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and the precise measurement of heart function and structure&#46;<a class="elsevierStyleCrossRefs" href="#bib0005"><span class="elsevierStyleSup">1&#44;2</span></a> These aspects are crucial for the applicability and reliability of the technique in clinical practice&#44; and it is in these areas that we will comment on the advances that may help us most&#46;<a class="elsevierStyleCrossRefs" href="#bib0015"><span class="elsevierStyleSup">3&#8211;5</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">Artificial intelligence &#40;AI&#41; is undoubtedly of fundamental help<a class="elsevierStyleCrossRefs" href="#bib0030"><span class="elsevierStyleSup">6&#44;7</span></a> &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#41;&#46; The automation of images saves time and resources&#44; but deep learning algorithms such as convolutional networks and transformers have moreover shown a strong potential for extraction of the most important characteristics of images and their interpretation&#46;<a class="elsevierStyleCrossRefs" href="#bib0040"><span class="elsevierStyleSup">8&#44;9</span></a> The processing of medical images includes the suppression of background noise and the adjustment of brightness and contrast&#44; homogenizing size and standardizing pixel intensities&#46; Interpretation based on AI is somewhat more complex and requires problem adjustment &#8211; whether the classification of the images&#44; their segmentation&#44; or the detection of objects in them &#8211; in order to focus on the relevant aspects subject to analysis<a class="elsevierStyleCrossRefs" href="#bib0050"><span class="elsevierStyleSup">10&#44;11</span></a> &#40;<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#41;&#46;</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0020" class="elsevierStylePara elsevierViewall">We will focus not on the <span class="elsevierStyleItalic">how</span> but on the evolution itself&#44; which has led to a revolution in ultrasound applied to the critically ill&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0030">Cardiac image acquisition and recognition</span><p id="par0025" class="elsevierStylePara elsevierViewall">The acquisition of optimum and reproducible echocardiographic images in intensive care is challenging&#46; Not only the positioning of the patient but also the limited cooperation involved&#44; the interpositioning of surgical dressings&#44; and assisted ventilation imply that obtaining an optimum window over several cardiac cycles in which to perform measurements is not feasible in some cases &#8211; a fact that limits the precision and diagnostic performance of the technique&#46;</p><p id="par0030" class="elsevierStylePara elsevierViewall">Caption AI&#169; and Caption Guidance&#169; are new software applications allowing intensivists &#8211; even those with no prior experience in ultrasound &#8211; to perform echocardiographic explorations quickly&#44; precisely and at the patients bedside&#46;<a class="elsevierStyleCrossRefs" href="#bib0060"><span class="elsevierStyleSup">12&#8211;14</span></a> This novel software combines immediate information on the orientation and inclination of the transducer in order to optimize the image and at the same time automatically assess its quality &#8211; increasing the capacity to secure an intelligent interpretation of the exploration &#40;Figs&#46; <a class="elsevierStyleCrossRef" href="#fig0010">2</a> and <a class="elsevierStyleCrossRef" href="#fig0010">3</a>&#41;&#46;<a class="elsevierStyleCrossRefs" href="#bib0075"><span class="elsevierStyleSup">15&#44;16</span></a></p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0035" class="elsevierStylePara elsevierViewall">Caption AI&#169; recognizes the structures through artificial intelligence&#44; and Caption Guidance&#169; affords the precise orientation of the transducer at the patient bedside during the exploration&#46; Both applications generate real-time feedback on imaging quality in order to secure automatic high-quality image capture&#46;<a class="elsevierStyleCrossRefs" href="#bib0085"><span class="elsevierStyleSup">17&#8211;19</span></a> This technology allows continuous monitoring of the best images seen during each scan&#44; automatically capturing the image of greatest precision from each window&#46; Automatic review of the clips obtained classifies them according to image quality&#44; selecting the best ones for calculating basic exploratory parameters&#46; The operator can explore each plane with the confidence of always being able to access the best two-second clip for examination by