PMC:7782580 / 9823-30388 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7782580","sourcedb":"PMC","sourceid":"7782580","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7782580","text":"Results\n\nData set properties\nMulti-modal data from multiple sources were used in this study. X-data, CT-data, and clinical data used in our research were collected from four public data sets and one frontline hospital data (Youan hospital). Each data set was divided into two parts: train-val part and test part using a train-test-split function (TTSF) of the scikit-learn library which is shown in Table 1. The details of the multi-modal data types are described in the “Methods” section (see “Data sets splitting” section).\nTable 1 Number of cases from four public data sets and the Youan hospital (X-data, CT-data, clinical indicator data).\nStudy X-data CT-data Clinical data\nTrain + Val Test Train + Val Test Train + Val Test\n*Normal (RSNA + LUNA16) 5000 100 100 20 – –\nPneumonia (RSNA + ICNP) 3000 100 83 20 – –\nCOVID-19 (CCD) 150 62 – – – –\nInfluenza (Youan Hospital) 100 45 35 15 – –\n*Normal (Youan Hospital) 478 25 139 20 – –\nPneumonia (Youan Hospital) 380 55 180 35 – –\nCOVID-19 (Youan Hospital) 35 10 75 20 75 20\nTotal 9143 397 612 130 75 20\nThe term *Normal in this work means the cases where the lungs are not manifest evidence of COVID-19, influenza, or pneumonia on imaging and the RT-PCR testing of the COVID-19 is negative.\n\nA platform was developed for annotating lesion areas of COVID-19 in medical images (X-data, CT-data)\nMedical imaging uses images of internal tissues of the human body or a part of the human body in a non-invasive manner for clinical diagnoses or treatment plans36. Medical images (e.g., X-data and CT-data) are usually acquired using computed radiography and are typically stored in the Digital Imaging and Communications in Medicine (DICOM) format37. X-data are two-dimensional grayscale images, and CT-data are three-dimensional data, consisting of slices of the data in the z axis direction of a two-dimensional grayscale image. Machine learning methods are playing increasingly important roles in medical image analysis, especially DL methods. DL uses multiple non-linear transformations to create a mapping relationship between the input data and output labels38. The objective of this study was to annotate lesion areas in medical images with high accuracy. Therefore, we developed a pseudo-coloring method, which is a technique that helps enhance medical images for physicians to isolate relevant tissues and groups different tissues together39. We converted the original grayscale images to color images using the open-source image processing tools Open Source Computer Vision Library (OpenCV) and Pillow. Examples of the pseudo-color images are shown in Fig. 1a. We developed a platform that uses a client-server architecture to annotate the potential lesion areas of COVID-19 on the CXR and CT images. The platform can be deployed on a private cloud for security and local sharing. All the images were annotated by two experienced radiologists (one was a 5th-year radiologist and the other was a 3rd-year radiologist) in the Youan Hospital. If there was disagreement about a result, a senior radiologist and a respiratory doctor made the final decision to ensure the precision of the annotation process. The details of the annotation pipeline are shown in Supplementary Fig. 1.\nFig. 1 Demonstrations of data preprocessing methods including pseudo-coloring and dimension normalization.\na Pseudo-coloring for abnormal examples in the CXR and CT images. The original grayscale images were transformed into color images using the pseudo-coloring method and were annotated by the experts. The scale bar on the right is the range of pixel values of the image data. b Dimension normalization to reduce the dimensions in the CT images. The number of CT images were first resampled to a multiple of three and then divided into three groups. Followed by the 1 × 1 convolution layers to reduce the dimensions of the data.