关节疾病 多类分类使用深度学习
Bhornsawan Thanathornwong1, Treesukon Treebupachatsakul2, Thitirat Teechot2
1Faculty of Dentistry, Srinakharinwirot University, Bangkok, Thailand.
Studies in health technology and informatics
|January 25, 2024
概括
深度学习准确地分类了关节 (TMJ) 疾病和全景放射图中的正常变化. DenseNet-121的准确率达到了99%,有助于正确的关节障碍诊断和治疗.
科学领域:
- 牙科 牙科是指牙科的专业.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 关节 (TMJ) 疾病经常被误诊,原因是与正常的解剖变异混.
- 对TMJ特征的误解可能导致无效的治疗策略和患者的不满.
研究的目的:
- 评估深度学习算法,特别是DenseNet-121和InceptionV3在分类正常TMJ变异和障碍方面的有效性.
- 用全景放射图来评估这些人工智能模型的性能.
主要方法:
- 这项研究使用了1,710张全景放射图的数据集.
- 两个深度学习模型DenseNet-121和InceptionV3被用于多类分类任务.
- 计算了性能指标,包括整体准确性和曲线下的面积 (AUC).
主要成果:
- 丹斯网-121的整体准确率为0.99 (99%).
- 在InceptionV3的总体准确度达到0.95 (95%).
- 这两种模型都表现出高性能,AUC值在0.99至1.00之间.
结论:
- 深度学习算法,特别是DenseNet-121显示出在精确分类TMJ障碍和全景X射线图中的正常变异方面的卓越能力.
- 这些发现表明,人工智能在改善关节疾病的诊断准确性方面发挥着有希望的作用,可能减少治疗失败.
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