通过深度卷积神经网络对卵巢瘤进行超声波诊断
Min Xi1, Runan Zheng2, Mingyue Wang1
1The First Affiliated Hospital of Soochow University, Suzhou City, China, China.
Ginekologia polska
|October 16, 2023
概括
一个新的深层卷积神经网络 (DCNN) 模型,DenseNet,在与专家放射科医生相比,通过超声波图像识别恶性卵巢瘤方面表现出卓越的准确性. 这种由人工智能驱动的方法显示了改善卵巢癌诊断的前景.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 在瘤学瘤学.
背景情况:
- 卵巢瘤需要准确和及时的诊断才能得到有效的治疗.
- 使用超声波来区分良性和恶性卵巢瘤对人类专家来说可能是一个挑战.
研究的目的:
- 开发和验证使用深 convolutional 神经网络 (DCNNs) 的卵巢瘤的诊断模型.
- 将DCNN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.
主要方法:
- 收集并策划了一套数据集,包括来自405名女性的1003张卵巢瘤超声波图像.
- 培训并评估了五个DCNN模型 (移动网络,Xception,Inception,ResNet,DenseNet).
- 将DCNN模型的性能与三位专家放射科医生对200张图像的测试组进行的诊断进行了比较.
主要成果:
- 在验证组中,DenseNet模型实现了0.997的曲线下的最高面积 (AUC).
- 在测试组中,与放射科医生相比,DenseNet显示了显著更高的精度 (0.975比0.825),灵敏度 (0.975比0.700) 和特异性 (0.975比0.908).
- 与人类专家相比,DenseNet的所有性能指标都具有统计学意义 (p < 0.001).
结论:
- 在超声波检查中,DenseNet DCNN 模型在区分恶性和良性卵巢瘤方面的表现优于其他 DCNN 和专家放射科医生.
- 需要进行进一步的临床试验,以探索这种AI模型在现实世界临床环境中的潜力.
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