一种新的深度学习方法用于急性尾炎的自动诊断
1Radiology Department, Faculty of Medicine, Kahramanmaras Sutcu Imam University, Kahramanmaras 46050, Turkey.
Journal of clinical medicine
|August 29, 2024
概括
深度学习使用计算机断层扫描 (CT) 扫描准确诊断急性尾炎 (AA),即使在具有挑战性的情况下. 这种人工智能方法提高了细微尾炎发现的诊断成功率,减少了误诊风险.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医学成像分析 医学成像分析
背景情况:
- 急性尾炎 (AA) 是急性腹痛的常见原因,需要手术.
- 错误或延迟的AA诊断发生在约20%的病例中,增加了穿孔风险.
- 计算机断层扫描 (CT) 有助于AA诊断,但假阴性结果可能会推迟治疗.
研究的目的:
- 开发一种新的深度学习 (DL) 方法,用于使用CT进行自动 AA 诊断.
- 通过根据放射性难度对病例进行分类来提高诊断准确度.
- 在具有挑战性的AA演示中,尽量减少虚假阴性诊断.
主要方法:
- 在CT图像和266名病理确诊AA患者的临床数据上训练了一种深度学习模型.
- 根据CT发现和放射学报告的解释,患者被分为两层.
- DL模型分析了CT图像和诊断的临床/实验室参数.
主要成果:
- DL模型在诊断AA时取得了96%的成功率,具有先进的放射学发现.
- 在模仿正常的放射学上可疑发现的病例中,成功率为83.3%.
- 即使尾直径和胀没有显著增加,也证明了高精度.
结论:
- 深度学习提供了一个有前途的工具,通过CT对急性尾炎进行准确的自动诊断.
- 拟议的DL战略有效地处理具有微妙放射性迹象的具有挑战性的病例.
- 这种方法可以帮助减少诊断错误并优化AA的治疗.
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