Automation, machine learning, and artificial intelligence in echocardiography: A brave new world

From Digital Culture & Society

Revision as of 18:53, 13 March 2019 (edit) Bl14hl (Talk | contribs) (New page: Chow, Chi-Ming., Gandhi, Sumeet., Mosleh, Wassim., Shen, Joshua. (2018). Automation, machine learning, and artificial intelligence in echocardiography: A brave new world. ''Wiley'', 35(9),...) ← Previous diff		Revision as of 21:06, 19 March 2019 (edit) (undo) Bl14hl (Talk | contribs) Next diff →
Line 1:		Line 1:
-	Chow, Chi-Ming., Gandhi, Sumeet., Mosleh, Wassim., Shen, Joshua. (2018). Automation, machine learning, and artificial intelligence in echocardiography: A brave new world. ''Wiley'', 35(9), 1402-1417.	+	Chow, Chi-Ming., Gandhi, Sumeet., Mosleh Wassim., Shen, Joshua. (2018). “Automation, machine learning, and artificial intelligence in echocardiography: A brave new world.” Wiley Echocardiography, 35(9), 1402-1417.
	Find article online:		Find article online:
-
-	Link goes here
	DOI: 10.1111/echo.14086		DOI: 10.1111/echo.14086
Line 9:		Line 7:
	==Context==		==Context==
-	The online presence via social media for online service brands has become an important factor in order for brands to promote themselves through advertisement and marketing. Many services that were once ‘offline’ have shifted their focus from one-way communication to customer-customer interaction via social media. (Mills & Plangger, p. 522). This shift creates a more meaningful relationship between customer and service, offering long-lasting relationships and personal connections between customer and service. The authors begin by explaining the various forms of social media and conclude their argument by offering a strategy of how to resource for online services.	+	This article discusses how automation, machine learning, and artificial intelligence have been utilized as tools in echocardiography to assist physicians in their work. As technological practices have gotten advanced over time, the scope of those practices are now able to reach different sectors. As discussed in this article, the realm of medical work has the benefit of adopting deep learning to assist medical physicians in their work. Although the usage of machine learning in the medical field is still in its infancy, its potential allows for the training of data sets to go beyond what traditional statistical reasoning can handle. These methods of deep learning when applied to large image archives will be able to recognize complex patterns that helps to establish a better understanding of the prognosis of cardiac diseases. The purpose of this journal article was to describe the methods of deep learning to discuss how it will impact echocardiography in the future.
	==Overview==		==Overview==
-	The authors of this article discuss how brands can establish beneficial communication between their customers by improving or creating an online presence via social media (Mills & Plangger, p. 529). They argue how the communication of dialogue between brand service and customers develops a level of trust between brands and customers. With the integration of social media and the internet, there has been a migration of how these service brands must develop their interactive relationship with customers, which is now done through online services. The integration of social media has an outlonging reach as well, a third of 18-44 year-old social media users consciously follow brands and brand pages on social media networks (Mills & Plangger, p. 528). The young-adult market is a crucial market segment in order to retain customers for long-term sustainability, social media acts as the bridge for service brands to create a relationship with their targeted audience. To further their argument about the importance of an online presence for a brand, the authors provide a nine-step process of a strategic process for creating the relationship between brand and customer.	+	This article begins by discussing the boundaries of deep learning by explaining how they can aid clinical decision-making. The authors explain the concepts of artificial intelligence and define terms associated with deep learning. Practices of deep learning are seen as suitable for clinical use because of how machine learning acts as an artificial neural network. An artificial neural network assists in making targeted decisions based upon the weights of neurons found in a human brain, in order to discover these neurons the process of machine learning is used. To apply this data, convolution neural network is used to identify echocardiographic views which can assist in the prognosis of cardiac diseases through echocardiography. In order to support their research the authors have taken an abundance of other studies that pertain to automation in echocardiography to summarize findings between the fields of research. The main portion of the article is an explanation of how automation can be applied to echocardiographic data such as knowledge-based identification of cardiac chamber shapes. A limitation in their image collection was discovered when the software used did not produce quality images. Echocardiographic analysis acts as a component of decision-making and management in patients with valve disease. The authors conclude their study by deeming that current machines are not able to replace human experts in echocardiography interpretation and that the practice with machines will need further development.
	==Strengths and Weaknesses==		==Strengths and Weaknesses==
-	The first strength of this article is the constant reiteration of why the concept of communication between brand and customer is important. The authors go into detail about how the connection between brand and customer establishes a relationship where the customer is likely to feel association towards the brand and therefore be more trustworthy of that brand. Another point the authors make is how the constant activity of social media allows for their viewers to have a constant channel of communication between their audience. The article explains how self-promotion of a brand through social media also simultaneously engages a relationship with their customer, thus causing a constant flux of activity between brand and user. Finally, the article outlines a strategy through steps as to how a brand should think when wanting to interact with their audience. It can be summed down to targeting a set audience, building a platform for that audience and managing the interaction of the platform between the targeted audience.	+	The main strength of this article is how it thoroughly articulates how artificial intelligence, machine learning, and automation have proficient usage in the medical field. Each practice of deep learning has its own section within the article where it is explained as to how it is relevant to echocardiographic imagery. The second strength of this article is how the field of automation in cardiography is showcased. The authors have incorporated an extensive amount of other research data to correlate their discussion in the practices of deep learning within echocardiographic imaging. The research data is summarized within tables in the article that explain the main finding from the author of each research study.
-	The main weakness this article has is it doesn’t establish how a service brand should go about creating an online persona for their service. There is constant reiteration of the importance of why service brands should utilize social media to promote themselves, although the authors don’t dictate how that exactly should be done. They provide a process of steps, although these steps are more about reasoning than implementation. These steps explain as to what the service brand should be doing to target their audience but don’t establish how a service brand should go about creating a proper ‘persona’ of themselves on social media. This persona being what the company represents and what they offer for their customers.	+	The weakness of this article is how it does not compute the statistics or methodology of how effective machine learning and automation is within relation to echocardiographic imagery. This is a weakness due to how it is discussed how deep learning can benefit this field but the results of how it benefits aren’t necessarily outlined within the article. While the authors go into extensive detail as to how various machine-learning algorithms and automation is incorporated into echocardiographic imagery, the measurement of how beneficial computer science in the medical field isn’t necessarily detailed.
	==Assessment==		==Assessment==
-	In conclusion, the article addresses the importance of why a service brand should go online, specifically through the integration of social media. The article describes different strategies of utilizing social media, and how different forms of social media can be utilized for different marketing purposes. After a brand establishes a channel of communication between their customer, it creates a level of trust that the customer has for that brand through the relationship that is built. The article analyzes the importance of an online presence and how social media should be used by brands to build their online presence.	+	In conclusion, this article provides an excellent explanation of how machines can be utilized in the medical field. The authors utilize the work of their peers to assist their research while also explaining how each sector of deep learning can help contribute towards medical physicians in their line of work. Nonetheless, the authors are not biased in their work as they also delineate how their are limitations of automation and machine learning in echocardiographic imagery. Even though machine errors are probable, AI has the ability to optimize productivity and push the boundaries of technological discovery in the field of medicine.
		+	----
		+	--[[User:Bl14hl|Bl14hl]] 21:06, 19 March 2019 (EDT)

---

Revision as of 21:06, 19 March 2019

Chow, Chi-Ming., Gandhi, Sumeet., Mosleh Wassim., Shen, Joshua. (2018). “Automation, machine learning, and artificial intelligence in echocardiography: A brave new world.” Wiley Echocardiography, 35(9), 1402-1417.

Find article online:

DOI: 10.1111/echo.14086

Contents 1 Context 2 Overview 3 Strengths and Weaknesses 4 Assessment

Context

This article discusses how automation, machine learning, and artificial intelligence have been utilized as tools in echocardiography to assist physicians in their work. As technological practices have gotten advanced over time, the scope of those practices are now able to reach different sectors. As discussed in this article, the realm of medical work has the benefit of adopting deep learning to assist medical physicians in their work. Although the usage of machine learning in the medical field is still in its infancy, its potential allows for the training of data sets to go beyond what traditional statistical reasoning can handle. These methods of deep learning when applied to large image archives will be able to recognize complex patterns that helps to establish a better understanding of the prognosis of cardiac diseases. The purpose of this journal article was to describe the methods of deep learning to discuss how it will impact echocardiography in the future.

Overview

This article begins by discussing the boundaries of deep learning by explaining how they can aid clinical decision-making. The authors explain the concepts of artificial intelligence and define terms associated with deep learning. Practices of deep learning are seen as suitable for clinical use because of how machine learning acts as an artificial neural network. An artificial neural network assists in making targeted decisions based upon the weights of neurons found in a human brain, in order to discover these neurons the process of machine learning is used. To apply this data, convolution neural network is used to identify echocardiographic views which can assist in the prognosis of cardiac diseases through echocardiography. In order to support their research the authors have taken an abundance of other studies that pertain to automation in echocardiography to summarize findings between the fields of research. The main portion of the article is an explanation of how automation can be applied to echocardiographic data such as knowledge-based identification of cardiac chamber shapes. A limitation in their image collection was discovered when the software used did not produce quality images. Echocardiographic analysis acts as a component of decision-making and management in patients with valve disease. The authors conclude their study by deeming that current machines are not able to replace human experts in echocardiography interpretation and that the practice with machines will need further development.

Strengths and Weaknesses

The main strength of this article is how it thoroughly articulates how artificial intelligence, machine learning, and automation have proficient usage in the medical field. Each practice of deep learning has its own section within the article where it is explained as to how it is relevant to echocardiographic imagery. The second strength of this article is how the field of automation in cardiography is showcased. The authors have incorporated an extensive amount of other research data to correlate their discussion in the practices of deep learning within echocardiographic imaging. The research data is summarized within tables in the article that explain the main finding from the author of each research study.

The weakness of this article is how it does not compute the statistics or methodology of how effective machine learning and automation is within relation to echocardiographic imagery. This is a weakness due to how it is discussed how deep learning can benefit this field but the results of how it benefits aren’t necessarily outlined within the article. While the authors go into extensive detail as to how various machine-learning algorithms and automation is incorporated into echocardiographic imagery, the measurement of how beneficial computer science in the medical field isn’t necessarily detailed.

Assessment

In conclusion, this article provides an excellent explanation of how machines can be utilized in the medical field. The authors utilize the work of their peers to assist their research while also explaining how each sector of deep learning can help contribute towards medical physicians in their line of work. Nonetheless, the authors are not biased in their work as they also delineate how their are limitations of automation and machine learning in echocardiographic imagery. Even though machine errors are probable, AI has the ability to optimize productivity and push the boundaries of technological discovery in the field of medicine.

---

--Bl14hl 21:06, 19 March 2019 (EDT)