■甲状腺结节通常通过超声成像来识别,这在恶性肿瘤的早期检测中起着至关重要的作用。诊断的准确性,然而,受放射科医生的专业知识影响很大,设备的质量,和图像采集技术。这种可变性强调了对支持诊断的计算工具的关键需求。
■这项回顾性研究评估了人工智能(AI)驱动的甲状腺结节评估系统,整合泰国多个著名医疗中心的临床实践。我们纳入了在2015年1月至2021年3月期间接受甲状腺超声检查并辅以超声引导细针穿刺(FNA)的患者。参与者组成了一个连续的系列,提高研究的有效性。在AI模型的诊断性能与经验丰富的放射科医师和第三年放射科住院医师的诊断性能之间进行了比较分析,使用来自三个著名泰国医疗机构的600张超声图像的数据集,每个都经过细胞学检查。
■AI系统表现出卓越的诊断性能,总体敏感性为80%[95%置信区间(CI):59.3-93.2%],特异性为71.4%(95%CI:53.7-85.4%)。在Siriraj医院,AI的灵敏度为90.0%(95%CI:55.5-99.8%),特异性为100.0%(95%CI:69.2-100%),正预测值(PPV)为100.0%,负预测值(NPV)为90.9%,总体准确率为95.0%,表明AI在不同数据集中进行广泛培训的好处。有经验的放射科医师的敏感度为40.0%(95%CI:21.1-61.3%),而特异性为80.0%(95%CI:63.6-91.6%),分别,显示AI在灵敏度方面显著优于放射科医师(P=0.043),同时保持相当的特异性.观察者间变异性分析表明放射科医生和住院医师之间的一致性中等(K=0.53),与AI系统比较时的一致性(K=0.37/0.33)。值得注意的是,这些诊断索引的95%CI突出显示了AI系统在不同设置中的一致性能。
■这些发现主张将AI整合到临床环境中,以提高放射科医师评估甲状腺结节的诊断准确性。AI系统,设计为支持性工具而不是替代品,通过提供高水平的诊断精度,有望彻底改变甲状腺结节的诊断和管理。
UNASSIGNED: Thyroid nodules are commonly identified through ultrasound imaging, which plays a crucial role in the early detection of malignancy. The diagnostic accuracy, however, is significantly influenced by the expertise of radiologists, the quality of equipment, and image acquisition techniques. This variability underscores the critical need for computational tools that support diagnosis.
UNASSIGNED: This retrospective study evaluates an artificial intelligence (AI)-driven system for thyroid nodule assessment, integrating clinical practices from multiple prominent Thai medical centers. We included patients who underwent thyroid ultrasonography complemented by ultrasound-guided fine needle aspiration (FNA) between January 2015 and March 2021. Participants formed a consecutive series, enhancing the study\'s validity. A comparative analysis was conducted between the AI model\'s diagnostic performance and that of both an experienced radiologist and a third-year radiology resident, using a dataset of 600 ultrasound images from three distinguished Thai medical institutions, each verified with cytological findings.
UNASSIGNED: The AI system demonstrated superior diagnostic performance, with an overall sensitivity of 80% [95% confidence interval (CI): 59.3-93.2%] and specificity of 71.4% (95% CI: 53.7-85.4%). At Siriraj Hospital, the AI achieved a sensitivity of 90.0% (95% CI: 55.5-99.8%), specificity of 100.0% (95% CI: 69.2-100%), positive prediction value (PPV) of 100.0%, negative prediction value (NPV) of 90.9%, and an overall accuracy of 95.0%, indicating the benefits of AI\'s extensive training across diverse datasets. The experienced radiologist\'s sensitivity was 40.0% (95% CI: 21.1-61.3%), while the specificity was 80.0% (95% CIs: 63.6-91.6%), respectively, showing that the AI significantly outperformed the radiologist in terms of sensitivity (P=0.043) while maintaining comparable specificity. The inter-observer variability analysis indicated a moderate agreement (K=0.53) between the radiologist and the resident, contrasting with fair agreement (K=0.37/0.33) when each was compared with the AI system. Notably, 95% CIs for these diagnostic indexes highlight the AI system\'s consistent performance across different settings.
UNASSIGNED: The findings advocate for the integration of AI into clinical settings to enhance the diagnostic accuracy of radiologists in assessing thyroid nodules. The AI system, designed as a supportive tool rather than a replacement, promises to revolutionize thyroid nodule diagnosis and management by providing a high level of diagnostic precision.