Remote sensing technology: Difference between revisions
Jump to navigation
Jump to search
No edit summary |
|||
| Line 8: | Line 8: | ||
Imaging may also be able to detect: | Imaging may also be able to detect: | ||
* Heart rate<ref name="LomalizaPark2019">{{cite journal|last1=Lomaliza|first1=Jean-Pierre|last2=Park|first2=Hanhoon|title=Improved Heart-Rate Measurement from Mobile Face Videos|journal=Electronics|volume=8|issue=6|year=2019|pages=663|issn=2079-9292|doi=10.3390/electronics8060663}}</ref><ref name="pmid29865289">{{cite journal| author=Gonzalez Viejo C, Fuentes S, Torrico DD, Dunshea FR| title=Non-Contact Heart Rate and Blood Pressure Estimations from Video Analysis and Machine Learning Modelling Applied to Food Sensory Responses: A Case Study for Chocolate. | journal=Sensors (Basel) | year= 2018 | volume= 18 | issue= 6 | pages= | pmid=29865289 | doi=10.3390/s18061802 | pmc=6022164 | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=29865289 }} </ref> | * Heart rate<ref name="LomalizaPark2019">{{cite journal|last1=Lomaliza|first1=Jean-Pierre|last2=Park|first2=Hanhoon|title=Improved Heart-Rate Measurement from Mobile Face Videos|journal=Electronics|volume=8|issue=6|year=2019|pages=663|issn=2079-9292|doi=10.3390/electronics8060663}}</ref><ref name="pmid29865289">{{cite journal| author=Gonzalez Viejo C, Fuentes S, Torrico DD, Dunshea FR| title=Non-Contact Heart Rate and Blood Pressure Estimations from Video Analysis and Machine Learning Modelling Applied to Food Sensory Responses: A Case Study for Chocolate. | journal=Sensors (Basel) | year= 2018 | volume= 18 | issue= 6 | pages= | pmid=29865289 | doi=10.3390/s18061802 | pmc=6022164 | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=29865289 }} </ref> | ||
* Respiratory rate<ref name="pmid28249595">{{cite journal| author=Wei B, He X, Zhang C, Wu X| title=Non-contact, synchronous dynamic measurement of respiratory rate and heart rate based on dual sensitive regions. | journal=Biomed Eng Online | year= 2017 | volume= 16 | issue= 1 | pages= 17 | pmid=28249595 | doi=10.1186/s12938-016-0300-0 | pmc=5439118 | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=28249595 }} </ref> | |||
* [[Ethanol intoxication]]<ref name="HermosillaVerdugo2018">{{cite journal|last1=Hermosilla|first1=Gabriel|last2=Verdugo|first2=José Luis|last3=Farias|first3=Gonzalo|last4=Vera|first4=Esteban|last5=Pizarro|first5=Francisco|last6=Machuca|first6=Margarita|title=Face Recognition and Drunk Classification Using Infrared Face Images|journal=Journal of Sensors|volume=2018|year=2018|pages=1–8|issn=1687-725X|doi=10.1155/2018/5813514}}</ref>. | * [[Ethanol intoxication]]<ref name="HermosillaVerdugo2018">{{cite journal|last1=Hermosilla|first1=Gabriel|last2=Verdugo|first2=José Luis|last3=Farias|first3=Gonzalo|last4=Vera|first4=Esteban|last5=Pizarro|first5=Francisco|last6=Machuca|first6=Margarita|title=Face Recognition and Drunk Classification Using Infrared Face Images|journal=Journal of Sensors|volume=2018|year=2018|pages=1–8|issn=1687-725X|doi=10.1155/2018/5813514}}</ref>. | ||
* [[Cushing's syndrome]]<ref name="KosilekFrohner2015">{{cite journal|last1=Kosilek|first1=R P|last2=Frohner|first2=R|last3=Würtz|first3=R P|last4=Berr|first4=C M|last5=Schopohl|first5=J|last6=Reincke|first6=M|last7=Schneider|first7=H J|title=Diagnostic use of facial image analysis software in endocrine and genetic disorders: review, current results and future perspectives|journal=European Journal of Endocrinology|volume=173|issue=4|year=2015|pages=M39–M44|issn=0804-4643|doi=10.1530/EJE-15-0429}}</ref> | * [[Cushing's syndrome]]<ref name="KosilekFrohner2015">{{cite journal|last1=Kosilek|first1=R P|last2=Frohner|first2=R|last3=Würtz|first3=R P|last4=Berr|first4=C M|last5=Schopohl|first5=J|last6=Reincke|first6=M|last7=Schneider|first7=H J|title=Diagnostic use of facial image analysis software in endocrine and genetic disorders: review, current results and future perspectives|journal=European Journal of Endocrinology|volume=173|issue=4|year=2015|pages=M39–M44|issn=0804-4643|doi=10.1530/EJE-15-0429}}</ref> | ||
Revision as of 15:26, 29 April 2020
In telemetry, Remote sensing technology is defined as the "observation and acquisition of physical data from a distance by viewing and making measurements from a distance or receiving transmitted data from observations made at distant location."[1]
Medical imaging
Transdermal optical imaging, using video from a smartphone camera and using advanced machine learning may[2][3] or may not[4] be able to determine a subject's blood pressure.