\n\nPCA was used to determine the characteristics of the medical images for the COVID-19, influenza, and normal cases\nPCA was used to visually compare the characteristics of the medical images (X-data, CT-data) for the COVID-19 cases with those of the normal and influenza cases. Figure 2a shows the mean image of each category and the five eigenvectors that represent the principal components of PCA in the corresponding feature space. Significant differences are observed between the COVID-19, influenza, and normal cases, indicating the possibility of being able to distinguish COVID-19 cases from normal and influenza cases.\nFig. 2 PCA visualizations and example heatmaps of both X-data and CT-data.\na Mean image and eigenvectors of five different sub-data sets. The first column shows the mean image and the other columns show the eigenvectors. The first row shows the mean image and five eigenvectors of the normal CXR images; second row: COVID-19 CXR images, third row: normal CT images, fourth row: influenza CT images, last row: COVID-19 CT images. The scale bar on the right is the range of pixel values of the image data. b Heatmaps of both X-data and CT-data were demonstrated for better interpretability of the proposed frameworks. The scale bar on the right is the probability of the areas being suspected as infections.\n\nThe CNN-based classification framework exhibited excellent performance based on the validation by experts using multi-modal data from public data sets and Youan hospital\nThe structure of the proposed framework, consisting of the stage I sub-framework and the stage II sub-framework is shown in Fig. 3a, where Q, L, M, and N are the hyper-parameters of the framework for general use cases. The values of Q, L, M, and N were 1, 1, 2, and 2, respectively, in this study; this framework referred to as the CNNCF framework. The stage I and stage II sub-frameworks were designed to extract features corresponding to different optimization goals in the analysis of the medical images. The performance of the CNNCF was evaluated using multi-modal data sets (X-data and CT-data) to ensure the generalization and transferability of the model, and five evaluation indicators were used (sensitivity, precision, specificity, F1, kappa). The salient features of the images extracted by the CNNCF were visualized in a heatmap (four examples are shown in Fig. 2b). In this study, multiple experiments were conducted (including experiments that included data from the same source and from different sources) to validate the generalization ability of the framework while avoiding the possible sample selection bias. Five experts evaluated the images, i.e., a 7th-year respiratory resident (Respira.), a 3rd-year emergency resident (Emerg.), a 1st-year respiratory intern (Intern), a 5th-year radiologist (Rad-5th), and a 3rd-year radiologist (Rad-3rd). The definition of the expert group can be found in Supplementary Note 1. The abbreviations of all the data sets used in the following experiments including XPDS, XPTS, XPVS, XHDS, XHTS, XHVS, CTPDS, CTPTS, CTPVS, CTHDS, CTHTS, CTHVS, CADS, CATS, CAVS, XMTS, XMVS, CTMTS, and CTMVS were defined in the “Methods” section (see “Data sets splitting” section). The following results were obtained.\nFig. 3 CNN-based frameworks.\na The classification framework for the identification of COVID-19. b The regression framework for the correlation analysis between the lesion areas and the clinical indicators. c is the workflow of the classification framework for the identification of COVID-19.\n\nExperiment-A\nIn this experiment, we used the X-data of the XPVS where the normal cases were from the RSNA data set and the COVID-19 cases were from the COVID CXR data set (CCD) data set. The results of the five evaluation indicators for the comparison of the COVID-19 cases and normal cases of the XPVS are shown in Table 2. An excellent performance was obtained, with the best score of specificity of 99.33% and a precision of 98.33%. The F1 score was 96.72%, which was higher than that of the Respire. (96.12%), the Emerg. (93.94%), the Intern (84.67%), and the Rad-3rd (85.93%) and lower than that of the Rad-5th (98.41%). The kappa index was 95.40%, which was higher than