利用深度学习来识别甲状腺结核中的化和合物
Chen Chen1,2,3,4, Yuanzhen Liu2,3,4, Jincao Yao2,5,6
1Graduate School, Wannan Medical College, Wuhu, 241002, China.
Heliyon
|August 28, 2023
概括
深度学习 (DL) 模型在超声波图像上的合物与甲状腺结节化的区别方面明显优于放射学家. 这种人工智能进步提高了对回声发射焦点的诊断准确度.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 甲状腺结节在超声波上呈现出回声焦点,这可能代表化或合物.
- 由于它们存在不同的恶性瘤风险,区分化和合物至关重要.
研究的目的:
- 评估深度学习 (DL) 模型在区分甲状腺结节中的化与合物中的性能.
- 将DL模型的诊断准确性与人类放射科医生进行比较.
主要方法:
- 一项回顾性研究利用了来自1127个病理确认的甲状腺结节的30388张超声波图像.
- 训练和测试了YoloV5转移学习模型,以区分回声焦点.
- 使用灵敏度,特异性和准确度来评估性能,采用接收器-操作员特征曲线 (AUC) 下的面积作为主要指数.
主要成果:
- 与放射科医生 (51.14%, 82.58%, 61.29%) 相比,DL模型在测试组1中获得了更高的平均灵敏度 (78.41%),特异性 (91.36%) 和准确性 (77.81%).
- 对于测试组2中较小的回声焦点,DL模型也表现出优异的性能 (70.17%的灵敏度,77.14%的特异性,73.33%的精度) 与放射科医生 (57.69%,63.29%,59.38%) 相比.
结论:
- 深度学习模型显示在区分甲状腺结节回声源焦点作为化或合物方面具有卓越的能力.
- 该研究强调了人工智能在提高甲状腺结节评估诊断精度方面的潜力.
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