The coronavirus disease 2019 pandemic has underscored the limitations of traditional diagnostic methods, particularly in ensuring the safety of healthcare workers and patients during infectious outbreaks. Smartphone-based digital stethoscopes enhanced with artificial intelligence (AI) have emerged as potential tools for addressing these challenges by enabling remote, efficient, and accessible auscultation. Despite advancements, most existing systems depend on additional hardware and external processing, increasing costs and complicating deployment. This review examines the feasibility and limitations of smartphone-based digital stethoscopes powered by AI, focusing on their ability to perform real-time analyses of audible and inaudible sound frequencies. We also explore the regulatory barriers, data storage challenges, and diagnostic accuracy issues that must be addressed to facilitate broader adoption. The implementation of these devices in veterinary medicine is discussed as a practical step toward refining their applications. With targeted improvements and careful consideration of existing limitations, smartphone-based AI stethoscopes could enhance diagnostic capabilities in human and animal healthcare settings.

The coronavirus disease 2019 pandemic has underscored the limitations of traditional diagnostic methods, particularly in ensuring the safety of healthcare workers and patients during infectious outbreaks. Smartphone-based digital stethoscopes enhanced with artificial intelligence (AI) have emerged as potential tools for addressing these challenges by enabling remote, efficient, and accessible auscultation. Despite advancements, most existing systems depend on additional hardware and external processing, increasing costs and complicating deployment. This review examines the feasibility and limitations of smartphone-based digital stethoscopes powered by AI, focusing on their ability to perform real-time analyses of audible and inaudible sound frequencies. We also explore the regulatory barriers, data storage challenges, and diagnostic accuracy issues that must be addressed to facilitate broader adoption. The implementation of these devices in veterinary medicine is discussed as a practical step toward refining their applications. With targeted improvements and careful consideration of existing limitations, smartphone-based AI stethoscopes could enhance diagnostic capabilities in human and animal healthcare settings.

The coronavirus disease 2019 pandemic has underscored the limitations of traditional diagnostic methods, particularly in ensuring the safety of healthcare workers and patients during infectious outbreaks. Smartphone-based digital stethoscopes enhanced with artificial intelligence (AI) have emerged as potential tools for addressing these challenges by enabling remote, efficient, and accessible auscultation. Despite advancements, most existing systems depend on additional hardware and external processing, increasing costs and complicating deployment. This review examines the feasibility and limitations of smartphone-based digital stethoscopes powered by AI, focusing on their ability to perform real-time analyses of audible and inaudible sound frequencies. We also explore the regulatory barriers, data storage challenges, and diagnostic accuracy issues that must be addressed to facilitate broader adoption. The implementation of these devices in veterinary medicine is discussed as a practical step toward refining their applications. With targeted improvements and careful consideration of existing limitations, smartphone-based AI stethoscopes could enhance diagnostic capabilities in human and animal healthcare settings.

The coronavirus disease 2019 pandemic has underscored the limitations of traditional diagnostic methods, particularly in ensuring the safety of healthcare workers and patients during infectious outbreaks. Smartphone-based digital stethoscopes enhanced with artificial intelligence (AI) have emerged as potential tools for addressing these challenges by enabling remote, efficient, and accessible auscultation. Despite advancements, most existing systems depend on additional hardware and external processing, increasing costs and complicating deployment. This review examines the feasibility and limitations of smartphone-based digital stethoscopes powered by AI, focusing on their ability to perform real-time analyses of audible and inaudible sound frequencies. We also explore the regulatory barriers, data storage challenges, and diagnostic accuracy issues that must be addressed to facilitate broader adoption. The implementation of these devices in veterinary medicine is discussed as a practical step toward refining their applications. With targeted improvements and careful consideration of existing limitations, smartphone-based AI stethoscopes could enhance diagnostic capabilities in human and animal healthcare settings.

