Mirniaharikandehei Seyedehnafiseh, Abdihamzehkolaei Alireza, Choquehuanca Angel, Aedo Marco, Pacheco Wilmer, Estacio Laura, Cahui Victor, Huallpa Luis, Quiñonez Kevin, Calderón Valeria, Gutierrez Ana Maria, Vargas Ana, Gamero Dery, Castro-Gutierrez Eveling, Qiu Yuchen, Zheng Bin, Jo Javier A
School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019-1102, USA.
School of Systems Engineering and Informatics, Universidad Nacional de San Agustín de Arequipa, Arequipa 04000, Peru.
Bioengineering (Basel). 2023 Mar 2;10(3):321. doi: 10.3390/bioengineering10030321.
To help improve radiologists' efficacy of disease diagnosis in reading computed tomography (CT) images, this study aims to investigate the feasibility of applying a modified deep learning (DL) method as a new strategy to automatically segment disease-infected regions and predict disease severity.
We employed a public dataset acquired from 20 COVID-19 patients, which includes manually annotated lung and infections masks, to train a new ensembled DL model that combines five customized residual attention U-Net models to segment disease infected regions followed by a Feature Pyramid Network model to predict disease severity stage. To test the potential clinical utility of the new DL model, we conducted an observer comparison study. First, we collected another set of CT images acquired from 80 COVID-19 patients and process images using the new DL model. Second, we asked two chest radiologists to read images of each CT scan and report the estimated percentage of the disease-infected lung volume and disease severity level. Third, we also asked radiologists to rate acceptance of DL model-generated segmentation results using a 5-scale rating method.
Data analysis results show that agreement of disease severity classification between the DL model and radiologists is >90% in 45 testing cases. Furthermore, >73% of cases received a high rating score (≥4) from two radiologists.
This study demonstrates the feasibility of developing a new DL model to automatically segment disease-infected regions and quantitatively predict disease severity, which may help avoid tedious effort and inter-reader variability in subjective assessment of disease severity in future clinical practice.
为提高放射科医生在解读计算机断层扫描（CT）图像时疾病诊断的效率，本研究旨在探讨应用改进的深度学习（DL）方法作为一种新策略来自动分割疾病感染区域并预测疾病严重程度的可行性。
我们使用从20名新冠肺炎患者获取的公共数据集（其中包括手动标注的肺部和感染掩码）来训练一个新的集成DL模型，该模型结合了五个定制的残差注意力U-Net模型以分割疾病感染区域，随后使用特征金字塔网络模型来预测疾病严重程度阶段。为测试新DL模型的潜在临床实用性，我们进行了一项观察者比较研究。首先，我们收集了从80名新冠肺炎患者获取的另一组CT图像，并使用新DL模型处理图像。其次，我们请两名胸部放射科医生阅读每次CT扫描的图像，并报告疾病感染肺体积的估计百分比和疾病严重程度级别。第三，我们还请放射科医生使用5级评分方法对DL模型生成的分割结果的可接受性进行评分。
数据分析结果表明，在45个测试病例中，DL模型与放射科医生之间疾病严重程度分类的一致性>90%。此外，>73%的病例从两名放射科医生那里获得了高分（≥4）。
本研究证明了开发一种新的DL模型以自动分割疾病感染区域并定量预测疾病严重程度的可行性，这可能有助于在未来临床实践中避免在主观评估疾病严重程度时的繁琐工作和读者间的变异性。