that of the Respire. (94.43%), the Emerg. (91.21%), the Intern (77.45%), and the Rad-3rd (79.42%), and lower than that of the Rad-5th (97.74%). The sensitivity index was 95.16%, which was higher than that of the Intern (93.55%) and the Rad-3rd (93.55%) and lower than that of the Respire. (100%), the Emerg. (100%) and Rad-5th (100%). The receiver operating characteristic (ROC) scores for the CNNCF and the experts are plotted in Fig. 4a; the area under the ROC curve (AUROC) of the CNNCF is 0.9961. The precision-recall scores for the CNNCF and the experts are plotted in Fig. 4d; the area under the precision-recall curve (AUPRC) of the CNNCF is 0.9910.\nTable 2 Performance indices of the classification framework (CNNCF) of experiment A and the average performance of the 7th-year respiratory resident (Respira.), the 3rd-year emergency resident (Emerg.), the 1st-year respiratory intern (Intern), the 5th-year radiologist (Rad-5th), and the 3rd-year radiologist (Rad-3rd).\nF1 (95% CI) Kappa (95% CI) Specificity (95% CI) Sensitivity (95% CI) Precision (95% CI)\nCNNCF 0. 9672 (0.9307, 0.9890) 0.9540 (0.9030, 0.9924) 0.9933 (0.9792, 1.0000) 0.9516 (0.8889, 1.0000) 0. 9833 (0.9444, 1.0000)\nRespire. 0.9612 (0.9231, 0.9920) 0.9443 (0.8912, 0.9887) 0.9667 (0.9363, 0.9933) 1.0000 (1.0000, 1.0000) 0.9254 (0.8095, 0.9571)\nEmerg. 0. 9394 (0.8947, 0.9781) 0.9121 (0.8492, 0.9677) 0.9467 (0.9091, 0.9797) 1.0000 (1.0000, 1.0000) 0.8857 (0.8095, 0.9571)\nIntern. 0.8467 (0.7692, 0.9041) 0.7745 (0.6730, 0.8592) 0.8867 (0.8333, 0.9343) 0.9355 (0.8596, 0.984) 0.7733 (0.6708, 0.8649)\nRad-5th 0.9841 (0.9593, 1.0000) 0.9774 (0.9433, 1.0000) 0.9867 (0.9662, 1.0000) 1.0000 (1.0000, 1.0000) 0.9688 (0.9219, 1.0000)\nRad-3rd 0.8593 (0.7931, 0.9180) 0.7942 (0.7062, 0.8779) 0.9000 (0.8541, 0.9481) 0.9355 (0.8666, 0.9841) 0.7945 (0.6974, 0.8873)\nFig. 4 ROC and PRC curves for the CNNCF of the experiments A-C.\nNC indicates that the positive case is a COVID-19 case, and the negative case is *Normal. CI indicates that the positive case is COVID-19, and the negative case is influenza. The points are the results of experts, corresponding to the results in Tables 2 and 3. The background gray dashed curves in the PRC curve correspond to the iso-F1 curves. a ROC curve for the NC using X-data. b ROC curve for the NC using CT-data. c ROC curve for the CI using CT-data. d PRC curve for the NC using X-data. e PRC curve for the NC using CT-data. f PRC curve for the CI using CT-data.\n\nExperiment-B\nIn this experiment, we used the CT-data of the CTPVS and CTHVS where the normal cases were from the LUNA data set and the COVID-19 cases were from the Youan hospital. The results of the five evaluation indicators for the comparison of the COVID-19 cases and normal cases of the CTHVS and the CTPVS are shown in Table 3, where the normal cases are from CTPVS and the COVID-19 cases are from the CTHVS. The CNNCF exhibits good performance for the five evaluation indices, which are similar to that of the Respire. and the Rad-5th and higher than that of the Intern, the Emerg. and Rad-3rd. The ROC scores are plotted in Fig. 4b; the AUROC of the CNNCF is 1.0. The precision-recall scores are shown in Fig. 4e; the AUPRC of the CNNCF is 1.0.\nTable 3 Performance indices of the classification framework (CNNCF) of the experiments B and C, and the average performance of the 7th-year respiratory resident (Respira.), the 3rd-year emergency resident (Emerg.), the 1st-year respiratory intern (Intern), the 5th-year radiologist (Rad-5th), and the 3rd-year radiologist (Rad-3rd).