simply pressing a button &#40;Video 1&#44; Supplementary material&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a></p><p id="par0040" class="elsevierStylePara elsevierViewall">Their reliability has been certified by the United States Food and Drug Administration &#40;FDA&#41;&#44; and in this regard the studies cited above have analyzed over four million images corresponding to examinations performed by inexperienced physicians&#44; in which calculation of the ejection fraction &#40;EF&#41; and its algorithm were found to be precise and reliable even in obese patients with impaired or normal cardiac function&#46;</p><p id="par0045" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Clinical application in critically ill patients&#58;</span><ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005"><span class="elsevierStyleLabel">-</span><p id="par0050" class="elsevierStylePara elsevierViewall">Optimization of ultrasound images</p></li><li class="elsevierStyleListItem" id="lsti0010"><span class="elsevierStyleLabel">-</span><p id="par0055" class="elsevierStylePara elsevierViewall">Reduction of intra- and inter-operator variability</p></li><li class="elsevierStyleListItem" id="lsti0015"><span class="elsevierStyleLabel">-</span><p id="par0060" class="elsevierStylePara elsevierViewall">Automatic saving of images</p></li></ul></p></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Recognition and interpretation of pulmonary&#44; venous and Doppler imaging</span><p id="par0065" class="elsevierStylePara elsevierViewall">Image identification and recognition is not the only important consideration in critically ill patients&#46; These subjects accumulate tests and studies&#44; and it is important to be able to file these data for successive and continuous analysis&#46; This is a key aspect&#44; since immediate information is constantly contrasted against previously obtained information in the Intensive Care Unit &#40;ICU&#41;&#46; The facilitation of repeated and grouped data consultation corresponding to the same patient is therefore of great help in analyzing his or her evolution over time&#46;</p><p id="par0070" class="elsevierStylePara elsevierViewall">Different commercial software applications are available for automatically identifying cardiac&#44; vascular and pulmonary structures&#44; as well as for delimiting Doppler waves&#44; from a hemodynamic perspective closely oriented toward the critically ill patient&#46;<a class="elsevierStyleCrossRefs" href="#bib0105"><span class="elsevierStyleSup">21&#44;22</span></a><ul class="elsevierStyleList" id="lis0010"><li class="elsevierStyleListItem" id="lsti0020"><span class="elsevierStyleLabel">-</span><p id="par0075" class="elsevierStylePara elsevierViewall">Automatic assessment of the inferior vena cava &#40;IVC&#41; as a surrogate marker of preload and response to volume replacement allows optimum and precise evaluation of the vein with regard to its major diameters&#44; and measurement of its collapsibility or distensibility &#40;<a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 4</a>&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">23</span></a> Reliability is equivalent to the capacity of an expert in 87&#37; of the cases&#46;<a class="elsevierStyleCrossRefs" href="#bib0120"><span class="elsevierStyleSup">24&#8211;27</span></a></p><elsevierMultimedia ident="fig0015"></elsevierMultimedia></li><li class="elsevierStyleListItem" id="lsti0025"><span class="elsevierStyleLabel">-</span><p id="par0080" class="elsevierStylePara elsevierViewall">Automatic assessment of the velocity-time integral &#40;VTI&#41; in the left ventricular outflow tract &#40;LVOT&#41;&#44; as a surrogate marker of cardiac output&#44; and its variability in the context of mechanical ventilation&#44; is used to predict response to volume replacement&#46; It identifies the largest and best-defined pulsed Doppler wave contour&#44; and its variation with patient respiration&#46; The recording can be filed&#44; generating a trend plot that is of help in quickly visualizing the time course&#44; becoming a monitoring technique &#40;<a class="elsevierStyleCrossRef" href="#fig0020">Fig&#46; 5</a>&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0140"><span class="elsevierStyleSup">28</span></a> Reliability is equivalent to the capacity of an expert in 82&#37; of the cases&#46;<a class="elsevierStyleCrossRefs" href="#bib0145"><span class="elsevierStyleSup">29&#8211;31</span></a></p><elsevierMultimedia ident="fig0020"></elsevierMultimedia></li><li class="elsevierStyleListItem" id="lsti0030"><span class="elsevierStyleLabel">-</span><p