Patent Publication Number: US-2020297415-A1

Title: Automated electrode recommendation for ablation systems

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This application claims priority to Provisional Application No. 62/822,142, filed Mar. 22, 2019, which is herein incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates generally to systems, devices, and methods involving cardiac ablation. 
     BACKGROUND 
     Various cardiac abnormalities can be attributed to improper electrical activity of cardiac tissue. Such improper electrical activity can include generation of electrical signals, conduction of electrical signals of the tissue, etc., in a manner that does not support efficient and/or effective cardiac function. For example, an area of cardiac tissue may become electrically active prematurely or otherwise out of synchrony during the cardiac cycle, causing the cardiac cells of the area and/or adjacent areas to contract out of rhythm. The result is an abnormal cardiac contraction that is not timed for optimal cardiac output. In some cases, an area of cardiac tissue may provide a faulty electrical pathway (e.g., a short circuit) that causes an arrhythmia, such as atrial fibrillation or supraventricular tachycardia. In some cases, inactive tissue (e.g., scar tissue) may be preferable to malfunctioning cardiac tissue. 
     SUMMARY 
     In Example 1, a computer-implemented method includes recommending, via one or more processors and a graphical user interface, a set of ablation electrodes to activate based, at least in part, on analysis of an image containing the ablation electrodes. 
     In Example 2, the method of Example 1, wherein the analysis includes recognizing the ablation electrodes in the image. 
     In Example 3, the method of any of Examples 1 or 2, wherein the analysis includes comparing contrast of pixels in the image around the ablation electrodes. 
     In Example 4, the method of any of Examples 1-3, wherein the analysis determines which of the ablation electrodes are selectable. 
     In Example 5, the method of any of Examples 1-4, wherein the analysis is carried out by a trained neural network. 
     In Example 6, the method of any of Examples 1-5, wherein the recommending is also based, at least in part, on impedance measurements. 
     In Example 7, the method of Example 6, wherein the impedance measurements determine which of the ablation electrodes are selectable. 
     In Example 8, the method of Example 7, wherein the selectable ablation electrodes are ablation electrodes associated with an impedance measurement within a predetermined range of impedance values. 
     In Example 9, the method of any of Examples 4, 5, 7, and 8, further comprising assigning, via the one or more processors, each of the recommended ablation electrodes to be either a sink or a source. 
     In Example 10, the method of any of Examples 1-9, wherein the recommending is based, at least in part, on balancing estimated power received by source electrodes. 
     In Example 11, the method of any of Examples 1-10, wherein the recommending is based, at least in part, on limiting estimated power received by source electrodes below a predetermined threshold. 
     In Example 12, the method of any of Examples 1-11, wherein the recommended set of ablation electrodes forms a closed circuit. 
     In Example 13, a computing device adapted to execute the steps of the method of Examples 1-12. 
     In Example 14, a computer program product comprising instructions to cause the one or more processors to carry out the steps of the method of Examples 1-12. 
     In Example 15, a computer-readable medium having stored thereon the computer program product of Example 14. 
     In Example 16, an ablation system includes a radiofrequency (RF) generator configured to generate RF energy; an ablation catheter in communication with the RF generator and including a plurality of ablation electrodes; a camera positioned on the ablation catheter and arranged to take an image that includes at least some of the plurality of ablation electrodes; and one or more processors configured to recommend a subset of the plurality of ablation electrodes to be activate ablation electrodes based, at least in part, on the image. 
     In Example 17, the ablation system of Example 16, wherein the recommendation is based, at least in part, on comparing contrast of pixels in the image around the ablation electrodes. 
     In Example 18 the ablation system of any of Examples 16 and 17, wherein the one or more processors is configured to determine which of the ablation electrodes are selectable based, at least in part, on the comparison of pixels in the image. 
     In Example 19, the ablation system of any of Examples 16-18, wherein the recommendation is also based, at least in part, on impedance measurements taken by the plurality of ablation electrodes. 
     In Example 20, the ablation system of any of Examples 16-19, wherein the recommended ablation electrodes are ablation electrodes associated with an impedance measurement within a predetermined range of impedance values. 