\nCT (*Normal and COVID-19 cases)\nCNNCF Respire. Emerg. Intern. Rad-5th Rad-3rd\nF1 (95% CI) 1.0000 (1.0000, 1.0000) 1.0000 (1.0000, 1.0000) 1.0000 (1.0000, 1.0000) 0.9500 (0.8571, 1.0000) 1.0000 (1.0000, 1.0000) 0.9500 (0.8667, 1.0000)\nKappa (95% CI) 1.0000 (1.0000, 1.0000) 1.0000 (1.0000, 1.0000) 1.0000 (1.0000, 1.0000) 0.9500 (0.7422, 1.0000) 1.0000 (1.0000, 1.0000) 0.9000 (0.7487, 1.0000)\nSpecificity (95% CI) 1.0000 (1.0000, 1.0000) 1.0000 (1.0000, 1.0000) 1.0000 (1.0000, 1.0000) 0.9500 (0.8333, 1.0000) 1.0000 (1.0000, 1.0000) 0.9500 (0.8333, 1.0000)\nSensitivity (95% CI) 1.0000 (1.0000, 1.0000) 1.0000 (1.0000, 1.0000) 1.0000 (1.0000, 1.0000) 0.9500 (0.8333, 1.0000) 1.0000 (1.0000, 1.0000) 0.9500 (0.8421, 1.0000)\nPrecision (95% CI) 1.0000 (1.0000, 1.0000) 1.0000 (1.0000, 1.0000) 1.0000 (1.0000, 1.0000) 0.9500 (0.8235, 1.0000) 1.0000 (1.0000, 1.0000) 0.9500 (0.8333, 1.0000)\nCT (Influenza and COVID-19 cases)\nCNNCF Respire. Emerg. Intern. Rad-5th Rad-3rd\nF1 (95%CI) 1.0000 (1.0000, 1.0000) 1.0000 (1.0000, 1.0000) 0.8966 (0.7332, 1.0000) 0.8000 (0.6207, 0.9412) 0.9677 (0.8889, 1.0000) 0.8667 (0.7199, 0.9744)\nKappa (95%CI) 1.0000 (1.0000, 1.0000) 1.0000 (1.0000, 1.0000) 0.8236 (0.5817, 1.0000) 0. 6500 (0.3698, 0.8852) 0.9421 (0.8148, 1.0000) 0.7667 (0.5349, 0.9429)\nSpecificity (95%CI) 1.0000 (1.0000, 1.0000) 1.0000 (1.0000, 1.0000) 0.9048 (0.7619, 1.0000) 0.8500 (0.6818, 1.0000) 0.9500 (0.8333, 1.0000) 0.9000 (0.7619, 1.0000)\nSensitivity (95%CI) 1.0000 (1.0000, 1.0000) 1.0000 (1.0000, 1.0000) 0.9286 (0.7500, 1.0000) 0.8000 (0.5714, 1.0000) 1.0000 (1.0000, 1.0000) 0.8667 (0.6667, 1.0000)\nPrecision (95%CI) 1.0000 (1.0000, 1.0000) 1.0000 (1.0000, 1.0000) 0.8667 (0.6874, 1.0000) 0.8000 (0.5881, 1.0000) 0.9375 (0.8000, 1.0000) 0.8667 (0.6667, 1.0000)\n\nExperiment-C\nIn this experiment, we used the CT-data of the CTHVS where the normal cases and the COVID-19 cases were all from the Youan hospital. The results of the five evaluation indicators for the comparison of the COVID-19 cases and influenza cases of the CTHVS are shown in Table 3 where the influenza cases and the COVID-19 cases are all from the CTHVS. The CNNCF achieved the highest performance and the best score of all five evaluation indices. The ROC scores are plotted in Fig. 4c; the AUROC of the CNNCF is 1.0. The precision-recall scores are shown in Fig. 4f, and the AUPRC of the CNNCF is 1.0.\n\nExperiment-D\nThe boxplots of the five evaluation indicators, the F1 score (Fig. 5a, d, g), the kappa coefficient (Fig. 5b, e, h), and the specificity (Fig. 5c, f, i) of experiments A–C are shown in Fig. 5, and the precision and sensitivity are shown in Supplementary Fig. 2. A bootstrapping method40 was used to calculate the empirical distributions, and McNemar’s test41 was used to analyze the differences between the CNNCF and the experts. The p-values of the McNemar’s test (Supplementary Tables 1–3) for the five evaluation indicators were all 1.0, indicating no statistically significant difference between the CNNCF results and the expert evaluations.\nFig. 5 Boxplots of the F1 score, kappa score, and specificity for the CNNCF and expert results for COVID-19 identification.\nNC indicates that the positive case is a COVID-19 case, and the negative case is *Normal. CI indicates that the positive case is COVID-19, and the negative case is influenza. Bootstrapping is used to generate n = 1000 resampled independent validation sets for the XVS and the CTVS. a F1 score for the NC using X-data. b Kappa score for the NC using X-data. c Specificity for the NC using X-data. d F1 score for the NC using CT-data. e Kappa score for the NC using CT-data. f Specificity for the NC using CT-data. g F1 score for the CI using CT-data. h Kappa score for the CI using CT-data. i Specificity for the CI using CT-data.\nWe also conducted extra experiments with both configurations of the same data source and different data sources: the descriptions and graph charts can be found in the Supplementary Experiments and Tables (Supplementary Tables 4–19 and Supplementary Figs. 3–18). The data used in experiments E–G were CTHVS and the data were all from the Youan hospital. The data used in experiments H–K were XHVS and the data were all from the Youan hospital. The data used in experiments L–N were XPVS and CTPVS. The data used in the experiment L was from the same data set RSNA, while the data used in experiment M was from different data sets where the pneumonia cases were from the ICNP, and the normal cases were from LUNA16. The data used in the experiments O–R, from the four public data sets and one hospital (Youan hospital) data set (including normal cases, pneumonia cases and COVID-19 cases), were XMVS and CTMVS. In all the experiments (experiments A–R), the CNNCF achieved good performance. Notably, in order to obtain a more comprehensive evaluation of the CNNCF while further improving the usability in clinical practice, experiment-R was performed. In the experiment-R, the CNNCF was used to distinguish three types of cases simultaneously (Including the COVID-19, pneumonia, and normal cases) on both the XMVS and CTMVS. Good performances were obtained on the XMVS, with the best score of F1 score of 91.89%, kappa score of 89.74%, specificity of 97.14%, sensitivity of 94.44%, and a precision of 89.47%, respectively. Excellent performances were obtained on the CTMVS, with the best score of the five evaluation indicators were all 100.00%. The ROC score and PRC score in the experiment-R were also satisfactory which were shown in Supplementary Fig. 18. The results of the experiment-R further demonstrated the effectiveness and robustness of the proposed CNNCF.\n\nImage analysis identifies salient features of COVID-19\nIn clinical practice, the diagnostic decision of a clinician relies on the identification of the SAs in the medical images by radiologists. The statistical results show that the performance of the CNNCF for the identification of COVID-19 is as good as that of the experts. A comparison consisting of two parts was performed to evaluate the discriminatory ability of the CNNCF. In the first part, we used Grad-CAM, which is a non-intrusive method to extract the salient features in medical images, to create a heatmap of the CNNCF result. Figure 2b shows the heatmaps of four examples of COVID-19 cases in the X-data and CT-data. In the second part, we used density-based spatial clustering of applications with noise (DBSCAN) to calculate the center pixel coordinates (CPC) of the salient features corresponding to COVID-19. All CPCs were normalized to a range of 0 to 1. Subsequently, we used a significance test (ST)42 to analyze the relationship between the CPC of the CNNCF output and the CPC annotated by the experts. A good performance was obtained, with a mean square error (MSE) of 0.0108, a mean absolute error (MAE) of 0.0722, a root mean squared error (RMSE) of 0.1040, a correlation coefficient (r) of 0.9761, and a coefficient of determination (R2) of 0.8801.\n\nA strong correlation was observed between the lesion areas detected by the proposed framework and the clinical indicators\nIn clinical practice, multiple clinical indicators are analyzed to determine whether further examinations (i.e., medical image examination) are needed. These indicators can be used to assess the predictive ability of the model. In addition, various examinations are required to perform an accurate diagnosis in clinical practice. However, the correlations between the results of various examinations are often not clear. We used the stage II sub-framework and the regressor block of the CNNRF to conduct a correlation analysis between the lesion areas detected by the framework and five clinical indicators (white blood cell count, neutrophil percentage, lymphocyte percentage, procalcitonin, C-reactive protein) of COVID-19 using the CADS. The inputs of the CNNRF were the lesion area images of each case, and the output was a 5-dimensional vector describing the correlation between the lesion areas and the five clinical indicators.\nThe MAE, MSE, RMSE, r, and R2 were used to evaluate the results. The ST and the Pearson correlation coefficient (PCC)43 were used to determine the correlation between the lesion areas and the clinical indicators. A strong correlation was obtained, with MSE = 0.0163, MAE = 0.0941, RMSE = 0.1172, r = 0.8274, and R2 = 0.6465. At a significance level of 0.001, the value of r was 1.27 times the critical value of 0.6524. This result indicates a high and significant correlation between the lesion areas and the clinical indicators. The PCC was 0.8274 (range of 0.8–1.0), indicating a strong correlation. The CNNRF was trained on the CATS and evaluated using the CAVS. The initial learning rate was 0.01, and the optimization function was the stochastic gradient descent (SGD) method44. 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