Remote sensing technology
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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:
- Heart rate[5][3]
- Atrial fibrillation[6]
- Blood pressure[2]. 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%"[7] or "average difference no greater than 5 mmHg and SD no greater than 8 mmHg"[8].
- Respiratory rate[9]
- Jaundice[10]
- Ethanol intoxication[11].
- Cushing's syndrome[12]
- Acromegaly[12]
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].
Thermal image sensors may be helpful[16].
Legal issues
Legal issues have been debated about the role of transparency and human oversight in interpreting information derived from deep learning[17][18].
Limitations
The ability to generalize the accuracy of image analyses to images different that those that trained the system may be limited[19].
See also
External links
References
- ↑ Anonymous (2024), Remote sensing technology (English). Medical Subject Headings. U.S. National Library of Medicine.
- ↑ 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.
- ↑ 3.0 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.
- ↑ 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). - ↑ 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.
- ↑ 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.
- ↑ 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.
- ↑ 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.
- ↑ 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.
- ↑ 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). - ↑ Negishi T, Abe S, Matsui T, Liu H, Kurosawa M, Kirimoto T; et al. (2020). "Contactless Vital Signs Measurement System Using RGB-Thermal Image Sensors and Its Clinical Screening Test on Patients with Seasonal Influenza". Sensors (Basel). 20 (8). doi:10.3390/s20082171. PMC 7218727 Check
|pmc=value (help). PMID 32294973 Check|pmid=value (help). - ↑ 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/