Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques

Baghdadi, Nadiah A.; Maklad, Ahmed S.; Malki, Amer; Deif, Mohanad A.

doi:10.3390/s22103846

Cited by 27 publications

(9 citation statements)

References 56 publications

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“…Although, there is no prior literature on the sensitivity and specificty of diagnosing pulmonary sarcoidosis from chest CT, prior work [15] suggests the performance of our method is similar to that of expert radiologists in diagnosing cardiac sarcoidosis with pulmonary and mediastinal involvement. The performance of our method is also higher than previous works that used chest x-ray to diagnose pulmonary sarcoidosis from healthy [21] or patients with pneumonia [20] involving deep learning or radiomics respectively with much smaller cohorts.…”

Section: Discussionmentioning

confidence: 77%

A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis

Qiu,

Mitra,

Ghose

et al. 2024

Preprint

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Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. Early accurate detection of pulmonary sarcoidosis may prevent progression to pulmonary fibrosis, a serious and potentially life-threatening form of the disease. However, the lack of a gold-standard diagnostic test and specific radiographic findings pose challenges in diagnosing pulmonary sarcoidosis. Chest computed tomography (CT) imaging is commonly used but requires expert, chest-trained radiologists to differentiate pulmonary sarcoidosis from lung malignancies, infections, and other ILDs. In this work, we developed a multichannel, CT and radiomics-guided ensemble network (RadCT-CNNViT) with visual explainability for pulmonary sarcoidosis vs. lung cancer (LCa) classification using chest CT images. We leverage CT and hand-crafted radiomics features as input channels, and a 3D convolutional neural network (CNN) and vision transformer (ViT) ensemble network for feature extraction and fusion before a classification head. The 3D CNN sub-network captures localized spatial information of lesions, while the ViT sub-network captures long-range, global dependencies between features. Through multichannel input and feature fusion, our model achieved the highest performance with accuracy, sensitivity, specificity, and combined AUC of 0.93±0.04, 0.94±0.04, 0.93±0.08 and 0.97 respectively in a 5-fold cross-validation study with pulmonary sarcoidosis (n=126) and LCa (n=93) cases. The model offers promising potential for improving the diagnosis of pulmonary sarcoidosis. A detailed ablation study showing the impact of CNN+ViT compared to CNN or ViT alone, and CT+radiomics input, compared to CT or radiomics alone, is also presented in this work.

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Section: Discussionmentioning

confidence: 77%

A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis

Qiu,

Mitra,

Ghose

et al. 2024

Preprint

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“…The evaluation of ChoA's performance involved a comparison with several benchmark optimization methods, such as whale optimization algorithm (WOA), grey wolf optimizer (GWO) 65 , and particle swarm optimization (PSO) 66 . The initial parameter values for each optimizer can be found in Table 3 .…”

Section: Methodsmentioning

confidence: 99%

Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction

Ahmed,

Alsenany,

Abdelaliem

et al. 2023

Sci Rep

Self Cite

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The utilization of mechanical ventilation is of utmost importance in the management of individuals afflicted with severe pulmonary conditions. During periods of a pandemic, it becomes imperative to build ventilators that possess the capability to autonomously adapt parameters over the course of treatment. In order to fulfil this requirement, a research investigation was undertaken with the aim of forecasting the magnitude of pressure applied on the patient by the ventilator. The aforementioned forecast was derived from a comprehensive analysis of many variables, including the ventilator's characteristics and the patient's medical state. This analysis was conducted utilizing a sophisticated computational model referred to as Long Short-Term Memory (LSTM). To enhance the predictive accuracy of the LSTM model, the researchers utilized the Chimp Optimization method (ChoA) method. The integration of LSTM and ChoA led to the development of the LSTM-ChoA model, which successfully tackled the issue of hyperparameter selection for the LSTM model. The experimental results revealed that the LSTM-ChoA model exhibited superior performance compared to alternative optimization algorithms, namely whale grey wolf optimizer (GWO), optimization algorithm (WOA), and particle swarm optimization (PSO). Additionally, the LSTM-ChoA model outperformed regression models, including K-nearest neighbor (KNN) Regressor, Random and Forest (RF) Regressor, and Support Vector Machine (SVM) Regressor, in accurately predicting ventilator pressure. The findings indicate that the suggested predictive model, LSTM-ChoA, demonstrates a reduced mean square error (MSE) value. Specifically, when comparing ChoA with GWO, the MSE fell by around 14.8%. Furthermore, when comparing ChoA with PSO and WOA, the MSE decreased by approximately 60%. Additionally, the analysis of variance (ANOVA) findings revealed that the p-value for the LSTM-ChoA model was 0.000, which is less than the predetermined significance level of 0.05. This indicates that the results of the LSTM-ChoA model are statistically significant.

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“…Certain measures have been correlated to pulmonary function testing, which is notable given the historical discrepancy in pulmonary function and qualitative imaging [109]. Machine and deep learning methodologies have also been used to create a decision tool for the diagnosis of sarcoidosis from imaging data and to help differentiate pulmonary sarcoidosis from tuberculosis [110,111]. As further validation of these techniques evolves, their application as novel outcome measures and prognostic biomarkers holds great promise, particularly as larger datasets incorporate serum, tissue, imaging, and clinical data.…”

Section: Biomarkers In the Management Of Therapymentioning

confidence: 99%

Treatment of Granulomatous Inflammation in Pulmonary Sarcoidosis

Gerke

2024

JCM

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The management of pulmonary sarcoidosis is a complex interplay of disease characteristics, the impact of medications, and patient preferences. Foremost, it is important to weigh the risk of anti-granulomatous treatment with the benefits of lung preservation and improvement in quality of life. Because of its high spontaneous resolution rate, pulmonary sarcoidosis should only be treated in cases of significant symptoms due to granulomatous inflammation, lung function decline, or substantial inflammation on imaging that can lead to irreversible fibrosis. The longstanding basis of treatment has historically been corticosteroid therapy for the control of granulomatous inflammation. However, several corticosteroid-sparing options have increasing evidence for use in refractory disease, inability to taper steroids to an acceptable dose, or in those with toxicity to corticosteroids. Treatment of sarcoidosis should be individualized for each patient due to the heterogeneity of the clinical course, comorbid conditions, response to therapy, and tolerance of medication side effects.

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Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques

Cited by 27 publications

References 56 publications

A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis

A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis

Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction

Treatment of Granulomatous Inflammation in Pulmonary Sarcoidosis

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