     In Example 21, the ablation system of any of Examples 16-20, wherein only some of the plurality of ablation electrodes are selectable, wherein the one or more processors is configured to assign each of the selected ablation electrodes to be either a sink or a source. 
     In Example 22, the ablation system of any of Examples 16-21, wherein the recommendation is based, at least in part, on limiting estimated power received by sink electrodes below a predetermined threshold. 
     In Example 23, the ablation system of any of Examples 16-22, wherein the recommended ablation electrodes form a closed circuit. 
     In Example 24, the ablation system of any of Examples 16-23, wherein the recommendation is based, at least in part, on images of the ablation electrodes taken over multiple cardiac cycles. 
     In Example 25, the ablation system of any of Examples 16-24, wherein the ablation catheter includes an expandable member carrying the plurality of ablation electrodes, wherein the camera is positioned within the expandable member. 
     In Example 26, a computing device for generating and using a graphical user interface (GUI) is disclosed. The computing device includes one or more integrated circuits configured to generate a graphical representation of a plurality of electrodes of an ablation catheter for displaying via the GUI and to automatically highlight a subset of the plurality of electrodes on the GUI as active electrodes based, at least in part, on impedance values associated with each of the plurality of electrodes. 
     In Example 27, the computing device of Example 26, wherein automatically highlighting is further based, at least in part, on analysis of images including the electrodes of the ablation catheter. 
     In Example 28, the computing device of Example 27, wherein the analysis includes comparing contrast of pixels in the images around the electrodes. 
     In Example 29, the computing device of any of Examples 27 and 28, wherein the analysis includes recognizing each of the electrodes in the images. 
     In Example 30, the computing device of any of Examples 27-29, wherein the analysis is carried out, at least partially, by a trained neural network. 
     In Example 31, the computing device of any of Examples 26-30, wherein the one or more integrated circuits is further configured to automatically designate each of the highlighted subset of the plurality of electrodes to be either a source electrode or a sink electrode. 
     In Example 32, the computing device of Example 31, wherein the one or more integrated circuits is further configured to automatically assign each of the source electrodes an amount of energy. 
     In Example 33, the computing device of any of Examples 31 and 32, wherein the assigned amount of energy is based, at least in part, on an estimated power calculated for each of the designated sink electrodes. 
     In Example 34, the computing device of Example 33, wherein the assigned amount of energy is based, at least in part, on balancing the calculated estimated power among the designated sink electrodes. 
     In Example 35, the computing device of any of Examples 26-34, wherein the automatically highlighting is further based, at least in part, determining which of the electrodes of the ablation catheter are selectable. 
     While multiple embodiments are disclosed, still other embodiments of the present invention will become apparent to those skilled in the art from the following detailed description, which shows and describes illustrative embodiments of the invention. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not restrictive. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows an ablation system, in accordance with certain embodiments of the present disclosure. 
         FIG. 2  shows a perspective view of an ablation catheter, in accordance with certain embodiments of the present disclosure. 
         FIGS. 3 and 4  show various views of a graphical user interface, in accordance with certain embodiments of the present disclosure. 
         FIG. 5  shows a schematic representation of an electrode selection process, in accordance with certain embodiments of the present disclosure. 
         FIGS. 6A-C  show images captured by a camera of the ablation catheter of  FIG. 2 , in accordance with certain embodiments of the present disclosure. 
         FIG. 7  shows a schematic representation of features of a neural network, in accordance with certain embodiments of the present disclosure. 
         FIG. 8  shows a block representation of steps in a method for recommending an ablation path, in accordance with certain embodiments of the present disclosure. 
         FIG. 9  shows a schematic representation of electrodes of the ablation catheter of  FIG. 2 , in accordance with certain embodiments of the present disclosure. 
     
    
    
     While the invention is amenable to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and are described in detail below. The intention, however, is not to limit the invention to the particular embodiments described. On the contrary, the invention is intended to cover all modifications, equivalents, and alternatives falling within the scope of the invention as defined by the appended claims. 
