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Attention-guided multiple instance learning for COPD identification: To combine the intensity and morphology

Wu Yanan, Qi Shouliang, Feng Jie, Chang Runsheng, Pang Haowen, Hou Jie, Li Mengqi, Wang Yingxi, Xia Shuyue, Qian Wei

Biocybernetics and Biomedical Engineering

2023

Vol. 43, no. 3

568--585

Chronic obstructive pulmonary disease (COPD) is a complex and multi-component respiratory disease. Computed tomography (CT) images can characterize lesions in COPD patients, but the image intensity and morphology of lung components have not been fully exploited. Two datasets (Dataset 1 and 2) comprising a total of 561 subjects were obtained from two centers. A multiple instance learning (MIL) method is proposed for COPD identification. First, randomly selected slices (instances) from CT scans and multi-view 2D snapshots of the 3D airway tree and lung field extracted from CT images are acquired. Then, three attention-guided MIL models (slice-CT, snapshot-airway, and snapshot-lung-field models) are trained. In these models, a deep convolution neural network (CNN) is utilized for feature extraction. Finally, the outputs of the above three MIL models are combined using logistic regression to produce the final prediction. For Dataset 1, the accuracy of the slice-CT MIL model with 20 instances was 88.1%. The backbone of VGG-16 outperformed Alexnet, Resnet18, Resnet26, and Mobilenet_v2 in feature extraction. The snapshotairway and snapshot-lung-field MIL models achieved accuracies of 89.4% and 90.0%, respectively. After the three models were combined, the accuracy reached 95.8%. The proposed model outperformed several state-of-the-art methods and afforded an accuracy of 83.1% for the external dataset (Dataset 2). The proposed weakly supervised MIL method is feasible for COPD identification. The effective CNN module and attention-guided MIL pooling module contribute to performance enhancement. The morphology information of the airway and lung field is beneficial for identifying COPD.

Nanomedicine - a boon for respiratory disease management

Balasubramanian D., Lavanya S.J., Girigoswami A., Girigoswami K.

Archives of Materials Science and Engineering

2023

Vol. 119, nr 2

71--85

Purpose: Respiratory diseases affect the lungs and other parts of the respiratory system. The respiratory disease affects hundreds of millions of humans, and premature death is observed in nearly four million people yearly. The major cause of the increase in this disease is the increased level of air pollution and higher tobacco usage in public places. Design/methodology/approach: We have used the search engines PubMed and Google Scholar for the keywords Respiratory diseases, Nanomaterials, diagnosis, Nanomedicine, and Target drug delivery; recent and relevant articles are selected for reviewing this paper. Findings: Nanomedicine is a recent field of research that deals with monitoring, repairing, theragnosis, and development of human biological systems at the sub-atomic level, where we utilize engineered nanodevices and nanostructures. The conventional therapeutic strategies designed for respiratory diseases have limited solubility and bioavailability. Moreover, the robust effect of the drugs led to adverse side effects due to their high dose requirement. The local delivery of therapeutic Nanoparticles (NPs) or drug-loaded nano vehicles to the lung is a safe technique for managing various respiratory tract-related diseases like chronic obstructive pulmonary diseases, cystic fibrosis, lung cancer, tuberculosis, asthma, and infection. To overcome the difficulties of conventional treatment with antibiotics and anti-inflammatory drugs, nano-enabled drug delivery, nanoformulations of drugs as well as drug nanoencapsulation have been used recently. In this mini-review, we will discuss the importance and application of nanomedicine for diagnosis, treatment and clinical research involved in the different types of respiratory diseases. Practical implications: Nanomedicine provides an alternative delivery of drugs with the help of various nanocarriers, which enhances controlled drug delivery at the pulmonary region and can be used for treating and diagnosing respiratory diseases in vivo and in vitro studies. Further experiments followed by clinical examination are warranted to prove the potential application of nanomedicine in treating respiratory disease. Originality/value: This mini-review will help the readers and budding scientists apply new methods for developing highly efficient drugs with low side effects and improved targeted sites of action.

Respiratory sound denoising using sparsity-assisted signal smoothing algorithm

Haider Nishi Shahnaj, Behera Ajoy K.

Biocybernetics and Biomedical Engineering

2022

Vol. 42, no. 2

481--493

Noises are the unavoidable entities which stands as a big barrier in the field of computerized lung sound (LS) based disease diagnosis, as it impairs the quality of LS and therefore greatly misleads the clinical interpretations done based on that. It has numerous sources, of which a few of them could be the noises due to body sounds, environmental noises, power line noises and recording artifacts, which easily contaminates the LS recordings. This paper presents a novel denoising algorithm to eliminate the noises from LS recordings in a more powerful way using Butterworth band-pass filter and sparsity assisted signal smoothing (SASS) algorithm. This study is carried out over LS captured from 80 Chronic Obstructive Pulmonary Disease (COPD), 80 pneumonia and 80 healthy participants in a clinical environment. Each of the recorded LS is denoised using Butterworth band-pass filter and sparse-assisted signal smoothing algorithm. The denoising performance of the proposed algorithm is evaluated on the basis of denoising performance parameters. As per the evaluation of the denoising performance parameters, it is observed that the proposed denoising method suppressed the LS noises with the signal to noise ratio (SNR) of 66.8 dB and with the peak signal to noise ratio (PSNR) of 78.5 dB. The proposed endeavour can be recommended for clinical use for producing noise free LSs to bring effective interpretations. The future endeavours involve the suppression of the heart sound noises from the LS recordings.