PURPOSE: To evaluate the effects of air purifiers on the concentrations of indoor air pollutants and on asthma control in children.MATERIALS AND METHODS: In this randomized crossover trial, daily use of an air purifier filter was compared with a matched placebo with the filter off. Thirty elementary school students who had asthma were enrolled and randomly allocated to one of two groups. The primary endpoints were changes in indoor air quality, asthma severity, lung function, airway inflammatory, urine microbiome, and phthalate after the installation of air purifiers. PM(2.5) and CO₂ were measured as indoor air pollutants. Asthma severity was assessed in terms of both symptom and medication scores acquired using a daily questionnaire. The higher the score, the better the symptom or the less frequent the use of medication. Peak expiratory flow rate and fractional exhaled nitric oxide were also measured.RESULTS: The mean age of the enrolled patients was 9.2±1.98 years. The mean concentration of PM2.5 was 17.0 µg/m³ in the filter-off condition, but significantly lower at 9.26 µg/m³ in the filter-on condition. Medication scores were 6.9 for the filter-off and 7.12 for the filter-on conditions, reflecting a statistically significant decrease in the frequency of medications used during air purifier operation. Bacterial richness, as determined using the Chao 1 index, was markedly lower in the filter-on than the filter-off condition.CONCLUSION: This study suggests that air purifiers benefit medication burden in children with asthma by reducing PM(2.5) levels.

Purpose: Patients undergoing radiation therapy (RT) often experience psychological anxiety that manifests as muscle contraction. Our study explored psychological anxiety in these patients by using biological signals recorded using a smartwatch. Materials and Methods: Informed consent was obtained from participating patients prior to the initiation of RT. The patients wore a smartwatch from the waiting room until the conclusion of the treatment. The smartwatch acquired data related to heart rate features (average, minimum, and maximum) and stress score features (average, minimum, and maximum). On the first day of treatment, we analyzed the participants' heart rates and stress scores before and during the treatment. The acquired data were categorized according to sex and age. For patients with more than three days of data, we observed trends in heart rate during treatment relative to heart rate before treatment (HRtb) over the course of treatment. Statistical analyses were performed using the Wilcoxon signed-rank test and paired t-test. Results: Twenty-nine individuals participated in the study, of which 17 had more than 3 days of data. During treatment, all patients exhibited elevated heart rates and stress scores, particularly those in the younger groups. The HRtb levels decreased as treatment progresses. Conclusion Patients undergoing RT experience notable psychological anxiety, which tends to diminish as the treatment progresses. Early stage interventions are crucial to alleviate patient anxiety during RT.

We attempted to investigate the correlation between the severity of atopic dermatitis (AD) in children and the indoor level of house dust mite (HDM) allergens. Ninety-five patients (31.1 +/- 19.5 months of age) with AD were enrolled in this study, and serum specific IgE against Dermatophagoides pteronyssinus and D. farinae was measured. The severity of AD was assessed using the visual analogue scale on the same day of house dust collection. Living rooms and mattresses where the child usually slept were vacuumed for 2 minutes and concentrations of Der f 1 were measured by enzyme-linked immunosorbent assay. The skin symptoms were more severe in patients with Der f 1 concentrations in living room > 2 microg/g dust than < or = 2 microg/g dust (P = 0.018). This difference was noted in AD patients without sensitization to HDM (P = 0.004), but not in patients with sensitization. There was no difference in symptom severity according to Der f 1 concentrations in mattresses (P = 0.062). The severity of skin symptoms is associated with indoor concentrations of HDM in children with AD, and it is likely to act as nonspecific irritants as well as allergens in AD skin lesions.
Adolescent ; Animals ; Antigens, Dermatophagoides/*analysis ; Beds/parasitology ; Child ; Child, Preschool ; Dermatitis, Atopic/*diagnosis/pathology ; Dermatophagoides farinae/immunology ; Dermatophagoides pteronyssinus/immunology ; *Enzyme-Linked Immunosorbent Assay ; Female ; Humans ; Immunoglobulin E/blood ; Infant ; Male ; Severity of Illness Index