id="par0085" class="elsevierStylePara elsevierViewall">Automatic assessment of the B lines in the lung fields&#44; as a lung congestion surrogate allows the software to calculate the number&#44; consistency and thickness of the B lines&#44; storing this information according to the thoracic segments explored and the examinations made&#44; in order to subsequently visualize the evolution over time in the serial form&#46;<a class="elsevierStyleCrossRefs" href="#bib0160"><span class="elsevierStyleSup">32&#8211;34</span></a> The LungSweep&#169; application allows a sweep in thoracic exploration according to the intercostal space and lung segment explored &#40;Figure S1&#44; Supplementary material&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0175"><span class="elsevierStyleSup">35</span></a> This recording generates a lung aeration map of use in diagnosing pneumonia&#46; The exploration according to lung segments makes it possible to identify the number&#44; thickness and consistency of the pulmonary B lines &#40;Figure S2&#44; Supplementary material&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0180"><span class="elsevierStyleSup">36</span></a> Reliability is equivalent to the capacity of an expert in 86&#37; of the cases&#46;<a class="elsevierStyleCrossRefs" href="#bib0185"><span class="elsevierStyleSup">37&#8211;39</span></a></p></li></ul></p><p id="par0090" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Clinical application in critically ill patients&#58;</span><ul class="elsevierStyleList" id="lis0015"><li class="elsevierStyleListItem" id="lsti0035"><span class="elsevierStyleLabel">-</span><p id="par0095" class="elsevierStylePara elsevierViewall">Assessment of preload&#44; volemia and response to fluid replacement</p></li><li class="elsevierStyleListItem" id="lsti0040"><span class="elsevierStyleLabel">-</span><p id="par0100" class="elsevierStylePara elsevierViewall">Assessment and trends in cardiac output</p></li><li class="elsevierStyleListItem" id="lsti0045"><span class="elsevierStyleLabel">-</span><p id="par0105" class="elsevierStylePara elsevierViewall">Assessment of lung congestion</p></li><li class="elsevierStyleListItem" id="lsti0050"><span class="elsevierStyleLabel">-</span><p id="par0110" class="elsevierStylePara elsevierViewall">Aggregation and analysis of ultrasound data &#40;hemodynamic trends&#41;</p></li></ul></p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Strain and auto-strain</span><p id="par0115" class="elsevierStylePara elsevierViewall">&#34;Strain&#34; and &#34;strain rate&#34; are two recent parameters that require offline edition in the imaging workstation&#46; Nevertheless&#44; software has now been incorporated making it possible for some systems to calculate them automatically at the patient&#39;s bedside &#40;point of care&#41;&#46; This information is important&#44; as it affords greater reliability in assessing intrinsic ventricular systolic function&#44; and can also be used to assess diastolic function with the analysis of the left atrial contraction&#46;</p><p id="par0120" class="elsevierStylePara elsevierViewall">&#8220;Strain&#8221; refers to ventricular muscle fiber deformation during systole&#46; When the heart contracts&#44; the muscle fibers shorten in certain directions and elongate in others&#46; &#8220;Strain&#8221; is a quantitative measure of this deformation&#44; and is expressed as a percentage&#46; A negative &#8220;strain&#8221; value indicates shortening&#44; while a positive value indicates elongation&#46; In echocardiography&#44; &#8220;strain&#8221; is recorded using advanced techniques such as &#8220;speckle tracking&#8221;&#44; monitoring the movement of small points in the myocardium of the ventricles and left atrium&#44; fundamentally&#46; The &#8220;strain rate&#8221; &#40;deformation rate&#41; refers to the rate at which ventricle muscle fiber deformation occurs&#44; and measures how &#8220;strain&#8221; varies per unit time&#46; The &#8220;strain rate&#8221; is expressed as units of length per unit of time &#40;e&#46;g&#46;&#44; 1&#47;s&#41;&#44; and provides additional information on cardiac contraction compared with static &#8220;stain&#8221; alone<a class="elsevierStyleCrossRefs" href="#bib0200"><span class="elsevierStyleSup">40&#8211;42</span></a> &#40;Figure S3&#44; Supplementary material&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0215"><span class="elsevierStyleSup">43</span></a></p><p id="par0125" class="elsevierStylePara elsevierViewall">&#8220;Strain&#8221; can also be used in the left atrium&#46; The deformation of this cavity is directly related to the diastolic function of the left