开发和验证用于小肠囊内镜的多任务人工智能辅助系统.
Jian Chen1,2, Hongwei Wang1,2, Zihao Zhang3
1Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Suzhou, Jiangsu, 215500, People's Republic of China.
International journal of general medicine
|May 19, 2025
概括
这项研究开发了一个使用变压器网络用于小肠囊内镜 (SBCE) 的AI系统. 人工智能系统准确地识别病变,提高了内镜医生的诊断速度和效率.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 胃肠病学 胃肠病学
背景情况:
- 小肠囊内镜 (SBCE) 对于诊断胃肠道疾病至关重要.
- 解释SBCE图像是耗时的,容易出现诊断错误.
- 现有的人工智能工具缺乏为高效的SBCE分析提供全面的功能.
研究的目的:
- 使用变压器神经网络为SBCE开发一个人工智能辅助的多任务系统.
- 整合损伤识别,累积时间统计和进度条标记.
- 提高诊断准确性和效率,同时尽量减少SBCE中错过的诊断.
主要方法:
- 收集并注释了来自三个设备品牌的12个类别的34,799张SBCE图像的数据集.
- 在五个预先训练的变压器模型上利用转移学习和微调.
- 使用最佳的FocalNet模型 (ONNX格式) 和OpenCV/MMCV工具开发了一个名为"FocalCE-Master"的多任务SBCE辅助阅读系统.
主要成果:
- 焦点网络模型的加权平均灵敏度为85.69%,特异性为98.58%,准确度为85.69%.
- 焦点网络的诊断准确度优于初级医生,与高级医生相比.
- "FocalCE-Master"系统以每秒592.40的速度处理图像,比人类内镜师快得多,并简化了病变定位.
结论:
- 基于变压器的多任务人工智能系统展示了小肠病变的快速和准确的分类.
- 该系统有可能显著提高SBCE的诊断效率和图像审查速度.
- 在未来的临床试验中需要进一步验证,以确认AI系统的临床适用性.
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