Health and Technology最新文献

Purpose: Lighting systems which use visible light blended with antimicrobial 405-nm violet-blue light have recently been developed for safe continuous decontamination of occupied healthcare environments. This paper characterises the optical output and antibacterial efficacy of a low irradiance 405-nm light system designed for environmental decontamination applications, under controlled laboratory conditions.

Methods: In the current study, the irradiance output of a ceiling-mounted 405-nm light source was profiled within a 3×3×2 m (18 m³) test area; with values ranging from 0.001-2.016 mWcm^-2. To evaluate antibacterial efficacy of the light source for environmental surface decontamination, irradiance levels within this range (0.021-1 mWcm^-2) at various angular ($\Delta$ ϴ=0-51.3) and linear (∆s=1.6-2.56 m) displacements from the source were used to generate inactivation kinetics, using the model organism, Staphylococcus aureus. Additionally, twelve bacterial species were surface-seeded and light-exposed at a fixed displacement below the source (1.5 m; 0.5 mWcm^-2) to demonstrate broad-spectrum efficacy at heights typical of high touch surfaces within occupied settings.

Results: Results demonstrate that significant (P≤0.05) inactivation was successfully achieved at all irradiance values investigated, with spatial positioning from the source affecting inactivation, with greater times required for inactivation as irradiance decreased. Complete/near-complete (≥93.28%) inactivation of all bacteria was achieved following exposure to 0.5 mWcm^-2 within exposure times realistic of those utilised practically for whole-room decontamination (2-16 h).

Conclusion: This study provides fundamental evidence of the efficacy, and energy efficiency, of low irradiance 405-nm light for bacterial inactivation within a controlled laboratory setting, further justifying its benefits for practical infection control applications.

Background: For the adoption of machine learning clinical decision support systems (ML-CDSS) it is critical to understand the performance aid of the ML-CDSS. However, it is not trivial, how the performance aid should be evaluated. To design reliable performance evaluation study, both the knowledge from the practical framework of experimental study design and the understanding of domain specific design factors are required.

Objective: The aim of this review study was to form a practical framework and identify key design factors for experimental design in evaluating the performance of clinicians with or without the aid of ML-CDSS.

Methods: The study was based on published ML-CDSS performance evaluation studies. We systematically searched articles published between January 2016 and December 2022. From the articles we collected a set of design factors. Only the articles comparing the performance of clinicians with or without the aid of ML-CDSS using experimental study methods were considered.

Results: The identified key design factors for the practical framework of ML-CDSS experimental study design were performance measures, user interface, ground truth data and the selection of samples and participants. In addition, we identified the importance of randomization, crossover design and training and practice rounds. Previous studies had shortcomings in the rationale and documentation of choices regarding the number of participants and the duration of the experiment.

Conclusion: The design factors of ML-CDSS experimental study are interdependent and all factors must be considered in individual choices.

Supplementary information: The online version contains supplementary material available at 10.1007/s12553-023-00763-1.

Background: For monitoring, providing, and managing COVID-19 pandemic healthcare services, telemedicine holds incredible potential. During this period, there has been a change in the remote services offered to cancer patients. As a result, the purpose of this study was to conduct a mapping review to identify and classify telemedicine applications for providing cancer care to patients during the COVID-19 pandemic.

Methods: Articles published in scientific databases such as Web of Science, Scopus, PubMed, and ProQuest up to 2022 were searched for in this systematic mapping study. Identifying keywords, creating a search strategy, and selecting data sources were all part of our search for relevant articles. The articles were chosen in phases based on inclusion and exclusion criteria.

Results: A total of 1331 articles were found, with the majority of them (46% of them) taking place in the United States. Telemedicine systems were most commonly developed for breast cancer (11.4%), lung cancer (7.9%), head and neck cancer (6.4%), brain cancer (5.4%), gynecologic cancer (6.0%), urological cancer (5.7%), prostate cancer (5.0%), colorectal cancer (5.0%), biliary tract cancer (5.0%), and skin cancer (5.0%). Teleconsultation was the most common type of telemedicine application, with 60% of it taking place in real time.

Conclusion: Because of its emphasis on providing high-quality health care while reducing costs, telemedicine has gained popularity in the majority of countries, with positive economic and social consequences. While telemedicine systems provide a variety of healthcare services, during the COVID-19 era, they do not currently provide many services to all cancer patients worldwide.

Supplementary information: The online version contains supplementary material available at 10.1007/s12553-023-00762-2.

Purpose: There are 47 municipalities and prefectures in Japan that operate similar COVID-19 policies in a unified manner. There are significant differences regarding their policy outcomes. In order to investigate when the outcomes are different, we made a COVID-19 policy outcome analysis tool, jpcovid for evaluating time-series scores of individual prefectures, not a policy analysis tool.

Methods: Scoring policies is based on a single population mortality metric: the number of COVID-19 deaths divided by the population in millions from a demographic perspective.

Results: Although uniformed policies have been adopted by the 47 prefectures in Japan, there are significant differences in the calculated scores among the 47 prefectures. This difference can be caused by differences in the herding instincts of the community with COVID-19 variants. The herd instinct is an inherent tendency to associate with others and follow the group's behavior or a behavior wherein people tend to react to the actions of others without considering the reason. The snapshot scoring tool, jpscore showed that Niigata has the best score of 67.9 while Osaka has the worst score of 727.9. jpcovid allows users to identify when herd instincts made changes in time-series scores.

Conclusions: This is the world's first large-scale measurement on the herd instinct of prefectures in Japan. The proposed method can be applied to other countries in general.

Supplementary information: The online version contains supplementary material available at 10.1007/s12553-023-00759-x.

Purpose: The first transmission of coronavirus to humans started in Wuhan city of China, took the shape of a pandemic called Corona Virus Disease 2019 (COVID-19), and posed a principal threat to the entire world. The researchers are trying to inculcate artificial intelligence (Machine learning or deep learning models) for the efficient detection of COVID-19. This research explores all the existing machine learning (ML) or deep learning (DL) models, used for COVID-19 detection which may help the researcher to explore in different directions. The main purpose of this review article is to present a compact overview of the application of artificial intelligence to the research experts, helping them to explore the future scopes of improvement.

Methods: The researchers have used various machine learning, deep learning, and a combination of machine and deep learning models for extracting significant features and classifying various health conditions in COVID-19 patients. For this purpose, the researchers have utilized different image modalities such as CT-Scan, X-Ray, etc. This study has collected over 200 research papers from various repositories like Google Scholar, PubMed, Web of Science, etc. These research papers were passed through various levels of scrutiny and finally, 50 research articles were selected.

Results: In those listed articles, the ML / DL models showed an accuracy of 99% and above while performing the classification of COVID-19. This study has also presented various clinical applications of various research. This study specifies the importance of various machine and deep learning models in the field of medical diagnosis and research.

Conclusion: In conclusion, it is evident that ML/DL models have made significant progress in recent years, but there are still limitations that need to be addressed. Overfitting is one such limitation that can lead to incorrect predictions and overburdening of the models. The research community must continue to work towards finding ways to overcome these limitations and make machine and deep learning models even more effective and efficient. Through this ongoing research and development, we can expect even greater advances in the future.