ventricle&#46; The normal &#8220;strain&#8221; values of the left atrium can vary&#44; though as a reference&#44; the typical left atrial &#8220;strain&#8221; values tend to range between &#8722;20&#37; and &#8722;30&#37;&#46;<a class="elsevierStyleCrossRefs" href="#bib0220"><span class="elsevierStyleSup">44&#8211;47</span></a></p><p id="par0130" class="elsevierStylePara elsevierViewall">&#8220;Auto-strain&#8221; is a software parameter allowing some systems to automatically calculate myocardial &#8220;strain&#8221; from images at the patient&#39;s bedside without the operator having to perform manual measurements&#46; The device only needs an apical image optimized as far as possible&#44; in the four-chamber&#44; two-chamber and three-chamber views&#46; This automated function has become more common with the incorporation of image processing technologies and workflow analysis in echocardiography<a class="elsevierStyleCrossRefs" href="#bib0240"><span class="elsevierStyleSup">48&#8211;51</span></a> &#40;Video 2&#44;<a class="elsevierStyleCrossRef" href="#bib0260"><span class="elsevierStyleSup">52</span></a> Video 3&#44;<a class="elsevierStyleCrossRef" href="#bib0265"><span class="elsevierStyleSup">53</span></a> Video 4&#44;<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">54</span></a> Supplementary material&#41;&#46;</p><p id="par0135" class="elsevierStylePara elsevierViewall">The generation of &#34;auto-strain&#34; is a further step in the automation of certain variables in ultrasound&#46; We have already seen that &#8220;strain&#8221; is a better and more reliable measure than the ejection fraction&#44; which is affected by artifacts secondary to many variables such as preload&#44; postload and the inherent characteristics of myocardial distensibility&#46; This parameter is extremely useful for exploring the right ventricle&#44; which proves very difficult and variable in critically ill patients subjected to mechanical ventilation&#46; Such software automatically provides information that will increase precision and change the terms of the systolic assessment of both ventricles&#46;</p><p id="par0140" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Clinical application in critically ill patients&#58;</span><ul class="elsevierStyleList" id="lis0020"><li class="elsevierStyleListItem" id="lsti0055"><span class="elsevierStyleLabel">-</span><p id="par0145" class="elsevierStylePara elsevierViewall">Reliable assessment of ventricular function</p></li><li class="elsevierStyleListItem" id="lsti0060"><span class="elsevierStyleLabel">-</span><p id="par0150" class="elsevierStylePara elsevierViewall">Assessment of diastolic function and intraventricular pressures</p></li></ul></p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Automatic determination of volume</span><p id="par0155" class="elsevierStylePara elsevierViewall">Ventricular area and volume can be evaluated in different ways&#46; The most common approach is the biplanar method&#44; based on the sum of two discs &#40;the modified Simpson&#8217;s rule&#41; obtained from two-dimensional &#40;2D&#41; echocardiographic images &#40;areas&#41;&#46; The analysis of areas allows us to determine volumes&#44; with values that can be used to calculate the ejection fraction &#40;EF&#41;&#44; systolic &#40;stroke&#41; volume&#44; filling or end-diastolic volume&#44; and cardiac output &#40;CO&#41;&#46; The limitations of the biplanar method are multiple&#44; such as a clear view of the endocardium&#44; imaging in a perfect longitudinal plane&#44; alignment of the apex&#44; or poor visualization in the apical two- and four-chamber views for volume estimation&#46;</p><p id="par0160" class="elsevierStylePara elsevierViewall">&#8220;Three-dimensional &#40;3D&#41; automated volume&#8221; in echocardiography is a software tool that allows the echocardiographic system to automatically generate a 3D volume of the heart without the operator having to perform manual measurements&#46;<a class="elsevierStyleCrossRefs" href="#bib0275"><span class="elsevierStyleSup">55&#8211;57</span></a> This function is an advanced feature that allows us to obtain a 3D representation of the heart that can be useful for a more detailed evaluation of cardiac function and anatomy &#40;Figure S4&#44; Supplementary material&#41;<a class="elsevierStyleCrossRef" href="#bib0290"><span class="elsevierStyleSup">58</span></a> &#40;Video 5 <a class="elsevierStyleCrossRef" href="#bib0295"><span class="elsevierStyleSup">59</span></a>&#44; Supplementary material&#41;&#46;</p><p id="par0165" class="elsevierStylePara elsevierViewall">Due to its morphology&#44; the most relevant