (ECAC). 这种人工智能工具实现了76%的准确性,有助于从医学成像中识别ECAC.

科学领域:

医学成像分析医学成像分析
放射学中的人工智能
心血管健康心血管健康

背景情况:

外动脉化 (ECACs) 是心血管疾病的指标.
准确检测ECAC对于风险评估至关重要.
圆束计算断层扫描 (CBCT) 越来越多地用于牙科和大面部成像.

研究的目的:

开发和评估用于检测ECAC的卷积神经网络 (CNN).
与人类评估相比,评估CNN模型的准确性和可靠性.
研究AI在分析血管化的CBCT扫描中的实用性.

主要方法:

使用TensorFlow开发了一个CNN模型.
在427次CBCT扫描中接受了培训,培训到验证的比例为80:20.
使用k倍交叉验证,F1得分和马修斯相关系数评估的性能.

主要成果:

美国有线电视新闻网 (CNN) 实现了76%的k倍交叉验证准确率.
回忆率为66%,精度为79%,F1得分为0.72.
马修斯相关系数为0.53表明所有混矩阵类别的表现强.

结论:

开发的CNN模型在识别ECAC方面表现出可靠的性能.
基于人工智能的分析可以帮助在CBCT扫描中检测血管化.
这项技术有望通过常规成像改善心血管风险评估.

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