以人工智能为基础的功能性消化不良的诊断支持系统基于大脑活动和食物偏好
Ryo Katsumata1, Takayuki Hosokawa2, Tomoari Kamada1
1Department of Health Care Medicine, Kawasaki Medical School General Medical Center, Okayama, JPN.
Cureus
|January 3, 2024
概括
人工智能现在可以帮助诊断肠-大脑相互作用障碍 (DGBI),如功能性消化不良 (FD) 和刺激性肠综合征 (IBS). 这种人工智能模型使用大脑活动和食物偏好,在区分患者和健康个体方面达到70%以上的准确性.
科学领域:
- 神经科学是一个神经科学.
- 胃肠病学 胃肠病学
- 人工智能的人工智能
背景情况:
- 肠-大脑相互作用障碍 (DGBI),包括功能性消化不良 (FD) 和刺激性肠综合征 (IBS),缺乏可检测的有机异常.
- 患有DGBI的个体的大脑活动模式与健康对照者的大脑活动模式有很大不同.
- 人工智能 (AI) 在识别用于DGBI诊断的生物标志物方面表现有前途.
研究的目的:
- 为DGBI.BI开发基于AI的诊断支持系统.
- 整合食物偏好和前额叶皮质大脑活动数据进行诊断.
- 评估人工智能模型的诊断准确性,以区分DGBI患者与健康对照.
主要方法:
- 根据ROME IV标准诊断出FD和IBS的患者.
- 通过视觉模拟尺度评估的食物偏好.
- 使用功能近红外光谱 (fNIRS) 测量的大脑活动.
- 一个使用fNIRS和食物偏好数据开发的人工神经网络模型.
主要成果:
- 该研究包括41名参与者 (25名DGBI).
- 人工智能模型在将DGBI患者与健康对照区分时获得了72.3%的准确性.
- 人工智能模型在将FD患者与健康对照患者区分方面实现了77.1%的准确性.
结论:
- 整合大脑活动和食物偏好的人工智能模型在识别DGBI时表现出显著的准确性.
- 功能近红外光谱 (fNIRS) 为DGBI诊断提供了客观证据.
- 这种方法支持开发功能性胃肠道疾病的新型诊断工具.
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