clinical challenge in this regard is the right ventricle &#40;RV&#41;&#46; The determination of its end-diastolic volume is simply not possible with the disc system&#44; which assumes a regular morphology to calculate volume from the areas&#44; as in the case of the left ventricle &#40;LV&#41;&#46; The determination of RV volume can be very useful in the critically ill patient since it is known to adapt and change in response to increases in pressure or volume&#59; its continuous monitoring in a patient under mechanical ventilation in the ICU&#44; therefore&#44; may be very relevant and can help improve ventilation management&#46;<a class="elsevierStyleCrossRefs" href="#bib0300"><span class="elsevierStyleSup">60&#8211;62</span></a> &#40;Figure S5 Supplementary material&#41; <a class="elsevierStyleCrossRef" href="#bib0315"><span class="elsevierStyleSup">63</span></a> &#40;Video 6&#44;<a class="elsevierStyleCrossRef" href="#bib0320"><span class="elsevierStyleSup">64</span></a> Supplementary material&#41;&#46;</p><p id="par0170" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Clinical application in critically ill patients&#58;</span><ul class="elsevierStyleList" id="lis0025"><li class="elsevierStyleListItem" id="lsti0065"><span class="elsevierStyleLabel">-</span><p id="par0175" class="elsevierStylePara elsevierViewall">Assessment of preload &#40;LV end-diastolic volume&#41;</p></li><li class="elsevierStyleListItem" id="lsti0070"><span class="elsevierStyleLabel">-</span><p id="par0180" class="elsevierStylePara elsevierViewall">Assessment of systolic volume and cardiac output</p></li><li class="elsevierStyleListItem" id="lsti0075"><span class="elsevierStyleLabel">-</span><p id="par0185" class="elsevierStylePara elsevierViewall">Assessment of the repercussions of mechanical ventilation upon the RV</p></li></ul></p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Continuous ultrasound monitoring</span><p id="par0190" class="elsevierStylePara elsevierViewall">The implementation of hemodynamic monitoring using ultrasound as a tool capable of competing with thermodilution or pulse wave techniques has faced several limitations&#46; At present&#44; ultrasound cannot be regarded as a continuous monitoring tool&#44; though it does constitute an essential exploratory complement&#46; Transesophageal echocardiography devices as small as a nasogastric tube are available on the market and can be kept in place for up to 72<span class="elsevierStyleHsp" style=""></span>h&#44; offering a continuous recording of indixes such as the response to volume replacement therapy based on the distensibility of the superior vena cava &#40;SVC&#41; and ventricular function in visual mode<a class="elsevierStyleCrossRefs" href="#bib0325"><span class="elsevierStyleSup">65&#44;66</span></a> &#40;Figure S6&#44; Supplementary material&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0335"><span class="elsevierStyleSup">67</span></a></p><p id="par0195" class="elsevierStylePara elsevierViewall">Future developments could include patches adhered to the skin and capable of transmitting images continuously for 48<span class="elsevierStyleHsp" style=""></span>h&#46; A research line at the Massachusetts Institute of Technology &#40;MIT&#41; has produced a patch with the size of a stamp that generates high-resolution images <span class="elsevierStyleItalic">in vivo&#46;</span><a class="elsevierStyleCrossRefs" href="#bib0340"><span class="elsevierStyleSup">68&#8211;71</span></a> The patch measures approximately 4<span class="elsevierStyleHsp" style=""></span>cm in width and 0&#46;5<span class="elsevierStyleHsp" style=""></span>cm in thickness &#40;<a class="elsevierStyleCrossRef" href="#fig0025">Fig&#46; 6</a>&#41;&#44;<a class="elsevierStyleCrossRef" href="#bib0360"><span class="elsevierStyleSup">72</span></a> and could replace the existing bulky ultrasound systems&#46; Through ultrasound pulses&#44; these devices capture high-resolution images of arterial flow and of the heart cavities&#44; with a wireless signal transmission&#46; In different studies and tests in healthy volunteers&#44; the patches were seen to adapt well to the skin&#44; making it possible to capture images even when standing up or sitting down&#44; jogging or cycling&#46;</p><elsevierMultimedia ident="fig0025"></elsevierMultimedia><p id="par0200" class="elsevierStylePara elsevierViewall">This adhered ultrasound device generates enhanced-resolution images by combining an elastic adhesive layer with a rigid series of transducers &#40;which convert energy from one form to another&#41;&#46; In between these components&#44; there is a solid hydrogel