Abstract
Point-of-care ultrasound (POCUS) is taking an even more prominent role in medical imaging, as healthcare systems around the world integrate more advanced technology into clinical medicine. We introduce and discuss the concepts that are revolutionizing the field of ultrasound-enabled clinical evaluation and decision-making. We outline the integration of artificial intelligence into ultrasonography and describe how deep learning technology aids the operator to obtain high-quality images. We look at the benefits miniaturization of ultrasound devices may potentially bring, and we consider how contrast-enhanced ultrasound and tele-ultrasound augment diagnostic imaging and provide rapid and accurate diagnosis in remote settings. Our goal is to broaden the readers’ mind and provide an insight into some futuristic utilities of POCUS.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang G. A perspective on deep imaging. IEEE Access. 2016;4:8914–24.
Liu S, Wang Y, Yang X, Lei B, Liu L, Li SX, Ni D, Wang T. Deep learning in medical ultrasound analysis: a review. Engineering. 2019;5(2):261–75. https://doi.org/10.1016/j.eng.2018.11.020. ISSN 2095-8099
U.S. Food and Drug Administration. FDA authorizes marketing of first cardiac ultrasound software that uses artificial intelligence to guide user. 2020. https://www.fda.gov/news-events/press-announcements/fda-authorizes-marketing-first-cardiac-ultrasound-software-uses-artificial-intelligence-guide-user
Cheema BS, Walter J, Narang A, Thomas JD. Artificial intelligence-enabled POCUS in the COVID-19 ICU: a new spin on cardiac ultrasound. JACC Case Rep. 2021;3(2):258–63. https://doi.org/10.1016/j.jaccas.2020.12.013.
Volpato V, Mor-Avi V, Narang A, et al. Automated, machine learning-based, 3D echocardiographic quantification of left ventricular mass. Echocardiography. 2019;36(2):312–9. https://doi.org/10.1111/echo.14234.
Medvedofsky D, Mor-Avi V, Byku I, et al. Three-dimensional echocardiographic automated quantification of left heart chamber volumes using an adaptive analytics algorithm: feasibility and impact of image quality in nonselected patients. J Am Soc Echocardiogr. 2017;30(9):879–85. https://doi.org/10.1016/j.echo.2017.05.018.
Huang C, Zhou Y, Tan W, et al. Applying deep learning in recognizing the femoral nerve block region on ultrasound images. Ann Transl Med. 2019;7(18):453. https://doi.org/10.21037/atm.2019.08.61.
Smistad E, Johansen KF, Iversen DH, Reinertsen I. Highlighting nerves and blood vessels for ultrasound-guided axillary nerve block procedures using neural networks. J Med Imaging (Bellingham). 2018;5(4):044004. https://doi.org/10.1117/1.JMI.5.4.044004.
KKH collaborates with NUS to develop world-first AI-powered system to enhance accuracy of spinal anaesthesia. https://www.kkh.com.sg/news/research/kkh-collaborates-with-nus-to-develop-world-first-ai-powered-system-to-enhance-accuracy-of-spinal-anaesthesia
Becker AS, Marcon M, Ghafoor S, Wurnig MC, Frauenfelder T, Boss A. Deep learning in mammography: diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer. Investig Radiol. 2017;52:434–40.
Baran JM, Webster JG. Design of low-cost portable ultrasound systems. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE; 2009. pp. 792–795.
Baribeau Y, Sharkey A, Chaudhary O, Krumm S, Fatima H, Mahmood F, et al. Handheld point-of-care ultrasound probes: the new generation of POCUS. J Cardiothorac Vasc Anesth. 2020;34(11):3139–45. https://doi.org/10.1053/j.jvca.2020.07.004.
Malik AN, Rowland J, Haber BD, et al. The use of handheld ultrasound devices in emergency medicine. Curr Emerg Hosp Med Rep. 2021;9(3):73–81. https://doi.org/10.1007/s40138-021-00229-6.
Galusko V, Bodger O, Ionescu A. A systematic review of pocket-sized imaging devices: small and mighty? Echo Res Pract. 2018;5(4):113–38.
Zardi EM, Franceschetti E, Giorgi C, Palumbo A, Franceschi F. Accuracy and performance of a new handheld ultrasound machine with wireless system. Sci Rep. 2019;9(1):14599. https://doi.org/10.1038/s41598-019-51160-6.
European Society of Radiology (ESR). ESR statement on portable ultrasound devices. Insights Imaging. 2019;10(1):89. https://doi.org/10.1186/s13244-019-0775-x.
