Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection

Fallahpoor, Maryam; Chakraborty, Subrata; Heshejin, Mohammad Tavakoli; Chegeni, Hossein; Horry, Michael J.; Pradhan, Biswajeet

doi:10.1016/j.compbiomed.2022.105464

Cited by 14 publications

(8 citation statements)

References 51 publications

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“…Fallahpoor et al 67 conducted a study aimed at assessing the generalization capabilities of deep learning models trained on 3D CT volumes of COVID-19 patients. They employed a methodology analogous to our own, involving the utilization of 3D convolutional layers to process the aforementioned CT volumes.…”

Section: Discussionmentioning

confidence: 99%

Potential diagnostic application of a novel deep learning- based approach for COVID-19

Sadeghi,

Sharifpour

et al. 2024

Sci Rep

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COVID-19 is a highly communicable respiratory illness caused by the novel coronavirus SARS-CoV-2, which has had a significant impact on global public health and the economy. Detecting COVID-19 patients during a pandemic with limited medical facilities can be challenging, resulting in errors and further complications. Therefore, this study aims to develop deep learning models to facilitate automated diagnosis of COVID-19 from CT scan records of patients. The study also introduced COVID-MAH-CT, a new dataset that contains 4442 CT scan images from 133 COVID-19 patients, as well as 133 CT scan 3D volumes. We proposed and evaluated six different transfer learning models for slide-level analysis that are responsible for detecting COVID-19 in multi-slice spiral CT. Additionally, multi-head attention squeeze and excitation residual (MASERes) neural network, a novel 3D deep model was developed for patient-level analysis, which analyzes all the CT slides of a given patient as a whole and can accurately diagnose COVID-19. The codes and dataset developed in this study are available at https://github.com/alrzsdgh/COVID. The proposed transfer learning models for slide-level analysis were able to detect COVID-19 CT slides with an accuracy of more than 99%, while MASERes was able to detect COVID-19 patients from 3D CT volumes with an accuracy of 100%. These achievements demonstrate that the proposed models in this study can be useful for automatically detecting COVID-19 in both slide-level and patient-level from patients’ CT scan records, and can be applied for real-world utilization, particularly in diagnosing COVID-19 cases in areas with limited medical facilities.

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Section: Discussionmentioning

confidence: 99%

Potential diagnostic application of a novel deep learning- based approach for COVID-19

Sadeghi,

Sharifpour

et al. 2024

Sci Rep

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“…Fallahpoor et al stated that creating a new dataset to overcome the generalizability problem is non-feasible and time-consuming, and they evaluated the effects of image preprocessing, different CNN models, and different combinations of datasets. 17 They assessed generalizability using various combinations of two large COVID-19 CT image datasets with deep learning, showing that a combination of datasets can improve generalizability. Zech et al pooled data from three different centers for their pneumonia-detection algorithms and obtained superior results compared with those of the separate datasets.…”

Section: Discussionmentioning

confidence: 99%

Can the generalizability issue of artificial intelligence be overcome? Pneumothorax detection algorithm

Verdi,

Yılmaz,

Doğan Mülazimoğlu

et al. 2023

Journal of Investigative Medicine

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The generalisability of artificial intelligence (AI) models is a major issue in the field of AI applications. Therefore, we aimed to overcome the generalisability problem of an AI model developed for a particular centre for pneumothorax detection by using a small dataset for external validation. Chest radiographs of patients diagnosed with pneumothorax (n=648) and those without pneumothorax (n=650) who visited the Ankara University Faculty of Medicine (AUFM; centre 1) were obtained. A deep learning-based pneumothorax detection algorithm (PDA-Alpha) was developed using the AUFM dataset. For implementation at the Health Sciences University (HSU; centre 2), PDA-Beta was developed through external validation of PDA-Alpha using 50 radiographs with pneumothorax obtained from HSU. Both PDA algorithms were assessed using the HSU test dataset (n=200) containing 50 pneumothorax and 150 non-pneumothorax radiographs. We compared the results generated by the algorithms with those of physicians to demonstrate the reliability of the results. The areas under the curve for PDA-Alpha and PDA-Beta were 0.993 (95% confidence interval (CI): 0.985–1.000) and 0.986 (95% CI: 0.962–1.000), respectively. Both algorithms successfully detected the presence of pneumothorax on 49/50 radiographs; however, PDA-Alpha had seven false-positive predictions, whereas PDA-Beta had one. The positive predictive value increased from 0.525 to 0.886 after external validation (p=0.041). The physicians’ sensitivity and specificity for detecting pneumothorax were 0.585 and 0.988, respectively. The performance scores of the algorithms were increased with a small dataset; however, further studies are required to determine the optimal amount of external validation data to fully address the generalisability issue.

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“…This imbalance can impact the performance of deep learning models, especially regarding sensitivity and specificity. To deal with the small imbalanced data, we used data augmentation to generate new real‐time training images, based on the selected original ECG images, during the training process 30 . As all the ECG images were taken in the same way for all the categories in the original dataset, one can select the category of interest without any special selection criteria.…”

Section: Methodsmentioning

confidence: 99%

Deep learning for COVID‐19 contamination analysis and prediction using ECG images on Raspberry Pi 4

Mhamdi,

Dammak,

Cottin

et al. 2023

Int J Imaging Syst Tech

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This paper's primary goal is to diagnose COVID‐19 contamination based on the artificial intelligence approach automatically. We used convolutional neural network deep learning algorithm for analyzing the ECG images to detect cardiac abnormalities, consequent of the contamination by the SARS‐CoV‐2 virus, responsible for the COVID‐19 epidemic. We designed, trained, and evaluated the performance of two deep learning models (MobileNetV2 and VGG16) in detecting and distinguishing between two different classes (healthy subjects and COVID‐19 positive cases). Indeed, this virus attacks the human respiratory system, which could affect the heart system. Thus, developing a deep learning model could help for a quick and efficient diagnosis, prediction, and physician decision‐making. The performed deep learning model will be used for predicting abnormal cardiac activities consequent to the contamination by the virus. The overall classification rate achieved by the models was 99.34% and 99.67% for MobileNetV2 and VGG16, respectively. Therefore, this approach can efficiently contribute to the diagnosis of COVID‐19 contamination.

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Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection

Cited by 14 publications

References 51 publications

Potential diagnostic application of a novel deep learning- based approach for COVID-19

Potential diagnostic application of a novel deep learning- based approach for COVID-19

Can the generalizability issue of artificial intelligence be overcome? Pneumothorax detection algorithm

Deep learning for COVID‐19 contamination analysis and prediction using ECG images on Raspberry Pi 4

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