Remote sensing technology
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:
Imaging of the retina, using deep-learning trained on data from 284,335 patients, may predict[8]:
- 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[9][10].
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.
- ↑ 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.
- ↑ 7.0 7.1 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