that transmits sound waves&#44; and the adhesive layer is composed of two thin elastomer films that prevent dehydration of the hydrogel during a certain period&#46;</p><p id="par0205" class="elsevierStylePara elsevierViewall">In the different tests made&#44; the patches have been used in different areas&#44; including the neck&#44; chest&#44; abdomen and arms &#40;Figure S7&#44; Supplementary material&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0365"><span class="elsevierStyleSup">73</span></a> The continuous images of the underlying structures are clear&#44; including the changing diameters and areas of the blood vessels and heart cavities&#44; during periods of up to 48<span class="elsevierStyleHsp" style=""></span>h&#46;<a class="elsevierStyleCrossRefs" href="#bib0370"><span class="elsevierStyleSup">74&#8211;77</span></a></p><p id="par0210" class="elsevierStylePara elsevierViewall">Such software is based not only on artificial intelligence&#44; allowing improved interpretation of the ultrasound images&#44; but also on research into frequency- and ultrasound wave-transmitting materials and gels&#46; Although the future potentials and applications remain to be confirmed&#44; this is a very promising field of research in critical care&#46;</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0055">Conclusions</span><p id="par0215" class="elsevierStylePara elsevierViewall">Artificial intelligence as a tool applied to ultrasound is already a reality&#46; Its evolution in the coming years will make the learning process shorter&#44; allowing faster and more automated use of the exploratory technique&#44; and resulting in more precise monitoring and echocardiographic diagnosis&#46;</p><p id="par0220" class="elsevierStylePara elsevierViewall">Progressive incorporation of the technology will be faster than expected&#44; affording more precise and fundamental information to intensivists on an immediate basis and with a shorter learning curve&#46; Likewise&#44; the development of new software applications will make cardiovascular physiology more comprehensible&#46;</p><p id="par0225" class="elsevierStylePara elsevierViewall">Lastly&#44; we should ponder whether clinicians will be able to quickly apply these continuous improvements in software applications and their reliability to daily practice&#44; and in which ways the new indixes will change our clinical language&#46; The future is undoubtedly fascinating&#46;</p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0060">Author contributions</span><p id="par0230" class="elsevierStylePara elsevierViewall">Fernando Clau-Terr&#233; prepared the manuscript&#39;s first draft&#44; which was subsequently modified by the rest of the authors&#46; Fernando Clau-Terr&#233; likewise provided the edited videos and images&#44; and requested the permissions for their reproduction&#46;</p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Financial support</span><p id="par0235" class="elsevierStylePara elsevierViewall">None&#46;</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Conflicts of interest</span><p id="par0240" class="elsevierStylePara elsevierViewall">The authors declare that they have no conflicts of interest in relation to the commercial companies cited in the article&#44; and have received no compensations of any kind&#46;</p></span></span>"
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        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">The present study highlights the advances in ultrasound&#44; especially regarding its clinical applications to critically ill patients&#46;</p><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Artificial intelligence &#40;AI&#41; is crucial in automating image interpretation&#44; improving accuracy and efficiency&#46; Software has been developed to make it easier to perform accurate bedside ultrasound examinations&#44; even by professionals lacking prior experience&#44; with automatic image optimization&#46; In addition&#44; some applications identify cardiac structures&#44; perform planimetry of the Doppler wave&#44; and measure the size of vessels&#44; which is especially useful in hemodynamic monitoring and continuous recording&#46; The &#34;strain&#34; and &#34;strain rate&#34; parameters evaluate ventricular function&#44; while &#34;auto strain&#34; automates its calculation from bedside images&#46; These advances&#44; and the automatic determination of ventricular volume&#44; make ultrasound monitoring more precise and faster&#46; The next step is continuous monitoring using gel devices attached to the skin&#46;</p></span>"
