使用基于深度学习的模型对超声波图像进行分析,作为诊断胆囊多的辅助诊断工具
Jin Ho Choi1, Jaesung Lee2, Sang Hyub Lee3
1Division of Gastroenterology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
概括
一个深度学习模型准确地区分了瘤性胆囊息肉 (GBP). 计算机辅助诊断提高了医生的准确性,特别是对于缺乏经验的医生,改善了诊断结果.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 胃肠病学 胃肠病学
背景情况:
- 准确诊断胆囊聚体 (GBPs) 对于防止误诊和过度治疗至关重要.
- 从非NGBP中区分瘤性GBP (NGBP) 需要精确的诊断方法.
研究的目的:
- 评估GBP的深度学习 (DL) 模型的诊断效果.
- 为了评估医生对GBPs的计算机辅助诊断 (CAD) 的准确性.
主要方法:
- 对来自263名患者的3,754张图像 (2006年1月至2021年9月) 的回顾性分析.
- 对DL模型在区分NGBP与非NGBP中的性能进行比较.
- 评估医生诊断准确度,有或没有CAD辅助.
主要成果:
- DL模型在区分NGBPs方面取得了很高的效率 (精度:0.858,灵敏度:0.856,特异性:0.861).
- 与无辅助诊断 (0.634;p<0.001) 相比,医生的准确性在CAD (0.785) 中显著改善.
- CAD 特别有利于缺乏经验的医生 (0.772 vs 0.614; p<0.001),而经验丰富的医生没有显著改善.
结论:
- 深度学习模型在区分NGBP与非NGBP方面表现出卓越的准确性.
- 人工智能驱动的CAD工具可以作为有价值的辅助工具,大大提高了经验较少的医生的诊断准确性.
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