临床验证的神经测量得分的全自动化版本显示出比人类得分更好的预测实用性
Andrew M Wood1, Nour Abdallah1, Nicholas Heller2
1Glickman Urological and Kidney Institute, Cleveland, OH, USA.
BJU international
|February 12, 2024
概括
人工智能 (AI) 现在可以从CT扫描中自动生成肺部计分,与人类的准确度相匹配. 这些人工智能驱动的得分可以预测患者的结果,帮助临床决策.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 计计分数系统对于瘤评估至关重要.
- 手动评分可能是耗时的,容易引起观察者之间的变化.
研究的目的:
- 使用人工智能在手术前CT扫描上自动生成三种经过验证的肺部计分系统 (C-Index,PADUA,CSA).
- 评估人工智能产生的病理和外科手术后期结果得分的预测能力.
主要方法:
- 深度神经网络和几何算法用于自动瘤和脏细分和评分.
- 人工智能得分与人类得分使用线性回归进行了比较.
- 通过对300名患者的逻辑回归和ROC分析来评估预测能力.
主要成果:
- 人工智能生成的肺部测量得分与人类得分显著一致 (R = 0.5740.828).
- 人工智能分数在预测结果方面相当或优越,例如高档组织学,高阶段瘤和手术方法 (部分与激进切除术).
结论:
- 完全自动化的人工智能生成的神经测量得分 (C-Index,PADUA,CSA) 与人类得分相当.
- 人工智能分数可以预测各种有意义的结果,可能有助于在护理点的临床决策.
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