El-Gedaily Mona, Euler André, Guldimann Mike, Schulz Bastian, Aghapour Zangeneh Foroud, Prause Andreas, Kubik-Huch Rahel A, Niemann Tilo
Department of Radiology, Kantonsspital Baden, affiliated Hospital for Research and Teaching of the Faculty of Medicine of the University of Zurich, Baden, Switzerland.
Department of Radiology, Klinik Hirslanden, Zürich, Switzerland.
Medicine (Baltimore). 2024 Nov 22;103(47):e40485. doi: 10.1097/MD.0000000000040485.
The aim of our study was to evaluate the specific performance of an artificial intelligence (AI) algorithm for lung nodule detection in chest radiography for a larger number of nodules of different sizes and densities using a standardized phantom approach. A total of 450 nodules with varying density (d1 to d3) and size (3, 5, 8, 10 and 12 mm) were inserted in a Lungman phantom at various locations. Radiographic images with varying projections were acquired and processed using the AI algorithm for nodule detection. Computed tomography (CT) was performed for correlation. Ground truth (detectability) was established through a human consensus reading. Overall sensitivity and specificity of 0.978 and 0.812, respectively, were achieved for nodule detection. The false-positive rate was low with an overall rate of 0.19. The overall accuracy was calculated as 0.84 for all nodules. While most studies evaluating AI performance in the detection of pulmonary nodules have evaluated a mix of varying nodules, these are the first results of a controlled phantom-based study using a balanced number of nodules of all sizes and densities. To increase the radiologist's diagnostic performance and minimize the risk of decision bias, such algorithms have an obvious benefit in a clinical scenario.
我们研究的目的是使用标准化体模方法，针对大量不同大小和密度的肺结节，评估人工智能（AI）算法在胸部X光片中检测肺结节的具体性能。总共450个具有不同密度（d1至d3）和大小（3、5、8、10和12毫米）的结节被放置在Lungman体模的不同位置。获取具有不同投影的X光图像，并使用AI算法进行结节检测处理。进行计算机断层扫描（CT）以作对照。通过人工一致阅片确定真实情况（可检测性）。结节检测的总体灵敏度和特异性分别达到0.978和0.812。假阳性率较低，总体率为0.19。所有结节的总体准确率计算为0.84。虽然大多数评估AI在肺结节检测中性能的研究评估的是不同结节的混合情况，但这些是基于体模的对照研究的首批结果，该研究使用了数量均衡的各种大小和密度的结节。为了提高放射科医生的诊断性能并将决策偏差风险降至最低，此类算法在临床场景中具有明显优势。
