通过人工智能辅助对整个幻灯片图像进行量化和评估,用于儿童病诊断
Chunyue Feng1,2, Kokhaur Ong3, David M Young4,5
1Department of Nephrology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China.
Bioinformatics (Oxford, England)
|December 7, 2023
概括
一个人工智能工具,基于人工智能的儿科病诊断 (APKD),通过准确地细分和分类结构,帮助病理学家诊断儿科病. APKD实现了高精度,并且比手动检测快得多.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 病理学 病理学 病理学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 儿科病显著影响儿童发育,往往需要侵袭性活检进行诊断.
- 活检的病理评估是劳动密集型的,容易出现人为错误.
- 准确的细分和分类儿科脏结构对于及时诊断和治疗至关重要.
研究的目的:
- 开发和验证一种人工智能 (AI) 方法,即基于人工智能的儿科诊断 (APKD),用于协助病理学家分析儿科瘤活检.
- 通过组织结构的自动化分析,提高儿童病诊断的准确性和效率.
- 为了能够早期识别球细胞异常,以改善患者的治疗结果.
主要方法:
- 基于人工智能的儿科病诊断 (APKD) 模型的开发,使用2935名儿科病患者的数据集.
- 三名专家病理学家对93,932个组织结构进行手动注释,用于模型培训和验证.
- 使用准确度指标,斯皮尔曼相关系数和类内相关系数 (ICC) 来定量评估APKD的表现.
主要成果:
- 在所有脏结构类别中,APKD的平均准确率为94%,而质细胞的准确率为99%.
- 在APKD和手动检测质细胞之间观察到强烈的相关性 (斯皮尔曼的r = 0.98,ICC = 0.98).
- 与手工方法相比,APKD显示了质细胞细分速度的5.5倍增长,并确定了关键的病理特征.
结论:
- 基于人工智能的儿科诊断 (APKD) 系统在细分和分类儿科结构方面表现出高准确性和效率.
- 通过减少诊断时间和潜在的错误,APKD可以帮助病理学家,促进儿童脏疾病的早期和更准确的诊断.
- 人工智能模型识别特定病理特征的能力有助于早期检测质毛细管壁异常,改善诊断能力.
相关概念视频
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