Kaviani Parisa, Bizzo Bernardo C, Digumarthy Subba R, Dasegowda Giridhar, Karout Lina, Hillis James, Neumark Nir, Kalra Mannudeep K, Dreyer Keith J
Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
Mass General Brigham Data Science Office, Boston, MA 02114, USA.
Diagnostics (Basel). 2022 Jul 30;12(8):1844. doi: 10.3390/diagnostics12081844.
(1) Background: Optimal anatomic coverage is important for radiation-dose optimization. We trained and tested (R2.2.4) two (R3-2) deep learning (DL) algorithms on a machine vision tool library platform (Cognex Vision Pro Deep Learning software) to recognize anatomic landmarks and classify chest CT as those with optimum, under-scanned, or over-scanned scan length. (2) Methods: To test our hypothesis, we performed a study with 428 consecutive chest CT examinations (mean age 70 ± 14 years; male:female 190:238) performed at one of the four hospitals. CT examinations from two hospitals were used to train the DL classification algorithms to identify lung apices and bases. The developed algorithms were then tested on the data from the remaining two hospitals. For each CT, we recorded the scan lengths above and below the lung apices and bases. Model performance was assessed with receiver operating characteristics (ROC) analysis. (3) Results: The two DL models for lung apex and bases had high sensitivity, specificity, accuracy, and areas under the curve (AUC) for identifying under-scanning (100%, 99%, 99%, and 0.999 (95% CI 0.996-1.000)) and over-scanning (99%, 99%, 99%, and 0.998 (95%CI 0.992-1.000)). (4) Conclusions: Our DL models can accurately identify markers for missing anatomic coverage and over-scanning in chest CTs.
(1) 背景：最佳解剖覆盖范围对于优化辐射剂量很重要。我们在机器视觉工具库平台（康耐视视觉Pro深度学习软件）上训练并测试了两种深度学习（DL）算法，以识别解剖标志并将胸部CT分类为扫描长度最佳、扫描不足或扫描过度的CT。(2) 方法：为了验证我们的假设，我们对四家医院之一进行的428例连续胸部CT检查（平均年龄70±14岁；男∶女为190∶238）进行了研究。来自两家医院的CT检查用于训练DL分类算法以识别肺尖和肺底。然后在其余两家医院的数据上测试所开发的算法。对于每例CT，我们记录了肺尖和肺底上方和下方的扫描长度。使用受试者操作特征（ROC）分析评估模型性能。(3) 结果：用于肺尖和肺底的两种DL模型在识别扫描不足（100%、99%、99%和0.999（95%CI 0.996 - 1.000））和扫描过度（99%、99%、99%和0.998（95%CI 0.992 - 1.000））方面具有高灵敏度、特异性、准确性和曲线下面积（AUC）。(4) 结论：我们的DL模型可以准确识别胸部CT中解剖覆盖缺失和扫描过度的标志。