Barreiros AP, Cui XW, Ignee A, De Molo C, Pirri C, Dietrich CF. EchoScopy in scanning abdominal diseases: initial clinical experience. Z Gastroenterol. 2014;52:269–75.
Stock KF, Klein B, Steubl D, Lersch C, Heemann U, Wagenpfeil S, Eyer F, Clevert D-A. Comparison of a pocket-size ultrasound device with a premium ultrasound machine: diagnostic value and time required in bedside ultrasound examination. Abdom Imaging. 2015;40:2861–6.
Andrea S, Giovanna L, Pietro C, Luca F. Teaching echoscopy for the early diagnosis of ascites in cirrhosis: assessment of an objective structured clinical examination (OSCE). J Ultrasound. 2017;20:123–6.
Esposito R, Ilardi F, Schiano Lomoriello V, Sorrentino R, Sellitto V, Giugliano G, Esposito G, Trimarco B, Galderisi M. Identification of the main determinants of abdominal aorta size: a screening by pocket size imaging device. Cardiovasc Ultrasound. 2017;15:2.
Bonnafy T, Lacroix P, Desormais I, Labrunie A, Marin B, Leclerc A, Oueslati A, Rollé F, Vignon P, Aboyans V. Reliability of the measurement of the abdominal aortic diameter by novice operators using a pocket-sized ultrasound system. Arch Cardiovasc Dis. 2013;106:644–50.
Dijos M, Pucheux Y, Lafitte M, Réant P, Prevot A, Mignot A, Barandon L, Roques X, Roudaut R, Pilois X, et al. Fast track echo of abdominal aortic aneurysm using a real pocket-ultrasound device at bedside. Echocardiography (Mount Kisco, NY). 2012;29:285–90.
Grau T, Leipold RW, Conradi R, Martin E, Motsch J. Ultrasound imaging facilitates localization of the epidural space during combined spinal and epidural anesthesia. Reg Anesth Pain Med. 2001;26:64–7.
Seligman KM, Weiniger CF, Carvalho B. The accuracy of a handheld ultrasound device for neuraxial depth and landmark assessment: a prospective cohort trial. Anesth Analg. 2018;126(6):1995–8. https://doi.org/10.1213/ANE.0000000000002407.
Carvalho B, Seligman KM, Weiniger CF. The comparative accuracy of a handheld and console ultrasound device for neuraxial depth and landmark assessment. Int J Obstet Anesth. 2019;39:68–73. https://doi.org/10.1016/j.ijoa.2019.01.004. Epub 2019 Jan 11
Wilson SR, Greenbaum LD, Goldberg BB. Contrast-enhanced ultrasound: what is the evidence and what are the obstacles? AJR Am J Roentgenol. 2009;193(1):55–60. https://doi.org/10.2214/AJR.09.2553.
Greis C. Technology overview: SonoVue (Bracco, Milan). Eur Radiol. 2004;14(Suppl 8):P11–5.
Sontum PC. Physicochemical characteristics of Sonazoid, a new contrast agent for ultrasound imaging. Ultrasound Med Biol. 2008 May;34(5):824–33.
Chung YE, Kim KW. Contrast-enhanced ultrasonography: advance and current status in abdominal imaging. Ultrasonography. 2015;34(1):3–18. https://doi.org/10.14366/usg.14034.
Piscaglia F, Bolondi L. Italian Society for ultrasound in medicine and biology (SIUMB) study group on ultrasound contrast agents the safety of Sonovue in abdominal applications: retrospective analysis of 23188 investigations. Ultrasound Med Biol. 2006;32:1369–75.
Sidhu PS, Choi BI, Nielsen MB. The EFSUMB guidelines on the nonhepatic clinical applications of contrast enhanced ultrasound (CEUS): a new dawn for the escalating use of this ubiquitous technique. Ultraschall Med. 2012;33:5–7.
The European Agency for the Evaluation of Medicinal Products. Public statement on SONOVUE (Sulphur hexafluoride) new contraindication in patients with heart disease: restriction of use to non-cardiac imaging. London: The European Agency for the Evaluation of Medicinal Products; 2014.
Seitz K, Strobel D. A milestone: approval of CEUS for diagnostic liver imaging in adults and children in the USA. Ultraschall Med. 2016;37(3):229–32. English. https://doi.org/10.1055/s-0042-107411.
Wilson SR, Jang HJ, Kim TK, Burns PN. Diagnosis of focal liver masses on ultrasonography: comparison of unenhanced and contrast-enhanced scans. J Ultrasound Med. 2007;26(6):775–87; quiz 788-90
Leen E, Ceccotti P, Kalogeropoulou C, Angerson WJ, Moug SJ, Horgan PG. Prospective multicenter trial evaluating a novel method of characterizing focal liver lesions using contrast-enhanced sonography. AJR Am J Roentgenol. 2006;186(6):1551–9.
Ding H, Wang WP, Huang BJ. Imaging of focal liver lesions low-mechanical-index real-time ultrasonography with SonoVue. J Ultrasound Med. 2005;24(3):285–97.
Cabassa P, Bipat S, Longaretti L, Morone M, Maroldi R. Liver metastases: Sulphur hexafluoride-enhanced ultrasonography for lesion detection: a systematic review. Ultrasound Med Biol. 2010;36(10):1561–7.
Yanagisawa K, Moriyasu F, Miyahara T, Yuki M, Iijima H. Phagocytosis of ultrasound contrast agent microbubbles by Kupffer cells. Ultrasound Med Biol. 2007;33(2):318–25. https://doi.org/10.1016/j.ultrasmedbio.2006.08.008.
Sawatzki M, Meyenberger C, Brand S, Semela D. Contrast-enhanced ultrasound (CEUS) has excellent diagnostic accuracy in differentiating focal liver lesions: results from a Swiss tertiary gastroenterological centre. Swiss Med Wkly. 2019;149:w20087. https://doi.org/10.4414/smw.2019.20087.
Sidhu PS, Cantisani V, Dietrich CF, et al. The EFSUMB guidelines and recommendations for the clinical practice of contrast-enhanced ultrasound (CEUS) in non-hepatic applications: update 2017 (long version). Ultraschall Medi. 2018;39(02):e2–e44.
Catalano O, Aiani L, Barozzi L, Bokor D, et al. CEUS in abdominal trauma: multi-center study. Abdom Imaging. 2009;34(2):225–34.
Yusuf GT, Wong A, Rao D, et al. The use of contrast-enhanced ultrasound in COVID-19 lung imaging. J Ultrasound. 2020;25(2):319–23. https://doi.org/10.1007/s40477-020-00517-z.
Whitson MR, Mayo PH. Ultrasonography in the emergency department. Crit Care. 2016;20:227. Pmid:27523885
Ferreira AC, Mahony E, Oliani AH, Junior AE, da Silva CF. Teleultrasound: historical perspective and clinical application. Int J Telemed Appl. 2015;2015:306259. pmid:25810717
Sheehan FH, Ricci MA, Murtagh C, Clark H, Bolson EL. Expert visual guidance of ultrasound for telemedicine. J Telemed Telecare. 2010;16(2):77–82.
Law J, Macbeth PB. Ultrasound: from earth to space. McGill J Medi. 2011;13(2):59–65.
Pian L, Gillman LM, McBeth PB, et al. Potential use of remote telesonography as a transformational technology in underresourced and/or remote settings. Emerg Med Int. 2013;2013:986160.
Law J, Macbeth PB. Ultrasound: from earth to space. Mcgill J Med. 2011;13(2):59.
Chiao L, Sharipov S, Sargsyan AE, Melton S, Hamilton DR, McFarlin K, Dulchavsky SA. Ocular examination for trauma; clinical ultrasound aboard the International Space Station. J Trauma. 2005;58(5):885–9.
Crawford I, McBeth PB, Mitchelson M, Ferguson J, Tiruta C, Kirkpatrick AW. How to set up a low cost tele-ultrasound capable videoconferencing system with wide applicability. Crit Ultrasound J. 2012;4(1):13.
Marsh-Feiley G, Eadie L, Wilson P. Telesonography in emergency medicine: a systematic review. PLoS One. 2018;13(5):e0194840. https://doi.org/10.1371/journal.pone.0194840.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Self-Test MCQ (20 Questions)
Self-Test MCQ (20 Questions)
-
1.
Fluid appears _____ on the ultrasound screen
-
A.
White
-
B.
Black
-
C.
Grey
-
D.
Not seen
-
A.
-
2.
The linear probe has a range of frequency of
-
A.
2–5 MHz
-
B.
4–7 MHz
-
C.
5–10 MHz
-
D.
10–15 MHz
-
A.
-
3.
If the image on the monitor screen is too dark, you should
-
A.
Increase depth
-
B.
Use more ultrasound gel
-
C.
Decrease gain
-
D.
Increase gain
-
A.
-
4.
Which of the following is a piezoelectric material?
-
A.
Nickel
-
B.
Tourmaline
-
C.
Iron
-
D.
None of the above
-
A.
-
5.
Speed of ultrasound is the fastest when traveling through
-
A.
Bone
-
B.
Air
-
C.
Water
-
D.
Muscle
-
A.
-
6.
Properties of sound waves include
-
A.
Amplitude
-
B.
Frequency
-
C.
Wavelength
-
D.
All of the above
-
A.
-
7.
The main factor affecting attenuation is
-
A.
Scattering
-
B.
Divergence
-
C.
Absorption
-
D.
Reflection
-
A.
-
8.
Which of the following are not reported benefits of using artificial intelligence (AI) in ultrasonography?
-
A.
Improved survival rates
-
B.
Enhanced diagnostic accuracy
-
C.
Wide clinical applications
-
D.
Image capture assistance
-
A.
-
9.
Which of the following statements regarding artificial intelligence (AI) is FALSE?
-
A.
Deep learning technology is one of the machine learning techniques used in AI
-
B.
AI-powered systems may lower false-positive rates in cancer detection
-
C.
Untrained operators may obtain diagnostic quality imaging
-
D.
AI allows automated calculation of cardiac ejection fraction
-
A.
-
10.
Which of the following statements regarding handheld ultrasound is FALSE?
-
A.
Piezoelectric crystals have a lower cost of production compared to Capacitive Micromachined Ultrasound Transducers (CMUT)
-
B.
Devices may use single or multiple probes
-
C.
2D imaging is classically used
-
D.
Epidural depth is one of the parameters that can be measured
-
A.
-
11.
Which of the following is not a benefit of handheld ultrasound compared to traditional cart-based ultrasound?
-
A.
Low cost
-
B.
Improved image quality
-
C.
Portable
-
D.
Reduced examination time
-
A.
-
12.
Current ultrasound contrast agents in the market consist of
-
A.
Hydrofluorocarbons
-
B.
Agarose gel
-
C.
Galactose and air
-
D.
Sulfur hexafluoride
-
A.
-
13.
The incidence of hypersensitivity reactions with the use of contrast-enhanced ultrasound (CEUS) is reported to be
-
A.
2%
-
B.
0.2%
-
C.
0.002%
-
D.
0.0002%
-
A.
-
14.
Which of the following is not a contraindication of contrast-enhanced ultrasound (CEUS)
-
A.
Coronary artery disease
-
B.
Acute respiratory distress syndrome
-
C.
Acute kidney injury
-
D.
Arrhythmias
-
A.
-
15.
Which of the following statements regarding contrast-enhanced ultrasound (CEUS) is FALSE?
-
A.
The US was the first country to approve the use of CEUS
-
B.
CEUS may be used in pediatric patients
-
C.
POCUS lung ultrasound may benefit from CEUS in detecting lung infarcts
-
D.
CEUS is highly accurate in differentiating benign and malignant lesions
-
A.
-
16.
The gold standard for diagnosis of hepatocellular carcinoma (HCC) is
-
A.
Computed tomography (CT) perfusion
-
B.
Liver biopsy
-
C.
Contrast-enhanced ultrasound (CEUS)
-
D.
Magnetic resonance imaging (MRI)
-
A.
-
17.
In contrast-enhanced ultrasound of the liver, microbubbles are taken up by
-
A.
Sinusoid cells
-
B.
Kupffer cells
-
C.
Stellate cells
-
D.
Hepatocytes
-
A.
-
18.
Which of the following regarding POCUS lung ultrasound is FALSE?
-
A.
B-lines are artifacts caused by acoustic impedance due to the underlying lung
-
B.
Loss of lung sliding is sensitive to pneumothorax
-
C.
In M-mode, the “barcode” sign suggests a pneumothorax
-
D.
“Lung point” is the most specific sign of pneumothorax
-
A.
-
19.
Which of the following regarding the FAST exam is true?
-
A.
It leads to few diagnostic peritoneal lavages
-
B.
FAST can be repeated for serial examinations
-
C.
It is safe for use in pregnant and pediatric patients
-
D.
All of the above
-
A.
-
20.
Regarding the FAST exam
-
A.
FAST is more sensitive in obese patients
-
B.
Solid organ injuries are easily identified
-
C.
Peritoneal free fluid will not be detected until more than 500 ml is present
-
D.
Diagnostic accuracy differs significantly between radiologists and non-radiologists
-
A.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Chia, R.H.X., Ashokka, B. (2022). POCUS: What does the Future Hold?. In: Chakraborty, A., Ashokka, B. (eds) A Practical Guide to Point of Care Ultrasound (POCUS). Springer, Singapore. https://doi.org/10.1007/978-981-16-7687-1_9
Download citation
DOI: https://doi.org/10.1007/978-981-16-7687-1_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-7686-4
Online ISBN: 978-981-16-7687-1
eBook Packages: MedicineMedicine (R0)