首页|人工智能超声辅助系统在不同超声参数下对甲状腺结节检测及诊断效能的研究

人工智能超声辅助系统在不同超声参数下对甲状腺结节检测及诊断效能的研究

Detection and Diagnostic Efficacy of Artificial Intelligence Ultrasound Assisted System for Thyroid Nodules Under Different Ultrasound Parameters

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目的 研究人工智能超声辅助系统(AI系统)在不同超声参数下对甲状腺结节的检测定位准确度和良恶性诊断效能的差异.资料与方法 前瞻性选取2023年3月30日—5月1日于解放军总医院第一医学中心外科准备接受甲状腺手术治疗的147例患者共289枚结节,调节不同的超声参数,在各参数下使用AI系统对甲状腺结节进行检测及良恶性诊断,以病理结果为"金标准",比较不同超声参数下甲状腺结节检测准确度及诊断效能的差异.结果 在标准超声参数下,AI系统检测甲状腺结节的准确度为94.1%,良恶性诊断敏感度为90.9%,特异度为79.6%,准确度为86.6%.在检测准确度方面,与标准超声参数下相比,AI系统在低增益(χ2=4.453,P=0.035)和高增益(χ2=6.215,P=0.013)参数下准确度降低,差异有统计学意义.在诊断效能方面,低增益的特异度(χ2=4.620,P=0.032)、准确度(χ2=7.521,P=0.006)、曲线下面积(Z=3.102,P=0.001)、高增益的敏感度(χ2=6.170,P=0.013)、准确度(χ2=4.127,P=0.042)、曲线下面积(Z=2.152,P=0.031)、高深度的准确度(χ2=5.011,P=0.025)、曲线下面积(Z=2.420,P=0.015)相比标准超声参数下均降低,差异有统计学意义.结论 使用AI系统辅助检查甲状腺结节时需要注意超声仪器参数调节,不恰当的参数调节可能会降低AI系统对甲状腺结节的检测能力和良恶性诊断效能.
Purpose To explore the differences of the accuracy of detection and recognition of thyroid nodules and the diagnostic efficacy of benign and malignant thyroid nodules via artificial intelligence(AI)ultrasound assisted systems based on different ultrasound parameters.Materials and Methods A total of 147 patients with 289 nodules who underwent thyroid surgery in the First Medical Center of Chinese PLA General Hospital from March 30,2023 to May 1,2023 were prospectively selected.Different ultrasound parameters were adjusted and the AI system was used to detect and diagnose benign and malignant thyroid nodules via each parameter.Taken pathological results as the gold standard,the accuracy of thyroid nodule detection and the accuracy of benign and malignant diagnosis under different ultrasound parameters were compared,respectively.Results Under the standard ultrasound parameters,the accuracy of AI system in detecting thyroid nodules was 94.1%,the sensitivity for benign and malignant diagnosis was 90.9%,the specificity was 79.6%,and the accuracy was 86.6%,respectively.In terms of detection accuracy,accuracy under low gain(χ2=4.453,P=0.035)and high gain(χ2=6.215,P=0.013)parameters of AI system were significantly lower than those of standard ultrasound parameters.In terms of diagnostic efficacy,specificity(χ2=4.620,P=0.032),accuracy(χ2=7.521,P=0.006),area under the curve(Z=3.102,P=0.001),high gain sensitivity(χ2=6.170,P=0.013),accuracy(χ2=4.127,P=0.042),area under the curve(Z=2.152,P=0.031)and high depth accuracy(χ2=5.011,P=0.025),area under the curve(Z=2.420,P=0.015)of low gain were all significantly reduced compared to standard ultrasound parameters,with statistical differences.Conclusion When using the AI system to assist in the examination of thyroid nodules,attention should be paid to the adjustment of ultrasound instrument parameters.Improper parameter adjustment may reduce the AI system's ability to detect thyroid nodules and the accuracy of benign and malignant diagnosis.

Thyroid noduleUltrasonographyArtificial intelligencePathology,surgicalDiagnosis,differential

孙斌、李盈盈、阎琳、肖静、李欣洋、张明博、罗渝昆

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川北医学院,四川 南充 637000

解放军总医院第一医学中心超声诊断科,北京 100853

甲状腺结节 超声检查 人工智能 病理学,外科 诊断,鉴别

北京市科技计划课题

Z221100003522001

2024

中国医学影像学杂志
中国医学影像技术研究会

中国医学影像学杂志

CSTPCD北大核心
影响因子:1.37
ISSN:1005-5185
年,卷(期):2024.32(1)
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