基于人工智能的自动PRAGMA和粘液堵塞算法的验证
Pranali Raut1, Yuxin Chen1, Ahmad Taleb2
1Department of Pediatric Pulmonology and Allergology, Erasmus MC -Sophia Children's Hospital, Rotterdam, The Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC -Sophia Children's Hospital, Rotterdam, The Netherlands.
概括
一个新的AI算法 (PRAGMA-AI) 显示了与囊性纤维化 (CF) 患者的视觉PRAGMA-CF评分的强烈相关性. 这种人工智能工具提供了一个有前途的,更快的替代方法来评估CF的呼吸道损伤.
科学领域:
- 医学成像
- 医学的人工智能
- 肺病学
背景情况:
- PRAGMA- CF是一种经过验证的视觉评分方法,用于用胸部CT量化囊性纤维化 (CF) 患者的结构性呼吸道损伤.
- 精确量化气道损伤对于CF管理和研究至关重要.
研究的目的:
- 对基于人工智能的新型自动化算法 (PRAGMA-AI) 和粘液插入算法与已建立的视觉PRAGMA-CF评分方法进行验证.
- 评估人工智能得分的可靠性和与CF呼吸道损伤的视觉评估的相关性.
主要方法:
- 对178名CF患者的363次胸部CT扫描进行了回顾性分析.
- 使用相关性和一致性统计数据对视觉PRAGMA-CF,自动PRAGMA-AI和粘液插入算法的结果进行比较.
- 进行了敏感性分析,以评估疾病严重程度,队列,切片数和卷积核的影响.
主要成果:
- PRAGMA-AI算法成功分析了97%的CT扫描.
- 在%支气管切除 (%BE),%疾病 (%DIS) 和%粘液堵塞 (%MP) 的自动和视觉评分之间观察到强烈的相关性.
- 气道壁加厚率 (%AWT) 的相关性较弱;%BE和%DIS的相关性平均,但%AWT的相关性较弱.
结论:
- 通过AI获得的得分与视觉PRAGMA-CF得分大小不相同,但与CF肺部疾病的关键指标有很强的相关性.
- PRAGMA-AI和粘液堵塞算法代表了手动评分的潜在价值和时间效率的替代方案,特别是在大规模研究中.
- 进一步验证可能会支持将基于人工智能的工具纳入常规CF临床实践和研究.
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