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在使用深度学习的新生儿MRI脑部扫描中无监督检测异常.

Jad Dino Raad¹, Ratna Babu Chinnam¹, Suzan Arslanturk²

¹Industrial and Systems Engineering Department, Wayne State University, Detroit, MI, 48201, USA.

Scientific reports

|July 17, 2023

概括

此摘要是机器生成的。

这项研究引入了一个新的AI框架来分析新生儿大脑MRI扫描,改善异常的检测和帮助预后. 该模型成功地区分了正常和异常扫描,识别了以前错过的条件.

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科学领域:

人工智能的人工智能
机器学习机器学习
医学成像分析医学成像分析

背景情况:

新生儿大脑MRI分析缺乏强大的无监督方法.
新生儿脑病变 (NE) 和其他早期出现的疾病需要更好的诊断生物标志物.
早期识别新生儿大脑异常对于终身结果至关重要.

研究的目的:

开发和评估用于新生儿大脑MRI分析的AI框架.
改善新生儿大脑疾病的识别和预后.
为神经放射学家在解释新生儿脑部扫描时提供辅助工具.

主要方法:

使用深度卷积自编码器 (AE) 无监督学习架构.
开发了一个框架来学习新生儿大脑的正常结构.
测试了开发人类结合体项目 (dHCP) 数据集 (97名患者) 的框架.

主要成果:

该框架有效地识别了新生儿大脑结构中的微妙形态特征.
正常和异常的新生儿脑部扫描以高达83%的准确度进行了区分.
人工智能识别了以前错过的异常,后来由神经放射学家证实.

结论:

拟议的AI框架增强了新生儿大脑MRI扫描的分析和评估.
这种方法有望改善新生儿大脑疾病的预后.
该框架可以有效地定位新生儿MRI数据中的新型大脑异常.