ArticleThe Journal of infection2015
An infectious disease/fever screening radar system which stratifies higher-risk patients within ten seconds using a neural network and the fuzzy grouping method.
Article in The Journal of infection, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 62 citations in OpenAlex.
- Harnessing AI for advancing pathogenic microbiology: a bibliometric and topic modeling approach.Frontiers in microbiology · 2024Pooled it
- Good Practices for Managing Acute Respiratory Viral Infections at Aerial Entry Points: A Scoping Review.Journal of epidemiology and global health · 2026Article
- Statistical Study on Human Temperature Measurement by Infrared Thermography.Sensors (Basel, Switzerland) · 2022Article
- Scaling up artificial intelligence to curb infectious diseases in Africa.Frontiers in digital health · 2022Article
- Artificial intelligence as a fundamental tool in management of infectious diseases and its current implementation in COVID-19 pandemic.Environmental science and pollution research international · 2021Review
- An Overview of Thermal Infrared Imaging-Based Screenings during Pandemic Emergencies.International journal of environmental research and public health · 2021Review
- Review of the efficacy of infrared thermography for screening infectious diseases with applications to COVID-19.Journal of medical imaging (Bellingham, Wash.) · 2021Review
- A Pneumonia Screening System based on Parasympathetic Activity Monitoring in Non-contact Way using Compact Radars Beneath the Bed Mattress.The Journal of infection · 2020Article
- Contactless Vital Signs Measurement System Using RGB-Thermal Image Sensors and Its Clinical Screening Test on Patients with Seasonal Influenza.Sensors (Basel, Switzerland) · 2020Article
- Development and Clinical Application of a Novel Non-contact Early Airflow Limitation Screening System Using an Infrared Time-of-Flight Depth Image Sensor.Frontiers in physiology · 2020Article
- Leveraging Computational Modeling to Understand Infectious Diseases.Current pathobiology reports · 2020Review
- A non-contact infection screening system using medical radar and Linux-embedded FPGA: Implementation and preliminary validation.Informatics in medicine unlocked · 2019Article
- Twenty-four-hour continuous and remote monitoring of respiratory rate using a medical radar system for the early detection of pneumonia in symptomatic elderly bedridden hospitalized patients.Clinical case reports · 2019Article
- Development and Clinical Application of a Novel Autonomic Transient Response-Based Screening System for Major Depressive Disorder Using a Fingertip Photoplethysmographic Sensor.Frontiers in bioengineering and biotechnology · 2018Article
- Remote sensing of multiple vital signs using a CMOS camera-equipped infrared thermography system and its clinical application in rapidly screening patients with suspected infectious diseases.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2017Article
- Smartwatch-based algorithm for early detection of pulmonary infection: Validation and performance evaluation.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo classify higher-risk influenza patients within 10 s, we developed an infectious disease and fever screening radar system.
methodsThe system screens infected patients based on vital signs, i.e., respiration rate measured by a radar, heart rate by a finger-tip photo-reflector, and facial temperature by a thermography. The system segregates subjects into higher-risk influenza (HR-I) group, lower-risk influenza (LR-I) group, and non-influenza (Non-I) group using a neural network and fuzzy clustering method (FCM). We conducted influenza screening for 35 seasonal influenza patients and 48 normal control subjects at the Japan Self-Defense Force Central Hospital. Pulse oximetry oxygen saturation (SpO2) was measured as a reference.
resultsThe system classified 17 subjects into HR-I group, 26 into LR-I group, and 40 into Non-I group. Ten out of the 17 HR-I subjects indicated SpO2 <96%, whereas only two out of the 26 LR-I subjects showed SpO2 <96%. The chi-squared test revealed a significant difference in the ratio of subjects showed SpO2 <96% between HR-I and LR-I group (p < 0.001). There were zero and nine normal control subjects in HR-I and LR-I groups, respectively, and there was one influenza patient in Non-I group.
conclusionsThe combination of neural network and FCM achieved efficient detection of higher-risk influenza patients who indicated SpO2 96% within 10 s.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.