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BACKGROUND AND OBJECTIVE: Obstructive airway diseases, including asthma and Chronic Obstructive Pulmonary Disease (COPD), are two of the most common chronic respiratory health problems. Both of these conditions require health professional expertise in making a diagnosis. Hence, this process is time intensive for healthcare providers and the diagnostic quality is subject to intra- and inter- operator variability. In this study we investigate the role of automated detection of obstructive airway diseases to reduce cost and improve diagnostic quality. METHODS: We investigated the existing body of evidence and applied Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search records in IEEE, Google scholar, and PubMed databases. We identified 65 papers that were published from 2013 to 2022 and these papers cover 67 different studies. The review process was structured according to the medical data that was used for disease detection. We identified six main categories, namely air flow, genetic, imaging, signals, and miscellaneous. For each of these categories, we report both disease detection methods and their performance. RESULTS: We found that medical imaging was used in 14 of the reviewed studies as data for automated obstructive airway disease detection. Genetics and physiological signals were used in 13 studies. Medical records and air flow were used in 9 and 7 studies, respectively. Most papers were published in 2020 and we found three times more work on Machine Learning (ML) when compared to Deep Learning (DL). Statistical analysis shows that DL techniques achieve higher Accuracy (ACC) when compared to ML. Convolutional Neural Network (CNN) is the most common DL classifier and Support Vector Machine (SVM) is the most widely used ML classifier. During our review, we discovered only two publicly available asthma and COPD datasets. Most studies used private clinical datasets, so data size and data composition are inconsistent. CONCLUSIONS: Our review results indicate that Artificial Intelligence (AI) can improve both decision quality and efficiency of health professionals during COPD and asthma diagnosis. However, we found several limitations in this review, such as a lack of dataset consistency, a limited dataset and remote monitoring was not sufficiently explored. We appeal to society to accept and trust computer aided airflow obstructive diseases diagnosis and we encourage health professionals to work closely with AI scientists to promote automated detection in clinical practice and hospital settings.

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Due to lack of robust data on childhood cystic fibrosis (CF) in Bangladesh we sought to evaluate their clinico-epidemiology. A cross-sectional observation was conducted adopting CF-foundation consensus-panel-diagnostic criteria in 3 tertiary-care-hospitals in Bangladesh from 2000 to 2017. Clinically suspected 95 CF-cases were subjected to sweat-chloride testing using locally-developed a fast, cheap and effective indigenously body-wrapped sweating technique measured by US-Easy Lyte-automated microprocessor-controlled analyzer marking ≥60 mmol/L as positive. Mean-age of CF-cases at disease-onset was 16.9 ± 26.6 months that significantly differed with age-at-diagnosis (P < .02). Pulmonary syndromes included chronic wet cough in 100%, respiratory distress in 90.5%, digital-clubbing in 78%, mucopurulent-sputum in 74%-cases, and crepitation in 82%. Radio-imaging revealed bronchiectasis in 60%, hyperinflation/peribronchial-thickening in 22% and, pan-sinusitis in 89%-cases. While 37% had history-of malabsorption, high-fecal-fat revealed in 53%-cases. Malnutrition prevailed as severe-underweight in 87%-cases and all CF-cases (100%) had high sweat-chloride (mean = 118 ± 53.34 mmol/L). Thus, children with pulmonary features coupled with severe malnutrition and associated radio-imaging bronchiectasis should be screened for CF with a fast, cheap and effective sweat test in resource poor settings.

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Diarrhea is the second leading cause of death among children less than 5 years of age. Most of these deaths occur in developing countries in the tropical areas of Africa and South Asia. Goreisan/Wulingsan, a formula of Japanese-Chinese medicinal herbs (Kampo), has been used for the treatment of diarrhea and vomiting from ancient times in East Asia. Therefore, we planned a randomized controlled clinical trial of Goreisan/Wulingsan in Bangladeshi children. Although it is believed to be safe in East Asia, information regarding its toxicity on animals is scarce. Since Goreisan/Wulingsan has never been used in Bangladesh, it was necessary to ensure the safety of the formula in an animal experiment. Rats were assigned to a control group (normal saline, n = 4) or various Goreisan/Wulingsan groups (n = 26) receiving doses of 1 to 8 mg/g/day (7.7 to 61.5 times the recommended pediatric dose) over a period of 25 days. Their activities and health conditions were observed until they were sacrificed, after which blood samples were collected for biochemical liver function tests. The kidneys, liver and heart tissue were collected for histopathological study. No lethality was observed during the experiment. All of the rats consumed the doses completely and no constipation was observed, suggesting the absence of any inhibitory effect on intestinal motion. Also, no abnormal neurological activity was detected, nor any significant elevation of AST, ALT or ALP levels, except for AST and ALT at the highest dose of 8 mg/g/day. Histopathological studies of the kidneys, liver and heart tissues revealed no abnormalities. In conclusion, our results showed that Goreisan/Wulingsan is safe for rats, thereby justifying the use of the drug in a human trial.