Remote sensing technology: Difference between revisions

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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>
* Atrial fibrillation<ref name="pmid32242908">{{cite journal| author=O'Sullivan JW, Grigg S, Crawford W, Turakhia MP, Perez M, Ingelsson E | display-authors=etal| title=Accuracy of Smartphone Camera Applications for Detecting Atrial Fibrillation: A Systematic Review and Meta-analysis. | journal=JAMA Netw Open | year= 2020 | volume= 3 | issue= 4 | pages= e202064 | pmid=32242908 | doi=10.1001/jamanetworkopen.2020.2064 | pmc=7125433 | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=32242908  }} </ref>
* Blood pressure<ref name="pmid31382766">{{cite journal| author=Luo H, Yang D, Barszczyk A, Vempala N, Wei J, Wu SJ | display-authors=etal| title=Smartphone-Based Blood Pressure Measurement Using Transdermal Optical Imaging Technology. | journal=Circ Cardiovasc Imaging | year= 2019 | volume= 12 | issue= 8 | pages= e008857 | pmid=31382766 | doi=10.1161/CIRCIMAGING.119.008857 | pmc= | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=31382766  }} </ref>. The goal for accuracy is the ISO standard of "a device is considered acceptable if its estimated probability of a tolerable error (≤10 mmHg) is at least 85%"<ref name="pmid29384983">{{cite journal| author=Stergiou GS, Alpert B, Mieke S, Asmar R, Atkins N, Eckert S | display-authors=etal| title=A universal standard for the validation of blood pressure measuring devices: Association for the Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization (AAMI/ESH/ISO) Collaboration Statement. | journal=J Hypertens | year= 2018 | volume= 36 | issue= 3 | pages= 472-478 | pmid=29384983 | doi=10.1097/HJH.0000000000001634 | pmc=5796427 | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=29384983  }} </ref> or "average difference no greater than 5 mmHg and SD no greater than 8 mmHg"<ref name="pmid30425819">{{cite journal| author=Khalid SG, Zhang J, Chen F, Zheng D| title=Blood Pressure Estimation Using Photoplethysmography Only: Comparison between Different Machine Learning Approaches. | journal=J Healthc Eng | year= 2018 | volume= 2018 | issue=  | pages= 1548647 | pmid=30425819 | doi=10.1155/2018/1548647 | pmc=6218731 | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=30425819  }} </ref>.
* Blood pressure<ref name="pmid31382766">{{cite journal| author=Luo H, Yang D, Barszczyk A, Vempala N, Wei J, Wu SJ | display-authors=etal| title=Smartphone-Based Blood Pressure Measurement Using Transdermal Optical Imaging Technology. | journal=Circ Cardiovasc Imaging | year= 2019 | volume= 12 | issue= 8 | pages= e008857 | pmid=31382766 | doi=10.1161/CIRCIMAGING.119.008857 | pmc= | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=31382766  }} </ref>. The goal for accuracy is the ISO standard of "a device is considered acceptable if its estimated probability of a tolerable error (≤10 mmHg) is at least 85%"<ref name="pmid29384983">{{cite journal| author=Stergiou GS, Alpert B, Mieke S, Asmar R, Atkins N, Eckert S | display-authors=etal| title=A universal standard for the validation of blood pressure measuring devices: Association for the Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization (AAMI/ESH/ISO) Collaboration Statement. | journal=J Hypertens | year= 2018 | volume= 36 | issue= 3 | pages= 472-478 | pmid=29384983 | doi=10.1097/HJH.0000000000001634 | pmc=5796427 | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=29384983  }} </ref> or "average difference no greater than 5 mmHg and SD no greater than 8 mmHg"<ref name="pmid30425819">{{cite journal| author=Khalid SG, Zhang J, Chen F, Zheng D| title=Blood Pressure Estimation Using Photoplethysmography Only: Comparison between Different Machine Learning Approaches. | journal=J Healthc Eng | year= 2018 | volume= 2018 | issue=  | pages= 1548647 | pmid=30425819 | doi=10.1155/2018/1548647 | pmc=6218731 | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=30425819  }} </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>
* 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>

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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 machine learning and 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:

Retina

Imaging of the retina, using deep-learning trained on data from 284,335 patients, may predict[13]:

  • 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[14].

Other

Pain sensitivity has been measured[15].

Legal issues

Legal issues have been debated about the role of transparency and human oversight in interpreting information derived from deep learning[16][17].

Limitations

The ability to generalize the accuracy of image analyses to images different that those that trained the system may be limited[18].

See also

External links

References

  1. Anonymous (2024), Remote sensing technology (English). Medical Subject Headings. U.S. National Library of Medicine.
  2. 2.0 2.1 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. 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.
  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.
  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.
  6. O'Sullivan JW, Grigg S, Crawford W, Turakhia MP, Perez M, Ingelsson E; et al. (2020). "Accuracy of Smartphone Camera Applications for Detecting Atrial Fibrillation: A Systematic Review and Meta-analysis". JAMA Netw Open. 3 (4): e202064. doi:10.1001/jamanetworkopen.2020.2064. PMC 7125433 Check |pmc= value (help). PMID 32242908 Check |pmid= value (help).
  7. Stergiou GS, Alpert B, Mieke S, Asmar R, Atkins N, Eckert S; et al. (2018). "A universal standard for the validation of blood pressure measuring devices: Association for the Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization (AAMI/ESH/ISO) Collaboration Statement". J Hypertens. 36 (3): 472–478. doi:10.1097/HJH.0000000000001634. PMC 5796427. PMID 29384983.
  8. Khalid SG, Zhang J, Chen F, Zheng D (2018). "Blood Pressure Estimation Using Photoplethysmography Only: Comparison between Different Machine Learning Approaches". J Healthc Eng. 2018: 1548647. doi:10.1155/2018/1548647. PMC 6218731. PMID 30425819.
  9. 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.
  10. 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.
  11. 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.
  12. 12.0 12.1 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.
  13. 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.
  14. 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.
  15. McIntyre MH, 23andMe Research Team. Kless A, Hein P, Field M, Tung JY (2020). "Validity of the cold pressor test and pain sensitivity questionnaire via online self-administration". PLoS One. 15 (4): e0231697. doi:10.1371/journal.pone.0231697. PMC 7162430 Check |pmc= value (help). PMID 32298348 Check |pmid= value (help).
  16. 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
  17. 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
  18. 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/