人工智能驱动的胸部X射线诊断:在肺部放射学中进行临床验证的变革性转移学习
Md Abu Sufian1,2, Wahiba Hamzi3, Tazkera Sharifi4
1IVR Low-Carbon Research Institute, Chang'an University, Xi'an 710018, China.
Journal of personalized medicine
|August 29, 2024
概括
肺部放射学中的人工智能 (AI) 显著提高了肺胸和等疾病的诊断准确性,超过了放射科医生. 人工智能还可以提高临床工作流程的效率和医疗成像分析的透明度.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 肺部放射学对于诊断胸部疾病至关重要.
- 当前的诊断方法在准确性和效率方面面临挑战.
- 先进的人工智能 (AI) 提供了潜在的解决方案.
研究的目的:
- 评估先进的AI方法,以提高肺部放射学诊断的准确性.
- 与专家放射科医生相比,评估AI模型的性能.
- 探索AI和自然语言处理 (NLP) 在临床工作流程中的整合.
主要方法:
- 在108,948张胸部X射线图像上使用了DenseNet121和ResNet50.
- 应用潜伏迪里克莱特分配 (LDA) 和命名实体识别 (NER) 用于临床文本分析.
- 使用DistilBERT进行情绪分析,使用XGBoost与SHAP进行解释性.
- 纳入了局部可解释的模型不可知解释 (LIME) 和闭塞灵敏度分析.
主要成果:
- 在识别肺胸和水时,DenseNet121获得了94%的AUC.
- 人工智能模型在特定的诊断任务中超过了专家放射科医生.
- NER系统实现了92%的精度和88%的回忆.
- 人工智能技术减少了60%的处理时间和75%的注释错误.
结论:
- 人工智能显示出在推进胸部诊断和加快医疗评估方面的巨大潜力.
- 人工智能集成提高了肺部放射学效率和准确性.
- 复杂的疾病诊断需要进一步的研究,但人工智能在医学成像中显示出变革性的潜力.
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