Ms. Aditi Kaveti Profile

Aditi Kaveti

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SPIE Journal Paper | 28 December 2023 Open Access

Assessment of a deep learning model for COVID-19 classification on chest radiographs: a comparison across image acquisition techniques and clinical factors

Mena Shenouda, Isabella Flerlage, Aditi Kaveti, Maryellen L. Giger, Samuel G. Armato

JMI, Vol. 10, Issue 06, 064504, (December 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.6.064504

KEYWORDS: COVID 19, Performance modeling, Data modeling, Lung, Chest imaging, Education and training, Deep learning, Image classification, Image fusion, Image acquisition

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PurposeThe purpose is to assess the performance of a pre-trained deep learning model in the task of classifying between coronavirus disease (COVID)-positive and COVID-negative patients from chest radiographs (CXRs) while considering various image acquisition parameters, clinical factors, and patient demographics.MethodsStandard and soft-tissue CXRs of 9860 patients comprised the “original dataset,” consisting of training and test sets and were used to train a DenseNet-121 architecture model to classify COVID-19 using three classification algorithms: standard, soft tissue, and a combination of both types of images via feature fusion. A larger more-current test set of 5893 patients (the “current test set”) was used to assess the performance of the pretrained model. The current test set contained a larger span of dates, incorporated different variants of the virus and included different immunization statuses. Model performance between the original and current test sets was evaluated using area under the receiver operating characteristic curve (ROC AUC) [95% CI].ResultsThe model achieved AUC values of 0.67 [0.65, 0.70] for cropped standard images, 0.65 [0.63, 0.67] for cropped soft-tissue images, and 0.67 [0.65, 0.69] for both types of cropped images. These were all significantly lower than the performance of the model on the original test set. Investigations regarding matching the acquisition dates between the test sets (i.e., controlling for virus variants), immunization status, disease severity, and age and sex distributions did not fully explain the discrepancy in performance.ConclusionsSeveral relevant factors were considered to determine whether differences existed in the test sets, including time period of image acquisition, vaccination status, and disease severity. The lower performance on the current test set may have occurred due to model overfitting and a lack of generalizability.

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Proceedings Article | 7 April 2023 Presentation + Paper

Assessing robustness of a deep-learning model for COVID-19 classification on chest radiographs

Mena Shenouda, Aditi Kaveti, Isabella Flerlage, Jayashree Kalpathy-Cramer, Maryellen Giger, Samuel Armato

Proceedings Volume 12465, 124650F (2023) https://doi.org/10.1117/12.2652106

KEYWORDS: COVID 19, Chest imaging, Visualization, Diseases and disorders, Artificial intelligence, Visual process modeling, Radiography, Deep learning

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The COVID-19 pandemic has made a dramatic impact on human life, medical systems, and financial resources. Due to the disease’s pervasive nature, many different and interdisciplinary fields of research pivoted to study the disease. For example, deep learning (DL) techniques were employed early to assess patient diagnosis and prognosis from chest radiographs (CXRs) and computed tomography (CT) scans. While the use of artificial intelligence (AI) in the medical sector has displayed promising results, DL may suffer from lack of reproducibility and generalizability. In this study, the robustness of a pre-trained DL model utilizing the DenseNet-121 architecture was evaluated by using a larger collection of CXRs from the same institution that provided the original model with its test and training datasets. The current test set contained a larger span of dates, incorporated different strains of the virus, and included different immunization statuses. Considering differences in these factors, model performance between the original and current test sets was evaluated using area under the receiver operating characteristic curve (ROC AUC) [95% CI]. Statistical comparisons were performed using the Delong, KolmogorovSmirnov, and Wilcoxon rank-sum tests. Uniform manifold approximation and projection (UMAP) was used to help visualize whether underlying causes were responsible for differences in performance between test sets. In the task of classifying between COVID-positive and COVID-negative patients, the DL model achieved an AUC of 0.67 [0.65, 0.70], compared with the original performance of 0.76 [0.73, 0.79]. The results of this study suggest that underlying biases or overfitting may hinder performance when generalizing the model.

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