AI-Empowered Lung Disease Classification and Detection: Advances and Challenges

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Medicine".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 2196

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Computational learning theory team, RIKEN-Advanced Intelligence Center, Fukuoka 819-0395 Japan
Interests: wireless communications; machine learning; online learning; 5G, B5G, and 6G systems; image processing; millimeter waves; RIS systems; the Internet of things
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Special Issue Information

Dear Colleagues,

Lung diseases, such as lung cancer, COVID-19, chronic obstructive pulmonary disease (COPD), and pneumonia, are among the leading causes of death worldwide. Early detection and accurate classification of lung diseases are crucial for timely and effective treatment. Artificial intelligence (AI) has shown great potential in the field of medical imaging and diagnostics, particularly in the detection and classification of lung diseases. However, there are still many challenges that need to be addressed to fully realize the potential of AI in this field. The main objectives of this Special Issue are to: review the latest advances in AI-empowered lung disease classification and detection, including deep learning, machine learning, and computer vision techniques; identify the main challenges and limitations in the field and discuss potential solutions; provide a comprehensive overview of the current state of the art in AI-empowered lung disease classification and detection; and discuss the ethical and regulatory issues related to the use of AI in medical imaging and diagnostics. Hence, this Special Issue aims to provide a comprehensive overview of the current state of the art in AI-empowered lung disease classification and detection and to discuss the challenges and limitations in the field. By bringing together experts from various fields, including computer science, medical imaging, and medicine, we hope to foster new collaborations and accelerate the development of AI-based diagnostic tools for lung diseases.

Dr. Sherief Hashima
Guest Editor

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Keywords

  • AI
  • deep learning
  • lung disease
  • COVID-19
  • COPD

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Published Papers (1 paper)

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Research

13 pages, 271 KiB  
Article
COPD Patients’ Behaviour When Involved in the Choice of Inhaler Device
by Sorin Bivolaru, Ancuta Constantin, Constantin Marinel Vlase and Cristian Gutu
Healthcare 2023, 11(11), 1606; https://doi.org/10.3390/healthcare11111606 - 30 May 2023
Cited by 4 | Viewed by 1681
Abstract
Background: Inhaler therapy plays a crucial role in controlling respiratory symptoms in patients with chronic obstructive pulmonary disease (COPD). Incorrect or partially correct use of inhaler devices causes many chronic obstructive pulmonary disease (COPD) patients to continue to have respiratory symptoms due to [...] Read more.
Background: Inhaler therapy plays a crucial role in controlling respiratory symptoms in patients with chronic obstructive pulmonary disease (COPD). Incorrect or partially correct use of inhaler devices causes many chronic obstructive pulmonary disease (COPD) patients to continue to have respiratory symptoms due to poor drug deposition in the airways as a result of poor inhaler technique, leading to increased healthcare costs due to exacerbations and multiple emergency room presentations. Choosing the right inhaler device for each individual patient is a bigger challenge for doctors and chronic obstructive pulmonary disease (COPD) patients. The type of inhaler device and the correct inhaler technique depends on the control of symptoms in chronic obstructive pulmonary disease (COPD). Physicians treating patients with chronic obstructive pulmonary disease (COPD) play a central role in educating patients about the correct use of inhalation devices. The steps for the correct use of inhalation devices should be taught to patients by doctors in the presence of the family so that if the patient has difficulties handling the device correctly, the family can support them. Methods: Our analysis included 200 subjects divided into two groups—recommended group (RG) and chosen group (CG)—and aimed primarily to identify the behaviour of chronic obstructive pulmonary disease (COPD) patients when faced with deciding which type of inhaler device is most suitable for them. The two groups were monitored three times during the 12-month follow-up period. Monitoring required the physical presence of the patient at the investigating physician’s office. The study included patients who were smokers, ex-smokers, and/or with significant exposure to occupational pollutants, aged over 40 years diagnosed with chronic obstructive pulmonary disease (COPD), risk group B and C according to the GOLD guideline staging, and on inhaled ICS+LABA treatment, although they had an indication for LAMA+LABA dual bronchodilation treatment. Patients presented for consultation on their own initiative for residual respiratory symptoms under background treatment with ICS+LABA. The investigating pulmonologist who offered consultations to all scheduled patients, on the occasion of the consultation, also checked the inclusion and exclusion criteria. If the patient did not meet the study entry criteria, they were assessed and received the appropriate treatment, and if the study entry criteria were met, the patient signed the consent and followed the steps recommended by the investigating pulmonologist. As a result, patient entry into the study was randomised 1:1, meaning that the first patient was recommended the inhaler device by the doctor and the next patient entered into the study was left to decide for themselves which type of device was most suitable for them. In both groups, the percentage of patients who had a different choice of inhaler device from that of their doctor was statistically significant. Results: Compliance to treatment at T12 was found to be low, but compared to results previously published on compliance, in our analysis, compliance was higher and the only reasons identified as responsible for the better results were related to the selection of the target groups and the regular assessments, where, in addition to reviewing the inhaler technique, patients were encouraged to continue treatment, thus creating a strong bond between patient and doctor. Conclusions: Our analysis revealed that empowering patients by involving them in the inhaler selection process increases adherence to inhaler treatment, reduces the number of mistakes in inhaler use of the inhaler device, and implicitly the number of exacerbations. Full article
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