人工智能模型协助医生量化急性胰腺炎中的胰腺亡
Cheng-Xiang Lu1, Jiali Zhou2,3, Yong-Chang Feng4
1Department of Intensive Care Unit, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Quantitative imaging in medicine and surgery
|January 22, 2025
概括
一种新的深度学习模型显著提高了在急性胰腺炎 (AP) 患者中诊断胰腺亡体积比率的准确性. 这种人工智能工具有助于临床医生评估严重程度,提高治疗决策和患者的治疗结果.
科学领域:
- 医疗成像中的人工智能
- 深度学习用于医学图像细分的深度学习.
- 放射学和定量成像技术
背景情况:
- 急性胰腺炎 (AP) 是一种严重的疾病,具有潜在的并发症,如胰腺缩.
- 精确评估胰腺亡体积对于指导治疗和改善患者的治疗结果至关重要.
- 目前手动CT扫描对瘤体积的解释是主观和可变的.
研究的目的:
- 开发一个深度学习网络模型,用于自动诊断胰腺亡体积比.
- 帮助临床医生根据修改的计算机断层扫描严重性指数 (MCTSI) 来评估胰腺亡的严重程度.
- 提高AP中亡体积量化的准确性和一致性.
主要方法:
- 开发了一种完善的全卷积神经网络,用于体积医学图像分割 (V-Net) 模型.
- 从189名患者 (144名回顾,45名NIH) 中利用了增强对比度的CT扫描.
- 将AI模型的细分精度 (精度,宏回忆,宏特异性) 与放射科医生和临床医生的手动细分解释进行了比较.
主要成果:
- 在V-Net模型中,整个胰腺的子系数为0.811,死亡组织的子系数为0.761.
- 与高级和初级临床医生 (P<0.05) 相比,AI模型显示出显著更高的诊断性能 (精度0.854,宏观回忆0.850,宏观特异性0.923).
- 最佳的训练涉及使用骨和整个胰腺作为输入对象,并结合子损失和光滑系数.
结论:
- 拟议的深度学习模型提高了临床医生的诊断胰腺亡体积比率的有效性.
- 人工智能辅助诊断提供了一种更客观,更准确的方法来量化AP中的胰腺缩.
- 这项技术有可能改善临床决策和急性胰腺炎患者管理.
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