人工智能与传统放射性解读在检测周围牙周炎中的比较:诊断准确性研究
Bharath Nagareddy1, Rohit Vadlamani2, Nikhila Reddy Venkannagari3
1Department of Conservative Dentistry and Endodontics, Adhiparasakthi Dental College and Hospital, Melmaruvathur, Tamil Nadu, India.
Journal of pharmacy & bioallied sciences
|February 10, 2025
概括
一个人工智能模型的性能与经验丰富的放射科医生在从放射图像中检测周围牙周炎时的性能相当. 这表明人工智能可以帮助牙科诊断,但需要进一步的研究.
科学领域:
- 牙科放射学 牙科放射学
- 人工智能在医学中的应用
- 诊断成像 诊断成像 诊断成像
背景情况:
- 周围牙周炎是一种常见的内牙相关的炎症状况.
- 精确的放射检测对于及时诊断和治疗计划至关重要.
- 对人类专家进行AI性能评估对于临床整合至关重要.
研究的目的:
- 将AI模型的诊断性能与经验丰富的放射科医生的诊断性能进行比较.
- 评估检测周围牙周炎的准确性,敏感性和特异性.
- 评估人工智能和人类解释的信心水平.
主要方法:
- 分析了30张放射图像 (CBCT,全景图像,周围图像).
- 一个人工智能模型和两个经验丰富的放射科医生解释了这些图像.
- 诊断准确性,敏感性,特异性和信心被统计分析.
主要成果:
- 人工智能模型实现了89.6%的准确度,86.5%的灵敏度和88.1%的特异性.
- 放射科医生1获得了98.5%的准确性,而放射科医生2获得了81.7%的准确性.
- 人工智能模型显示高信心率 (86.5%) 和与放射科医生的信心有中等的相关性.
结论:
- 人工智能在检测周围牙周炎方面表现与经验丰富的放射科医生相美.
- 人工智能工具可以提高牙科诊断的效率和一致性.
- 建议在不同的临床环境中进一步验证AI模型.
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