Application of mobile health, telemedicine and artificial intelligence to echocardiography

Abstract

The intersection of global broadband technology and miniaturized high-capability computing devices has led to a revolution in the delivery of healthcare and the birth of telemedicine and mobile health (mHealth). Rapid advances in handheld imaging devices with other mHealth devices such as smartphone apps and wearable devices are making great strides in the field of cardiovascular imaging like never before. Although these technologies offer a bright promise in cardiovascular imaging, it is far from straightforward. The massive data influx from telemedicine and mHealth including cardiovascular imaging supersedes the existing capabilities of current healthcare system and statistical software. Artificial intelligence with machine learning is the one and only way to navigate through this complex maze of the data influx through various approaches. Deep learning techniques are further expanding their role by image recognition and automated measurements. Artificial intelligence provides limitless opportunity to rigorously analyze data. As we move forward, the futures of mHealth, telemedicine and artificial intelligence are increasingly becoming intertwined to give rise to precision medicine.

Keywords: artificial intelligence; mobile health; telemedicine.

Figure 1

Type of handheld ultrasound machines. There are several types of handheld ultrasounds with various capabilities; a laptop-based equipment has almost every 2D echocardiographic application (panel A), while a pocket-size ultrasound does not usually have full-scale color-flow and spectral Doppler capabilities (panel B). Reproduced, with permission, from Chamsi-Pasha et al. (4).

Figure 2

Interrogation of mHealth devices and use of artificial intelligence. Technological advancement has created a number of mobile health devices, which are available even in resource-limited areas. Involving remote experts using telemedicine helps appropriate diagnosis and management. Artificial intelligence can efficiently address the lack of experts and the influx of complex data generated by mHealth and telemedicine as well as advanced imaging modalities.

Figure 3

Growth of publications in machine learning. The x- and y-axis shows the year and the number of publications in PubMed with ‘Cardiology’ and ‘Machine Learning’. The number of publications is rapidly growing, representing huge interest in the field. Reproduced, with permission, from Shameer et al. (50).

Figure 4

Association of artificial intelligence, machine learning and deep learning. Artificial intelligence (AI), though there are various definitions by itself, represents any techniques which enables computers mimic human behavior when it’s used in medical field. Machine learning is a subfield of AI, which aims at automatic discovery of regularities in data through the use of computer algorithms and generalizing those into new but similar data. Deep learning is a subset of machine learning, which makes the computation of multi-layer neural networks feasible.