Intelligent classification of B-line and white lung from COVID-19 pneumonia ultrasound images using radiomics analysis

Cao, Yucheng; Duan, Xi; Hou, Size; Xing, Wenyu; Yang, Mo; Ma, Yebo; Wang, Zhuoran; Li, Wenfang; Li, Qingli; He, Chao; Chen, Jiangang

doi:10.1145/3543377.3543384

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…While the above-mentioned techniques for reducing the number of variables can eliminate redundant and irrelevant features, de Paiva, Pereira, and de Andrade argue that it is not always clear whether these methods result in improvements in the predictive power of ML models [65]. Furthermore, as these methods project the features to a new dimension and the features in the new dimension become mixed features, these new features might not necessarily provide a strong explanatory basis [66]. Despite that, some of the aforementioned studies have provided XAI along with the corresponding AI algorithms.…”

Section: Methods [Source] Explanationmentioning

confidence: 99%

Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic

Khalili,

Wimmer

2024

Life

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By applying AI techniques to a variety of pandemic-relevant data, artificial intelligence (AI) has substantially supported the control of the spread of the SARS-CoV-2 virus. Along with this, epidemiological machine learning studies of SARS-CoV-2 have been frequently published. While these models can be perceived as precise and policy-relevant to guide governments towards optimal containment policies, their black box nature can hamper building trust and relying confidently on the prescriptions proposed. This paper focuses on interpretable AI-based epidemiological models in the context of the recent SARS-CoV-2 pandemic. We systematically review existing studies, which jointly incorporate AI, SARS-CoV-2 epidemiology, and explainable AI approaches (XAI). First, we propose a conceptual framework by synthesizing the main methodological features of the existing AI pipelines of SARS-CoV-2. Upon the proposed conceptual framework and by analyzing the selected epidemiological studies, we reflect on current research gaps in epidemiological AI toolboxes and how to fill these gaps to generate enhanced policy support in the next potential pandemic.

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Section: Methods [Source] Explanationmentioning

confidence: 99%

Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic

Khalili,

Wimmer

2024

Life

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“…Finally, they used K-nearest neighbors (KNN) and tested their accuracy using 10-fold crossvalidation to classify the regions as muscular tissue or lung tissue. In [13], Cao et al manually selected a region of interest (ROI) containing B-lines or white lung. Then, they extracted radiomic features such as first-order statistical features using wavelet filtering, Grey Level Co-Occurrence Matrix (GLCM) features, Gray Level Run Length Matrix (GLRLM) features, Gray Level Size Zone Matrix (GLSZM) features, Neighboring Gray Tone Difference Matrix (NGTDM) features, Gray Level Dependence Matrix (GLDM) features and Shape features.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, they used a support vector machine (SVM) for classifying the manually selected regions as B-line or white lung. Fundamentally, they are selecting a ROI in the image and classifying it as chest or lung in [12] and in [13] they are classifying the ROI as 2 morphologies, B-lines or white lung (which is a large area of coalescent B-lines).…”

Section: Introductionmentioning

confidence: 99%

Classification of Lung Pathologies in Neonates using Dual Tree Complex Wavelet Transform

Aujla¹,

Mohamed²,

Tan³

et al. 2023

Preprint

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Annually 8500 neonatal deaths are reported in the US due to respiratory failure, this is a third of all neonatal deaths in the US. Recently, Lung Ultrasound (LUS), due to its ionizing-radiation free nature, portability, and being cheaper is gaining wide acceptability as a diagnostic tool for different lung conditions. However, lack of highly trained medical professionals has limited its use especially in remote areas. To address this, an automated screening system that captures characteristics of the LUS patterns can be of significant assistance to clinicians who are not experts in lung ultrasound (LUS) images. In this paper, we propose a feature extraction method designed to quantify the spatially-localized line patterns and texture patterns found in LUS images. Using the dual-tree complex wavelet transform (DTCWT) and four types of common image features we propose a method to classify the LUS images into 6 common neonatal lung conditions. These conditions are normal lung, pneumothorax (PTX), transient tachypnea of the newborn (TTN), respiratory distress syndrome (RDS), chronic lung disease (CLD) and consolidation (CON) that could be pneumonia or atelectasis. The proposed method using DTCWT decomposition extracted global statistical, grey-level co-occurrence matrix (GLCM), grey-level run length matrix (GLRLM) and linear binary pattern (LBP) features to be fed to a linear discriminative analysis (LDA) based classifier. Using 15 best DTCWT features along with 3 clinical features the proposed approach achieved a per-image classification accuracy of 92.78% with a balanced dataset containing 720 images from 24 patients and 74.39% with the larger (class unbalanced) dataset containing 1550 images from 42 patients. Likewise, the proposed method achieved a maximum per-subject classification accuracy of 81.53% with 43 DTCWT features and 3 clinical features using the balanced dataset and 64.97% with 13 DTCWT features and 3 clinical features using the unbalanced larger dataset.

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Classification of lung pathologies in neonates using dual-tree complex wavelet transform

Aujla,

Mohamed,

Tan

et al. 2023

BioMed Eng OnLine

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Introduction Undiagnosed and untreated lung pathologies are among the leading causes of neonatal deaths in developing countries. Lung Ultrasound (LUS) has been widely accepted as a diagnostic tool for neonatal lung pathologies due to its affordability, portability, and safety. However, healthcare institutions in developing countries lack well-trained clinicians to interpret LUS images, which limits the use of LUS, especially in remote areas. An automated point-of-care tool that could screen and capture LUS morphologies associated with neonatal lung pathologies could aid in rapid and accurate diagnosis. Methods We propose a framework for classifying the six most common neonatal lung pathologies using spatially localized line and texture patterns extracted via 2D dual-tree complex wavelet transform (DTCWT). We acquired 1550 LUS images from 42 neonates with varying numbers of lung pathologies. Furthermore, we balanced our data set to avoid bias towards a pathology class. Results Using DTCWT and clinical features as inputs to a linear discriminant analysis (LDA), our approach achieved a per-image cross-validated classification accuracy of 74.39% for the imbalanced data set. Our classification accuracy improved to 92.78% after balancing our data set. Moreover, our proposed framework achieved a maximum per-subject cross-validated classification accuracy of 64.97% with an imbalanced data set while using a balanced data set improves its classification accuracy up to 81.53%. Conclusion Our work could aid in automating the diagnosis of lung pathologies among neonates using LUS. Rapid and accurate diagnosis of lung pathologies could help to decrease neonatal deaths in healthcare institutions that lack well-trained clinicians, especially in developing countries.

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Intelligent classification of B-line and white lung from COVID-19 pneumonia ultrasound images using radiomics analysis

Cited by 3 publications

References 3 publications

Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic

Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic

Classification of Lung Pathologies in Neonates using Dual Tree Complex Wavelet Transform

Classification of lung pathologies in neonates using dual-tree complex wavelet transform

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