Imaging may also be able to detect:
- Heart rate[5][3]
- Respiratory rate[6]
- Ethanol intoxication[7].
- Cushing's syndrome[8]
- Acromegaly[8]
Imaging of the retina, using deep-learning trained on data from 284,335 patients, may predict[9]:
- age (mean absolute error within 3.26 years)
- gender (area under the receiver operating characteristic curve (AUC) = 0.97)
- smoking status (AUC = 0.71)
- systolic blood pressure (mean absolute error within 11.23 mmHg)
- major adverse cardiac events (AUC = 0.70)
Legal issues
Legal issues have been debated about the role of transparency and human oversight in interpreting information derived from deep learning[10][11].
See also
External links
References
- ↑ Anonymous (2025), Remote sensing technology (English). Medical Subject Headings. U.S. National Library of Medicine.
- ↑ Luo H, Yang D, Barszczyk A, Vempala N, Wei J, Wu SJ; et al. (2019). "Smartphone-Based Blood Pressure Measurement Using Transdermal Optical Imaging Technology". Circ Cardiovasc Imaging. 12 (8): e008857. doi:10.1161/CIRCIMAGING.119.008857. PMID 31382766.
- ↑ 3.0 3.1 Gonzalez Viejo C, Fuentes S, Torrico DD, Dunshea FR (2018). "Non-Contact Heart Rate and Blood Pressure Estimations from Video Analysis and Machine Learning Modelling Applied to Food Sensory Responses: A Case Study for Chocolate". Sensors (Basel). 18 (6). doi:10.3390/s18061802. PMC 6022164. PMID 29865289.
- ↑ Raichle CJ, Eckstein J, Lapaire O, Leonardi L, Brasier N, Vischer AS; et al. (2018). "Performance of a Blood Pressure Smartphone App in Pregnant Women: The iPARR Trial (iPhone App Compared With Standard RR Measurement)". Hypertension. 71 (6): 1164–1169. doi:10.1161/HYPERTENSIONAHA.117.10647. PMID 29632098.
- ↑ Lomaliza, Jean-Pierre; Park, Hanhoon (2019). "Improved Heart-Rate Measurement from Mobile Face Videos". Electronics. 8 (6): 663. doi:10.3390/electronics8060663. ISSN 2079-9292.
- ↑ Wei B, He X, Zhang C, Wu X (2017). "Non-contact, synchronous dynamic measurement of respiratory rate and heart rate based on dual sensitive regions". Biomed Eng Online. 16 (1): 17. doi:10.1186/s12938-016-0300-0. PMC 5439118. PMID 28249595.
- ↑ Hermosilla, Gabriel; Verdugo, José Luis; Farias, Gonzalo; Vera, Esteban; Pizarro, Francisco; Machuca, Margarita (2018). "Face Recognition and Drunk Classification Using Infrared Face Images". Journal of Sensors. 2018: 1–8. doi:10.1155/2018/5813514. ISSN 1687-725X.
- ↑ 8.0 8.1 Kosilek, R P; Frohner, R; Würtz, R P; Berr, C M; Schopohl, J; Reincke, M; Schneider, H J (2015). "Diagnostic use of facial image analysis software in endocrine and genetic disorders: review, current results and future perspectives". European Journal of Endocrinology. 173 (4): M39–M44. doi:10.1530/EJE-15-0429. ISSN 0804-4643.
- ↑ Poplin, Ryan; Varadarajan, Avinash V.; Blumer, Katy; Liu, Yun; McConnell, Michael V.; Corrado, Greg S.; Peng, Lily; Webster, Dale R. (2018). "Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning". Nature Biomedical Engineering. 2 (3): 158–164. doi:10.1038/s41551-018-0195-0. ISSN 2157-846X.
- ↑ American Medical Association (2018). AMA passes first policy recommendations on augmented intelligence. Available at https://www.ama-assn.org/press-center/press-releases/ama-passes-first-policy-recommendations-augmented-intelligence
- ↑ Euopean Commission (2020). White paper: On Artificial Intelligence - A European approach to excellence and trust. Available at https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf