一个公开可用的喉炎数据集和细菌或非细菌分类的基线评估
Negar Shojaei1, Habib Rostami2, Mohammad Barzegar1
1Department of Computer Engineering, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr, 7516913817, Iran.
这项研究引入了大量的喉图像数据集,以帮助区分使用AI的细菌和非细菌炎. 这有助于减少对喉炎的不必要的抗生素处方.
科学领域:
- 医学成像医学成像
- 人工智能的人工智能是人工智能.
- 传染病 传染病 传染病
背景情况:
- 区分细菌和非细菌性喉炎是临床上具有挑战性的,因为类似的症状.
- 过度使用抗生素治疗病毒感染有助于抗生素耐药性.
- 需要新的诊断方法来准确和及时诊断喉炎.
研究的目的:
- 开发和发布用于喉炎研究的最大的公开可用的高分辨率喉图像数据集.
- 为了使人工智能驱动的工具的发展,用于细菌和非细菌炎的非侵入性诊断.
- 支持远程医疗保健和传染病医学图像分析方面的进步.
主要方法:
- 收集了742名患有普通感冒症状的患者的高分辨率喉图像.
- 记录了详细的临床数据,包括20种症状,年龄,性别和医生诊断.
- 利用深度神经网络进行图像分析以区分感染类型.
- 建立了三种细菌与非细菌感染差异化的基线模型.
主要成果:
- 创建了最大的公共可用数据集用于喉炎视觉诊断.
- 展示了人工智能和深度学习在分析喉图像用于诊断目的方面的潜力.
- 为未来的诊断工具的研究和开发提供了基线模型.
结论:
- 开发的数据集和人工智能方法为准确,非侵入性喉炎诊断提供了一个有希望的途径.
- 这项工作可以显著减少不必要的抗生素处方,并打击抗菌素耐药性.
- 未来的研究可以基于这个数据集来改进人工智能诊断能力,并推广远程医疗解决方案.
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