人工智能在诊断周角线性辐射发光中的作用
M Madhumitha1, Devika S Pillai1, Pradeep Kumar Yadalam2
1Department of Oral Medicine, Radiology and Special Care Dentistry, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, 60077, India.
人工智能 (AI) 改进了使用牙科放射图的冠心周辐射发光诊断. 后勤回归在识别这些异常方面略高于Naive Bayes,从而提高了患者的护理.
科学领域:
- 牙科 牙科是指牙科的专业.
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 增强了牙科放射图解读能力,用于诊断心周冠状动脉辐射发光.
- 人工智能算法识别出未爆发的牙附近的早期异常,有助于区分良性和恶性疾病.
- 诊断准确度的提高导致了更好的治疗计划和患者的治疗结果.
研究的目的:
- 评估人工智能算法的性能,以检测牙科X射线图中的冠状管周放射性.
- 为了比较物流回归和天真贝叶斯模型的诊断准确度,用于这种情况.
主要方法:
- 分析了1070张牙科放射图的数据集,其中包括315张在下第三牙中呈现出心冠状动脉辐射的图像.
- 后勤回归和天真贝叶斯算法用于分类.
- 使用准确度,灵敏度,特异性,精度,回忆,F1得分和AUC-ROC曲线等指标来评估性能.
主要成果:
- 后勤回归模型在预测心周冠状动脉放射性时,与天真贝叶斯模型 (52.2%) 相比,显示出略高的准确性 (58.3%).
- 后勤回归在CA,F1和回忆中表现出优异的表现.
- 纯粹的贝叶斯在AUC和精度方面表现更好.
结论:
- 逻辑回归在检测数字骨干造影仪中的心经冠状动脉辐射发光度方面表现出略高的准确性.
- 这两种人工智能算法在分类心脏周周放射性质方面都表现出相当大的表现,支持它们的临床实用性.
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