Deep learning facilitates the diagnosis of adult asthma

Tomita, Katsuyuki; Nagao, Ryo; Touge, Hirokazu; Ikeuchi, Tomoyuki; Sano, Hiroyuki; Yamasaki, Akira; Tohda, Yuji

doi:10.1016/j.alit.2019.04.010

Cited by 47 publications

(24 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recently, several classical ML algorithms, such as logistic regression analysis, support vector machine, and deep neural network, were compared for their diagnostic ability when based only on symptoms and physical signs, or when based on the combination of symptoms, physical signs, biochemical findings, lung function tests, and the bronchial provocation tests. That study included 566 adult outpatients and indicated that the deep neural network model was more accurate than other conventional ML tools, reaching an accuracy of 98% when symptoms, physical signs and objective tests were also used 35 . This study may be the first to report that AI can perform comparably to human experts for diagnosing asthma in adults.…”

Section: Ai/ml and Asthmamentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Feng¹,

Wang²,

Zeng³

et al. 2021

Int. J. Med. Sci.

View full text Add to dashboard Cite

Chronic airway diseases are characterized by airway inflammation, obstruction, and remodeling and show high prevalence, especially in developing countries. Among them, asthma and chronic obstructive pulmonary disease (COPD) show the highest morbidity and socioeconomic burden worldwide. Although there are extensive guidelines for the prevention, early diagnosis, and rational treatment of these lifelong diseases, their value in precision medicine is very limited. Artificial intelligence (AI) and machine learning (ML) techniques have emerged as effective methods for mining and integrating large-scale, heterogeneous medical data for clinical practice, and several AI and ML methods have recently been applied to asthma and COPD. However, very few methods have significantly contributed to clinical practice. Here, we review four aspects of AI and ML implementation in asthma and COPD to summarize existing knowledge and indicate future steps required for the safe and effective application of AI and ML tools by clinicians.

show abstract

Section: Ai/ml and Asthmamentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Feng¹,

Wang²,

Zeng³

et al. 2021

Int. J. Med. Sci.

View full text Add to dashboard Cite

show abstract

“…Still, it is not expected due to the long parameter setting steps, many architectures of neural networks to choose from, and the large number of algorithms used to train ANN. That is why DNN is a suitable tool to diagnose asthma [44]. Chronic obstructive pulmonary disease (COPD) is a diverse disease categorized by the progression of multiple subtypes and diseases.…”

Section: Respiratory Systemmentioning

confidence: 99%

Healthcare Techniques Through Deep Learning: Issues, Challenges and Opportunities

et al. 2021

View full text Add to dashboard Cite

In artificial intelligence, deep learning (DL) is a process that replicates the working mechanism of the human brain in data processing, and it also creates patterns for decision making. Deep learning or neural networks have been deployed in several fields, such as computer vision, natural language processing, and speech recognition. It has been used in many healthcare applications for the diagnosis and treatment of many chronic diseases. These algorithms have the power to avoid outbreaks of illness, recognize and diagnose illnesses, minimize running expenses for hospital management and patients. This paper discusses the deep learning methods used in different healthcare fields, i.e., identifying depression, heart diseases, physiological signals, lymph node metastases from breast cancer, etc. These diseases are categorized into the central nervous system, cardiovascular system, and respiratory system. For each category, after summarizing the studies, comparison tables are laid down using some important factors. Different applications, tools, methods, and data sets used for DL models are leveraged. Finally, research opportunities and challenges being faced for deep learning models are discussed.

show abstract

“…Three classifiers namely deep neural network (DNN), logistic model and support vector machine (SVM) were proposed [7] [12]for the diagnosis of adult asthma and lung cancer. The performance metrics of the three classifiers were compared.…”

Section: Introductionmentioning

confidence: 99%

Customized Convolution Neural Network for Multi-Class Lung Abnormality Classification From Ct Images

Lakshmi¹,

Sivakumar²,

Krishnan³

et al. 2021

ITII

View full text Add to dashboard Cite

Automated detection of lung abnormalities has a significant role in the computer aided diagnosis of lung diseases. Recently, medical image analysis utilizes Convolution Neural Network(CNN) to improve the outcome of clinical diagnosis. In this paper, we propose customized CNN based multi-class lung abnormality classifier from CT images. The custom CNN is trained and tested using CT images showing lung abnormalities of Carcinoma, Fibrosis, Necrosis and their performance is also compared with the results using VGG16 and VGG19. It is found that the our Custom CNN shows good results for Carcinoma, Fibrosis, Healthy, Inflammation and Necrosis with classification accuracy of 0.912 compared to VGG16 and VGG19 with accuracy of 0.7435 and 0.7216 respectively. Hence, it is proven that our custom CNN can be utilized as a second opinion to radiologist expert and improving mortality rate of these lung diseases by providing class-specific treatment for the patients.

show abstract

Deep learning facilitates the diagnosis of adult asthma

Cited by 47 publications

References 14 publications

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Healthcare Techniques Through Deep Learning: Issues, Challenges and Opportunities

Customized Convolution Neural Network for Multi-Class Lung Abnormality Classification From Ct Images

Contact Info

Product

Resources

About