Remote sensing technology: Difference between revisions

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Revision as of 13:05, 6 May 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].

Limitations

The ability to generalize the accuracy of image analyses to images different that those that trained the system may be limited^[14].

External links

References

↑ Anonymous (2024), 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
↑ Heaven, WD. Google’s medical AI was super accurate in a lab. Real life was a different story.MIT Technology Review 2020. Available at https://www.technologyreview.com/2020/04/27/1000658/google-medical-ai-accurate-lab-real-life-clinic-covid-diabetes-retina-disease/

[1] Anonymous (2024), Remote sensing technology (English). Medical Subject Headings. U.S. National Library of Medicine.

[pmid31382766-2] 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.

[pmid29865289-3] 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.

[pmid29632098-4] 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.

[LomalizaPark2019-5] 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.

[pmid28249595-6] 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.

[pmid28842403-7] 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.

[HermosillaVerdugo2018-8] 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.

[KosilekFrohner2015-9] 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.

[PoplinVaradarajan2018-10] 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.

[MileaNajjar2020-11] 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.

[12] 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

[13] 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

[14] Heaven, WD. Google’s medical AI was super accurate in a lab. Real life was a different story.MIT Technology Review 2020. Available at https://www.technologyreview.com/2020/04/27/1000658/google-medical-ai-accurate-lab-real-life-clinic-covid-diabetes-retina-disease/

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 ==Legal issues==
 Legal issues have been debated about the role of transparency and human oversight in interpreting information derived from deep learning<ref>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</ref><ref>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</ref>.
+==Limitations==
+The ability to generalize the accuracy of image analyses to images different that those that trained the system may be limited<ref>Heaven, WD. Google’s medical AI was super accurate in a lab. Real life was a different story.MIT Technology Review 2020. Available at https://www.technologyreview.com/2020/04/27/1000658/google-medical-ai-accurate-lab-real-life-clinic-covid-diabetes-retina-disease/</ref>.
 ==See also==