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基于MRI的深度学习模型用于术前乳房体积和密度评估,协助乳房重建

Muzi Chen¹, Jiahua Xing², Lingli Guo³

¹Department of Plastic and Reconstructive Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China.

Aesthetic plastic surgery

|May 28, 2024

概括

使用人工智能模型进行精确的自动化乳房细分,可以改善乳房重建的体积测量. 这些人工智能工具提供可靠和可重复的结果,有助于最佳的植入物选择,以获得更好的外科结果.

科学领域:

医疗成像医学成像
人工智能的人工智能
手术规划手术规划

背景情况:

准确的手术前乳房体积评估对于成功选择乳房重建植入物至关重要.
自动化定量方法可以提高手术规划和乳房重建的临床决策.

研究的目的:

开发和评估人工智能 (AI) 模型,用于自动化乳腺细分和体积测量.
将不同AI算法的准确性和可重复性与手动细分进行比较.

主要方法:

开发了三个自动化细分算法:简单的注册,动态编程和深度学习.
利用了249名接受重建的受试者的手术前乳腺MRI数据.
使用平均平方误差 (MSE) 和类内相关系数 (ICC) 对医生绘制的区域进行评估的体积一致性.

主要成果:

这三种人工智能模型都与手动细分有很强的一致性 (ICCs > 0.975).
动态编程模型显示了最高的测试-重新测试可重复性 (ICC=0.993).
自动细分表现出比不同放射科医生的手动细分更高的可重现性.

结论:

开发的AI算法提供了准确可靠的自动化乳腺细分和体积测量.
这些人工智能工具提高了细分的可重现性,有助于精确选择用于乳房重建的假肢.
自动化方法为优化手术规划和乳房重建的结果提供了宝贵的工具.

关键词:

自动编码自动编码.自动细分系统自动细分系统乳房重建乳房重建深度学习是一种深度学习.磁共振成像技术磁共振成像技术

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相关概念视频

Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...