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        "resumen" => "<span id="abst0010" class="elsevierStyleSection elsevierViewall"><p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">El art&#237;culo destaca los avances en ecograf&#237;a&#44; especialmente en su aplicaci&#243;n cl&#237;nica para pacientes cr&#237;ticos&#46;</p><p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">La inteligencia artificial &#40;IA&#41; juega un papel crucial al automatizar la interpretaci&#243;n de im&#225;genes&#44; mejorando la precisi&#243;n y eficiencia&#46; Se han desarrollado softwares que facilitan la realizaci&#243;n de ex&#225;menes ecogr&#225;ficos precisos a pie de cama&#44; incluso para personal sin experiencia previa&#44; con la optimizaci&#243;n de la imagen de manera autom&#225;tica&#46; Adem&#225;s&#44; existen aplicaciones que identifican estructuras card&#237;acas&#44; planimetran la onda Doppler y miden el tama&#241;o de vasos&#44; que son especialmente &#250;tiles en la monitorizaci&#243;n hemodin&#225;mica y en su registro continuo&#46; Los par&#225;metros &#34;strain&#34; y &#34;strain rate&#34; eval&#250;an la funci&#243;n ventricular&#44; mientras que el &#34;auto strain&#34; automatiza su c&#225;lculo desde im&#225;genes a pie de cama&#46; Estos avances&#44; junto con la determinaci&#243;n autom&#225;tica del volumen ventricular hace que la monitorizaci&#243;n con ultrasonidos sea m&#225;s precisa y r&#225;pida&#46; El siguiente paso es la monitorizaci&#243;n continua por dispositivos geles adheridos a la piel&#46;</p></span>"
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            "apendice" => "<p id="par0255" class="elsevierStylePara elsevierViewall">The following are Supplementary data to this article&#58;<elsevierMultimedia ident="upi0005"></elsevierMultimedia><elsevierMultimedia ident="upi0010"></elsevierMultimedia><elsevierMultimedia ident="upi0015"></elsevierMultimedia><elsevierMultimedia ident="upi0020"></elsevierMultimedia><elsevierMultimedia ident="upi0025"></elsevierMultimedia><elsevierMultimedia ident="upi0030"></elsevierMultimedia><elsevierMultimedia ident="upi0035"></elsevierMultimedia></p>"
            "etiqueta" => "Appendix A"
            "titulo" => "Supplementary data"
            "identificador" => "sec0060"
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          "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Artificial intelligence &#40;AI&#41; can use unprocessed echocardiographic images&#47;videos to automatically provide structural or functional parameters&#44; but it can also identify pathological conditions&#46; Image processing arises from deep analyses based on the pixel&#44; performed repeatedly&#46; This also implies analysis and improvement of image quality in order to extract information&#46;</p>"
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                  \t\t\t\t">The images generated in the echocardiographic study are in a digital format and are stored as such for analysis&#46;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">Preprocessing is performed to improve image quality&#44; including the suppression of noise&#44; the correction of gain and standardization of the images in order to ensure uniform quality&#46;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">Segmentation implies identification and delineation of the cardiac structures of interest&#44; such as the endocardium of the heart cavities&#44; the valves and the walls of the heart&#46;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">The extraction of characteristics implies that once the structures of interest have been identified&#44; AI extracts relevant characteristics from the images &#40;including the measurement of dimensions&#44; blood flow velocities&#44; and metrics&#41;&#46;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">Automatic learning uses convolutional neural networks &#40;CNNs&#41; or classification algorithms to analyze the extracted characteristics and make decisions based on previously known patterns&#46;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">Diagnosis and recommendations imply that AI&#44; based on the analysis of the echocardiographic images&#44; provides preliminary diagnoses&#44; identifies anomalies and measures cardiac parameters to generate recommendations for treatment or follow-up&#46;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">Continuous training benefits AI&#44; with big data sets of echocardiographic examinations and feedback from expert physicians in order to improve its precision over time&#46;&nbsp;\t\t\t\t\t\t\n
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