Observer performance evaluation of the feasibility of a deep learning model to detect cardiomegaly on chest radiographs

Ajmera, Pranav; Kharat, Amit; Gupte, Tanveer; Pant, Richa; Kulkarni, Viraj; Duddalwar, Vinay; Lamghare, Purnachandra

doi:10.1177/20584601221107345

Cited by 10 publications

(13 citation statements)

References 24 publications

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“…Then, the CTR can be calculated accordingly by traditional methods. Similar methods have been shown to be accurate and effective [ 12 , 13 , 14 , 15 , 16 ] in previous works. However, hemodialysis patients are predisposed to a considerable prevalence of pulmonary complications (e.g., pulmonary edema 8.20–20.50%, pleural effusions 22.95–33.80%) [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 69%

“…Previous studies have shown that CTRs determined by segmentation-based methods using deep learning can identify cardiomegaly with good accuracy [ 12 , 13 , 14 , 30 ]. The CXRs of the general population were selected for model training, validation, and testing.…”

Section: Discussionmentioning

confidence: 99%

“…However, even the senior nephrologists found it difficult at times to define the lung and heart margins. This is one of the limitations of CTR detection with plain CXRs [ 36 ], and chest computed tomography or magnetic resonance imaging [ 13 ] may be needed to obtain an accurate CTR when the above circumstances are present. To avoid erroneous segmentation with inaccurate CTR data, we developed an option for manual input to override any poorly predicted automated CTRs.…”

Section: Discussionmentioning

confidence: 99%

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Validation of an Automated Cardiothoracic Ratio Calculation for Hemodialysis Patients

et al. 2023

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Cardiomegaly is associated with poor clinical outcomes and is assessed by routine monitoring of the cardiothoracic ratio (CTR) from chest X-rays (CXRs). Judgment of the margins of the heart and lungs is subjective and may vary between different operators. Methods: Patients aged > 19 years in our hemodialysis unit from March 2021 to October 2021 were enrolled. The borders of the lungs and heart on CXRs were labeled by two nephrologists as the ground truth (nephrologist-defined mask). We implemented AlbuNet-34, a U-Net variant, to predict the heart and lung margins from CXR images and to automatically calculate the CTRs. Results: The coefficient of determination (R2) obtained using the neural network model was 0.96, compared with an R2 of 0.90 obtained by nurse practitioners. The mean difference between the CTRs calculated by the nurse practitioners and senior nephrologists was 1.52 ± 1.46%, and that between the neural network model and the nephrologists was 0.83 ± 0.87% (p < 0.001). The mean CTR calculation duration was 85 s using the manual method and less than 2 s using the automated method (p < 0.001). Conclusions: Our study confirmed the validity of automated CTR calculations. By achieving high accuracy and saving time, our model can be implemented in clinical practice.

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Section: Introductionmentioning

confidence: 69%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Validation of an Automated Cardiothoracic Ratio Calculation for Hemodialysis Patients

et al. 2023

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“…In a different study, the performance of radiologists in detecting cardiomegaly was assessed using the Attention U-Net deep learning architecture, which calculated the cardiothoracic ratio (CTR). The study revealed a moderate level of agreement, with a Kappa value of 0.506 [ 29 ]. Although our work is not directly comparable, the use of agreement statistics allows for comparison with both human readers and AI models.…”

Section: Discussionmentioning

confidence: 99%

Observer Performance Evaluation of a Deep Learning Model for Multilabel Classification of Active Tuberculosis Lung Zone-Wise Manifestations

