Remote sensing technology: Difference between revisions
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* [[Acromegaly]]<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> | * [[Acromegaly]]<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> | ||
==Retina== | |||
Imaging of the retina, using deep-learning trained on data from 284,335 patients, may predict<ref name="PoplinVaradarajan2018">{{cite journal|last1=Poplin|first1=Ryan|last2=Varadarajan|first2=Avinash V.|last3=Blumer|first3=Katy|last4=Liu|first4=Yun|last5=McConnell|first5=Michael V.|last6=Corrado|first6=Greg S.|last7=Peng|first7=Lily|last8=Webster|first8=Dale R.|title=Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning|journal=Nature Biomedical Engineering|volume=2|issue=3|year=2018|pages=158–164|issn=2157-846X|doi=10.1038/s41551-018-0195-0}}</ref>: | Imaging of the retina, using deep-learning trained on data from 284,335 patients, may predict<ref name="PoplinVaradarajan2018">{{cite journal|last1=Poplin|first1=Ryan|last2=Varadarajan|first2=Avinash V.|last3=Blumer|first3=Katy|last4=Liu|first4=Yun|last5=McConnell|first5=Michael V.|last6=Corrado|first6=Greg S.|last7=Peng|first7=Lily|last8=Webster|first8=Dale R.|title=Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning|journal=Nature Biomedical Engineering|volume=2|issue=3|year=2018|pages=158–164|issn=2157-846X|doi=10.1038/s41551-018-0195-0}}</ref>: | ||
* age (mean absolute error within 3.26 years) | * age (mean absolute error within 3.26 years) | ||
| Line 20: | Line 21: | ||
* systolic blood pressure (mean absolute error within 11.23 mmHg) | * systolic blood pressure (mean absolute error within 11.23 mmHg) | ||
* major adverse cardiac events (AUC = 0.70) | * major adverse cardiac events (AUC = 0.70) | ||
Retina imaging with deep learning can detect [[papilledema]]<ref name="MileaNajjar2020">{{cite journal|last1=Milea|first1=Dan|last2=Najjar|first2=Raymond P.|last3=Zhubo|first3=Jiang|last4=Ting|first4=Daniel|last5=Vasseneix|first5=Caroline|last6=Xu|first6=Xinxing|last7=Aghsaei Fard|first7=Masoud|last8=Fonseca|first8=Pedro|last9=Vanikieti|first9=Kavin|last10=Lagrèze|first10=Wolf A.|last11=La Morgia|first11=Chiara|last12=Cheung|first12=Carol Y.|last13=Hamann|first13=Steffen|last14=Chiquet|first14=Christophe|last15=Sanda|first15=Nicolae|last16=Yang|first16=Hui|last17=Mejico|first17=Luis J.|last18=Rougier|first18=Marie-Bénédicte|last19=Kho|first19=Richard|last20=Thi Ha Chau|first20=Tran|last21=Singhal|first21=Shweta|last22=Gohier|first22=Philippe|last23=Clermont-Vignal|first23=Catherine|last24=Cheng|first24=Ching-Yu|last25=Jonas|first25=Jost B.|last26=Yu-Wai-Man|first26=Patrick|last27=Fraser|first27=Clare L.|last28=Chen|first28=John J.|last29=Ambika|first29=Selvakumar|last30=Miller|first30=Neil R.|last31=Liu|first31=Yong|last32=Newman|first32=Nancy J.|last33=Wong|first33=Tien Y.|last34=Biousse|first34=Valérie|title=Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs|journal=New England Journal of Medicine|volume=382|issue=18|year=2020|pages=1687–1695|issn=0028-4793|doi=10.1056/NEJMoa1917130}}</ref>. | |||
==Legal issues== | ==Legal issues== | ||
Revision as of 00:01, 30 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]
- Jaundice[7]
- Ethanol intoxication[8].
- Cushing's syndrome[9]
- Acromegaly[9]
Retina
Imaging of the retina, using deep-learning trained on data from 284,335 patients, may predict[10]:
- 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)
Retina imaging with deep learning can detect papilledema[11].
Legal issues
Legal issues have been debated about the role of transparency and human oversight in interpreting information derived from deep learning[12][13].
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.
- ↑ Taylor JA, Stout JW, de Greef L, Goel M, Patel S, Chung EK; et al. (2017). "Use of a Smartphone App to Assess Neonatal Jaundice". Pediatrics. 140 (3). doi:10.1542/peds.2017-0312. PMC 5574723. PMID 28842403.
- ↑ 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.
- ↑ 9.0 9.1 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.
- ↑ Milea, Dan; Najjar, Raymond P.; Zhubo, Jiang; Ting, Daniel; Vasseneix, Caroline; Xu, Xinxing; Aghsaei Fard, Masoud; Fonseca, Pedro; Vanikieti, Kavin; Lagrèze, Wolf A.; La Morgia, Chiara; Cheung, Carol Y.; Hamann, Steffen; Chiquet, Christophe; Sanda, Nicolae; Yang, Hui; Mejico, Luis J.; Rougier, Marie-Bénédicte; Kho, Richard; Thi Ha Chau, Tran; Singhal, Shweta; Gohier, Philippe; Clermont-Vignal, Catherine; Cheng, Ching-Yu; Jonas, Jost B.; Yu-Wai-Man, Patrick; Fraser, Clare L.; Chen, John J.; Ambika, Selvakumar; Miller, Neil R.; Liu, Yong; Newman, Nancy J.; Wong, Tien Y.; Biousse, Valérie (2020). "Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs". New England Journal of Medicine. 382 (18): 1687–1695. doi:10.1056/NEJMoa1917130. ISSN 0028-4793.
- ↑ 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