Artificial intellengence (AI) and Asthma

Abstract

<bold>Aims and objective:</bold> 1. To study the role of AI in early diagnosis of asthma exacerbation 2. To study the cost effectiveness of AI <bold>Material and methods:</bold> Sample size: 100 <bold>Inclusion criteria:</bold> 1. Patients age 18 years and above 2. Patients diagnosed with asthma and on regular follow-up 3. Patients with basic knowledge of using smartphones 4. Patients willing to share real time location 5. Patients consenting for the study <bold>Exclusion criteria:</bold> 1. Patients in acute exacerbation of asthma 2. Patients with active tuberculosis and on anti-tuberculosis therapy 3. Patients with infective respiratory diseases 4. Patients unable to consent <bold>Methodology:</bold> Patients fulfilling the inclusion criteria were included in the study. Patients were asked to do peak expiratory flow (PEFR) measure twice a day using peak expiratory flow meter and share this information along with their location data. This data along with the climate condition at the shared locality was analysed using Artificial Neural Network (ANN). Duration of the study was 6 months. <bold>Results:</bold> 1. Our study was male predominated with 60 males and 40 females 2. Most common age group in our study was 26±6 (p≤0.01) 3. 40 patients were advised to consult the healthcare facility and seek immediate care during the 6 months thereby avoiding the risk of exacerbation 4. 50 patients were advised change in inhaler routine as per the data 5. 5 patients were required hospitalization for exacerbation during study period 6. 1 patient loss to follow-up during study period 7. Overall reduction in exacerbation was around 60% thereby leading to less hospital visits and significantly reduced cost of healthcare delivery References: 1. ASTHMA IN DOMESTIC HOUSEHOLD WORKERS. PATIL, SARANG CHEST, Volume 166, Issue 4, A73 - A74 DOI: 10.1016/j.chest.2024.06.064

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