Devasia,

Goswami,

Lakshminarayanan

et al. 2023

Cureus

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Background Chest X-rays (CXRs) are widely used for cost-effective screening of active pulmonary tuberculosis despite their limitations in sensitivity and specificity when interpreted by clinicians or radiologists. To address this issue, computer-aided detection (CAD) algorithms, particularly deep learning architectures based on convolution, have been developed to automate the analysis of radiography imaging. Deep learning algorithms have shown promise in accurately classifying lung abnormalities using chest X-ray images. In this study, we utilized the EfficientNet B4 model, which was pre-trained on ImageNet with 380x380 input dimensions, using its weights for transfer learning, and was modified with a series of components including global average pooling, batch normalization, dropout, and a classifier with 12 image-wise and 44 segment-wise lung zone evaluation classes using sigmoid activation. Objectives Assess the clinical usefulness of our previously created EfficientNet B4 model in identifying lung zone-specific abnormalities related to active tuberculosis through an observer performance test involving a skilled clinician operating in tuberculosis-specific environments. Methods The ground truth was established by a radiologist who examined all sample CXRs to identify lung zone-wise abnormalities. An expert clinician working in tuberculosis-specific settings independently reviewed the same CXR with blinded access to the ground truth. Simultaneously, the CXRs were classified using the EfficientNet B4 model. The clinician's assessments were then compared with the model's predictions, and the agreement between the two was measured using the kappa coefficient, evaluating the model's performance in classifying active tuberculosis manifestations across lung zones. Results The results show a strong agreement (Kappa ≥0.81) seen for lung zone-wise abnormalities of pneumothorax, mediastinal shift, emphysema, fibrosis, calcifications, pleural effusion, and cavity. Substantial agreement (Kappa = 0.61-0.80) for cavity, mediastinal shift, volume loss, and collapsed lungs. The Kappa score for lung zone-wise abnormalities is moderate (0.41-0.60) for 39% of cases. In image-wise agreement, the EfficientNet B4 model's performance ranges from moderate to almost perfect across categories, while in lung zone-wise agreement, it varies from fair to almost perfect. The results show strong agreement between the EfficientNet B4 model and the human reader in detecting lung zone-wise and image-wise manifestations. Conclusion The clinical utility of the EfficientNet B4 models to detect the abnormalities can aid clinicians in primary care settings for screening and triaging tuberculosis where resources are constrained or overburdened.

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“…Recently, Ajmera et al also deployed U-Net for segmenting areas of lungs and heart [16]. The CTR was calculated using widths of chest and heart.…”

Section: Introductionmentioning

confidence: 99%

Automatic measurement of cardiothoracic ratio in chest x-ray images with ProGAN-generated dataset

Kusakunniran

Saiviroonporn

Siriapisith

et al. 2023

ACI

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PurposeThe cardiomegaly can be determined by the cardiothoracic ratio (CTR) which can be measured in a chest x-ray image. It is calculated based on a relationship between a size of heart and a transverse dimension of chest. The cardiomegaly is identified when the ratio is larger than a cut-off threshold. This paper aims to propose a solution to calculate the ratio for classifying the cardiomegaly in chest x-ray images.Design/methodology/approachThe proposed method begins with constructing lung and heart segmentation models based on U-Net architecture using the publicly available datasets with the groundtruth of heart and lung masks. The ratio is then calculated using the sizes of segmented lung and heart areas. In addition, Progressive Growing of GANs (PGAN) is adopted here for constructing the new dataset containing chest x-ray images of three classes including male normal, female normal and cardiomegaly classes. This dataset is then used for evaluating the proposed solution. Also, the proposed solution is used to evaluate the quality of chest x-ray images generated from PGAN.FindingsIn the experiments, the trained models are applied to segment regions of heart and lung in chest x-ray images on the self-collected dataset. The calculated CTR values are compared with the values that are manually measured by human experts. The average error is 3.08%. Then, the models are also applied to segment regions of heart and lung for the CTR calculation, on the dataset computed by PGAN. Then, the cardiomegaly is determined using various attempts of different cut-off threshold values. With the standard cut-off at 0.50, the proposed method achieves 94.61% accuracy, 88.31% sensitivity and 94.20% specificity.Originality/valueThe proposed solution is demonstrated to be robust across unseen datasets for the segmentation, CTR calculation and cardiomegaly classification, including the dataset generated from PGAN. The cut-off value can be adjusted to be lower than 0.50 for increasing the sensitivity. For example, the sensitivity of 97.04% can be achieved at the cut-off of 0.45. However, the specificity is decreased from 94.20% to 79.78%.

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Observer performance evaluation of the feasibility of a deep learning model to detect cardiomegaly on chest radiographs

Cited by 10 publications

References 24 publications

Validation of an Automated Cardiothoracic Ratio Calculation for Hemodialysis Patients

Validation of an Automated Cardiothoracic Ratio Calculation for Hemodialysis Patients

Observer Performance Evaluation of a Deep Learning Model for Multilabel Classification of Active Tuberculosis Lung Zone-Wise Manifestations

Automatic measurement of cardiothoracic ratio in chest x-ray images with ProGAN-generated dataset

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