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Development of a Novel Vital-Signs-Based Infection Screening Composite-Type Camera With Truncus Motion Removal Algorithm to Detect COVID-19 Within 10 Seconds and Its Clinical Validation.
Unursaikhan, Batbayar; Amarsanaa, Gereltuya; Sun, Guanghao; Hashimoto, Kenichi; Purevsuren, Otgonbat; Choimaa, Lodoiravsal; Matsui, Takemi.
  • Unursaikhan B; Graduate School of Systems Design, Tokyo Metropolitan University, Hachioji, Japan.
  • Amarsanaa G; Machine Intelligence Laboratory, School of Engineering and Applied Sciences, National University of Mongolia, Ulaanbaatar, Mongolia.
  • Sun G; The First Central Hospital of Mongolia, Ulaanbaatar, Mongolia.
  • Hashimoto K; Graduate School of Informatics and Engineering, The University of Electro-Communications, Chofu, Japan.
  • Purevsuren O; Department of General Medicine, National Defense Medical College, Tokorozawa, Japan.
  • Choimaa L; Machine Intelligence Laboratory, School of Engineering and Applied Sciences, National University of Mongolia, Ulaanbaatar, Mongolia.
  • Matsui T; Machine Intelligence Laboratory, School of Engineering and Applied Sciences, National University of Mongolia, Ulaanbaatar, Mongolia.
Front Physiol ; 13: 905931, 2022.
Article in English | MEDLINE | ID: covidwho-1933746
ABSTRACT

Background:

To conduct a rapid preliminary COVID-19 screening prior to polymerase chain reaction (PCR) test under clinical settings, including patient's body moving conditions in a non-contact manner, we developed a mobile and vital-signs-based infection screening composite-type camera (VISC-Camera) with truncus motion removal algorithm (TMRA) to screen for possibly infected patients.

Methods:

The VISC-Camera incorporates a stereo depth camera for respiratory rate (RR) determination, a red-green-blue (RGB) camera for heart rate (HR) estimation, and a thermal camera for body temperature (BT) measurement. In addition to the body motion removal algorithm based on the region of interest (ROI) tracking for RR, HR, and BT determination, we adopted TMRA for RR estimation. TMRA is a reduction algorithm of RR count error induced by truncus non-respiratory front-back motion measured using depth-camera-determined neck movement. The VISC-Camera is designed for mobile use and is compact (22 cm × 14 cm × 4 cm), light (800 g), and can be used in continuous operation for over 100 patients with a single battery charge. The VISC-Camera discriminates infected patients from healthy people using a logistic regression algorithm using RR, HR, and BT as explanatory variables. Results are available within 10 s, including imaging and processing time. Clinical testing was conducted on 154 PCR positive COVID-19 inpatients (aged 18-81 years; M/F = 87/67) within the initial 48 h of hospitalization at the First Central Hospital of Mongolia and 147 healthy volunteers (aged 18-85 years, M/F = 70/77). All patients were on treatment with antivirals and had body temperatures <37.5°C. RR measured by visual counting, pulsimeter-determined HR, and BT determined by thermometer were used for references.

Result:

10-fold cross-validation revealed 91% sensitivity and 90% specificity with an area under receiver operating characteristic curve of 0.97. The VISC-Camera-determined HR, RR, and BT correlated significantly with those measured using references (RR r = 0.93, p < 0.001; HR r = 0.97, p < 0.001; BT r = 0.72, p < 0.001).

Conclusion:

Under clinical settings with body motion, the VISC-Camera with TMRA appears promising for the preliminary screening of potential COVID-19 infection for afebrile patients with the possibility of misdiagnosis as asymptomatic.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Prognostic study / Randomized controlled trials Language: English Journal: Front Physiol Year: 2022 Document Type: Article Affiliation country: Fphys.2022.905931

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Prognostic study / Randomized controlled trials Language: English Journal: Front Physiol Year: 2022 Document Type: Article Affiliation country: Fphys.2022.905931