高精度智能提高肺癌检测
![(A–C) Chest radiographs obtained as part of a health checkup in a 71-year-old male patient show reader susceptibility to high diagnostic accuracy artificial intelligence (AI). In the first session without AI, a thoracic radiologist with 16 years of experience read the chest radiograph as a normal radiograph (A). High diagnostic accuracy AI observed potential lung cancer in the radiograph with an 89% CI as indicated by the nodule localization map (B) (as the color changes from blue to red, the probability of the presence of a nodule increases). When presented with the AI suggestion at the second reading session, the radiologist changed the decision and annotated lung cancer in the area that overlapped with the right hemidiaphragm (box annotation) (C). (D, E) Contrast-enhanced chest CT scans show a 6.8-cm lung mass (arrow) with an air bronchogram in the right lower lobe in the axial (D) and coronal (E) planes. This mass was pathologically proven to be an invasive mucinous adenocarcinoma. Therefore, the reader’s decision was incorrect in the first session but correct in the second session after following the AI suggestion. Credit: Radiological Society of North America 高精度智能提高肺癌检测](https://scx1.b-cdn.net/csz/news/800a/2023/high-accuracy-ai-impro.jpg)
援助从一个人工智能(AI)算法具有较高的诊断准确性提高放射科医师性能检测肺癌胸部x射线和增加人类接受人工智能建议,根据一项研究发表在杂志上放射学。
而基于ai诊断迅速拥有先进的形象医学领域,影响因素放射科医生的诊断决定AI-assisted图像阅读仍未开发。首尔国立大学的研究人员观察了这些因素可能会影响检测恶性肺结节在AI-assisted胸部x光检查。
在这个回顾性研究,30岁的读者,包括20个胸放射科医生5到18年的经验和10放射居民只有两到三年的经验,评估120没有AI胸部x光检查。120胸片的评估,60人肺癌患者(32男性)和60人控制雄性(36)。患者的平均年龄67岁。在第二个会话中,每组重新解释x射线,协助通过高或低准确度AI。读者被忽视的事实,两个不同的AIs。
使用高精度AI改进读者的检测性能在更大程度上比低准确度AI。使用高精度AI也导致更频繁的读者determinations-a概念称为磁化率的变化。
![(A, B) Chest radiographs obtained as part of a health checkup in a 65-year-old male patient show reader susceptibility to low diagnostic accuracy performance artificial intelligence (AI). In the first session without AI, a thoracic radiologist with 17 years of experience annotated the mass opacity in the right upper lung zone as lung cancer (box annotation) (A). Low diagnostic accuracy AI suggested the radiograph as normal (B). When presented with the AI suggestion at the second reading session, the radiologist changed the decision and determined this radiograph was normal. (C, D) Contrast-enhanced chest CT scans show a 3-cm lung mass (arrow) in the right upper lobe in the axial (C) and coronal (D) planes. The mass was pathologically proven to be invasive adenocarcinoma. Therefore, the reader’s decision was correct in the first session but incorrect in the second session after following the AI suggestion. Credit: Radiological Society of North America 高精度智能提高肺癌检测](https://scx1.b-cdn.net/csz/news/800a/2023/high-accuracy-ai-impro-1.jpg)
”可能相对较大的样本量在这项研究支持读者的信心艾未未的建议,”研究作者常说分钟公园,医学博士博士,从放射学和放射医学研究所首尔国立首尔大学医学院的。人类相信人工智能“我们认为这个问题是我们在这项研究中观察到的易感性:人类使用高诊断性能时更容易受到人工智能AI。”
相比第一个会话,阅读读者协助诊断精度高的人工智能在二读会话显示更高per-lesion敏感性(0.63和0.53),和特异性(0.94和0.88)。另外,读者协助诊断精度低的人工智能在二读会话没有显示改善两个阅读会议之间的这些测量。
“我们的研究表明,人工智能可以帮助放射科医生,但是只有当AI的诊断性能达到或超过了人类的读者,“公园博士说。
结果强调使用AI高诊断性能的重要性。然而,公园博士指出,“高诊断性能的人工智能”的定义取决于它的任务和临床上下文将被使用。例如,一个人工智能模型,可以检测所有异常在胸部x光检查似乎是理想的。但在实践中,这样一个模型将有限的价值在减少工作负载在肺结核筛查质量环境。
“因此,我们的研究表明,临床合理使用人工智能的发展既需要高性能的人工智能模型给定的任务和考虑有关,AI会应用于临床,“公园博士说。
在未来,研究者想扩大他们的工作human-AI协作其他异常在胸部x光检查和CT图像。
更多信息:郑大世Hyuk李et al, Human-AI交互对检测的影响恶性肺结节在胸片上,放射学(2023)。DOI: 10.1148 / radiol.222976