Image-based deep learning in diagnosing the etiology of pneumonia on pediatric chest X-rays.

Comparing the efficacy of a deep-learning model in classifying the etiology of pneumonia on pediatric chest X-rays (CXRs) with that of human readers. We built a clinical-pediatric CXR set containing 4035 patients to exploit a deep-learning model called Resnet-50 for differentiating viral from bacterial pneumonia. The dataset was split into training (80%) and validation (20%). Model performance was assessed by receiver operating characteristic curve and area under the curve (AUC) on the first test set of 400 CXRs collected from different studies. For the second test set composed of 100 independent examinations obtained from the daily clinical practice at our institution, the kappa coefficient was selected to measure the interrater agreement in a pairwise fashion for the reference standard, all reviewers, and the model. Gradient-weighted class activation mapping was used to visualize the significant areas contributing to the model prediction. On the first test set, the best-performing classifier achieved an AUC of 0.919 (p < .001), with a sensitivity of 79.0% and specificity of 88.9%. On the second test set, the classifier achieved performance similar to that of human experts, which resulted in a sensitivity of 74.3% and specificity of 90.8%, positive and negative likelihood ratios of 8.1 and 0.3, respectively. Contingence tables and kappa values further revealed that expert reviewers and model reached substantial agreements on differentiating the etiology of pediatric pneumonia. This study demonstrated that the model performed similarly as human reviewers and recognized the regions of pathology on CXRs.

E L ，Zhao B ，Liu H ，Zheng C ，Song X ，Cai Y ，Liang H ... -  《-》

Using deep-learning techniques for pulmonary-thoracic segmentations and improvement of pneumonia diagnosis in pediatric chest radiographs.

To evaluate the efficacy of a deep-learning model to segment the lung and thorax regions in pediatric chest X-rays (CXRs). Validating the diagnosis of bacterial or viral pneumonia could be improved after lung segmentation. A clinical-pediatric CXR set including 1351 patients was proposed to develop a deep-learning model for the pulmonary-thoracic segmentations. Model performance was evaluated by Jaccard's similarity coefficient (JSC) and Dice's coefficient (DC). Two adult CXR sets were used to assess the model's generalizability. According to the pulmonary-thoracic ratio, Pearson's correlation coefficient and the Bland-Altman plot were generated to demonstrate the correlation and agreement between manual and automatic segmentations. The receiver operating characteristic curves and areas under the curve (AUCs) were used to compare the pneumonia classification performance based on the lung-extracted images with that based on the original images. The model achieved JSCs of 0.910 and 0.950, DCs of 0.948 and 0.974 for lung and thorax segmentations, respectively. Pearson's r = 0.96, P < .0001. In the Bland-Altman plot, the mean difference was 0.0025 with a 95% confidence interval of (-0.0451, 0.0501). For testing with two adult CXR sets, the JSCs were 0.903 and 0.888, respectively, while the DCs were 0.948 and 0.937, respectively. After lung segmentation, the AUC of a classifier to identify bacterial or viral pneumonia increased from 0.815 to 0.879. We built a pediatric CXR dataset and exploited a deep-learning model for accurate pulmonary-thoracic segmentations. Lung segmentation can notably improve the diagnosis of bacterial or viral pneumonia.

E L ，Zhao B ，Guo Y ，Zheng C ，Zhang M ，Lin J ，Luo Y ，Cai Y ，Song X ，Liang H ... -  《-》

Testing the performance, adequacy, and applicability of an artificial intelligence model for pediatric pneumonia diagnosis.

Community-acquired Pneumonia (CAP) is a common childhood infectious disease. Deep learning models show promise in X-ray interpretation and diagnosis, but their validation should be extended due to limitations in the current validation workflow. To extend the standard validation workflow we propose doing a pilot test with the next characteristics. First, the assumption of perfect ground truth (100% sensitive and specific) is unrealistic, as high intra and inter-observer variability have been reported. To address this, we propose using Bayesian latent class models (BLCA) to estimate accuracy during the pilot. Additionally, assessing only the performance of a model without considering its applicability and acceptance by physicians is insufficient if we hope to integrate AI systems into day-to-day clinical practice. Therefore, we propose employing explainable artificial intelligence (XAI) methods during the pilot test to involve physicians and evaluate how well a Deep Learning model is accepted and how helpful it is for routine decisions as well as analyze its limitations by assessing the etiology. This study aims to apply the proposed pilot to test a deep Convolutional Neural Network (CNN)-based model for identifying consolidation in pediatric chest-X-ray (CXR) images already validated using the standard workflow. For the standard validation workflow, a total of 5856 public CXRs and 950 private CXRs were used to train and validate the performance of the CNN model. The performance of the model was estimated assuming a perfect ground truth. For the pilot test proposed in this article, a total of 190 pediatric chest-X-ray (CXRs) images were used to test the CNN model support decision tool (SDT). The performance of the model on the pilot test was estimated using extensions of the two-test Bayesian Latent-Class model (BLCA). The sensitivity, specificity, and accuracy of the model were also assessed. The clinical characteristics of the patients were compared according to the model performance. The adequacy and applicability of the SDT was tested using XAI techniques. The adequacy of the SDT was assessed by asking two senior physicians the agreement rate with the SDT. The applicability was tested by asking three medical residents before and after using the SDT and the agreement between experts was calculated using the kappa index. The CRXs of the pilot test were labeled by the panel of experts into consolidation (124/176, 70.4%) and no-consolidation/other infiltrates (52/176, 29.5%). A total of 31/176 (17.6%) discrepancies were found between the model and the panel of experts with a kappa index of 0.6. The sensitivity and specificity reached a median of 90.9 (95% Credible Interval (CrI), 81.2-99.9) and 77.7 (95% CrI, 63.3-98.1), respectively. The senior physicians reported a high agreement rate (70%) with the system in identifying logical consolidation patterns. The three medical residents reached a higher agreement using SDT than alone with experts (0.66±0.1 vs. 0.75±0.2). Through the pilot test, we have successfully verified that the deep learning model was underestimated when a perfect ground truth was considered. Furthermore, by conducting adequacy and applicability tests, we can ensure that the model is able to identify logical patterns within the CXRs and that augmenting clinicians with automated preliminary read assistants could accelerate their workflows and enhance accuracy in identifying consolidation in pediatric CXR images.

Domínguez-Rodríguez S ，Liz-López H ，Panizo-LLedot A ，Ballesteros Á ，Dagan R ，Greenberg D ，Gutiérrez L ，Rojo P ，Otheo E ，Galán JC ，Villanueva S ，García S ，Mosquera P ，Tagarro A ，Moraleda C ，Camacho D ... -  《-》

Multi-View Ensemble Convolutional Neural Network to Improve Classification of Pneumonia in Low Contrast Chest X-Ray Images.

Ferreira JR ，Armando Cardona Cardenas D ，Moreno RA ，de Fatima de Sa Rebelo M ，Krieger JE ，Antonio Gutierrez M ... -  《-》

Deep Learning Method for Automated Classification of Anteroposterior and Posteroanterior Chest Radiographs.

Kim TK ，Yi PH ，Wei J ，Shin JW ，Hager G ，Hui FK ，Sair HI ，Lin CT ... -  《-》