     DETAILED DESCRIPTION 
     Cardiac ablation is a procedure during which cardiac tissue is treated to inactivate the tissue. The tissue targeted for ablation may be associated with improper electrical activity, as described above. Cardiac ablation can create lesions in the tissue and prevent the tissue from improperly generating or conducting electrical signals. For example, lesions in the form of a line or a circle can block the propagation of errant electrical signals. Control of the shape, depth, uniformity, etc., of the lesion is desirable. 
     Certain embodiments of the present disclosure involve systems, devices, and methods that can be used in connection with cardiac ablation therapy via ablation electrodes on an ablation catheter. In particular, the present disclosure describes approaches for recommending and/or selecting which ablation electrodes to activate for therapy. The recommended ablation path helps augment the process of determining which electrodes to activate during ablation procedures. Further, the present disclosure describes graphical user interfaces that display and enable control of graphical representations of features of the ablation catheter and can be used for viewing, selecting, and modifying ablation parameters, among other things. 
       FIG. 1  shows an ablation system  100  including an ablation catheter  102  comprising an elongated catheter body  104  and a distal catheter region  106 , which is configured to be positioned within a heart  108 . The ablation catheter  102  includes an expandable member  110  (e.g., membrane, balloon) and a plurality of energy delivery elements  112  (e.g., ablation electrodes) secured to the expandable member  110 . The energy delivery elements  112  are configured and positioned to deliver ablative energy (e.g., radiofrequency (RF) energy) to tissue when the expandable member  110  is inflated. 
     The system  100  includes a RF generator  114  electrically coupled to the plurality of energy delivery elements  112  and configured to generate RF energy. The RF generator  114  includes an RF generator controller  116  configured to control the RF energy to the plurality of energy delivery elements  112 . The RF generator controller  116  can be implemented using firmware, integrated circuits, and/or software modules that interact with each other or are combined together. For example, the RF generator controller  116  may include memory  118  storing computer-readable instructions/code  120  for execution by a processor  122  (e.g., microprocessor) to perform aspects of embodiments discussed herein. 
     The system  100  can also include a computing device  124  (e.g., personal computer) with one or more controllers. Although multiple, distinct controllers are shown in  FIG. 1  and described below, the functions of the various controllers can be implemented in fewer or more controllers (e.g., in multiple modules of a single controller) and/or multiple computing devices. 
     The computing device  124  of  FIG. 1  is shown with a display controller  126 , which is configured to communicate with various components of the system  100  and generate a graphical user interface (GUI) to be displayed via a display  128  (e.g., computer monitor, television, mobile device screen). The display controller  126  can be implemented using firmware, integrated circuits, and/or software modules that interact with each other or are combined together. For example, the display controller  126  may include memory  130  storing computer-readable instructions/code  132  for execution by one or more processors  134  (e.g., microprocessor) to perform aspects of embodiments of methods discussed herein. 
     The computing device  124  can also include a graphics processing unit (GPU)  136  configured to communicate with various components of the system  100 . The GPU  136  can be implemented using firmware, integrated circuits, and/or software modules that interact with each other or are combined together. For example, the GPU  136  may include memory  138  storing computer-readable instructions/code  140  for execution by one or more processors  142  to perform aspects of embodiments of methods discussed herein. The GPU  136  can be configured to access other memory in the computing device  124 . 
     The various components of the system  100  may be communicatively coupled to each other via communication links  144 . In certain embodiments, the communication links  144  may be, or include, a wired communication link (e.g., a serial communication), a wireless communication link such as, for example, a short-range radio link, such as Bluetooth, IEEE 802.11, a proprietary wireless protocol, and/or the like. The term “communication link” may refer to an ability to communicate some type of information in at least one direction between at least two components and may be a persistent communication link, an intermittent communication link, an ad-hoc communication link, and/or the like. The communication links  144  may refer to direct communications between components and/or indirect communications that travel between components via at least one other device (e.g., a repeater, router, hub). 
     In embodiments, the memory  118 ,  130 ,  140  includes computer-readable storage media in the form of volatile and/or nonvolatile memory and may be removable, non-removable, or a combination thereof. Media examples include Random Access Memory (RAM), Read Only Memory (ROM), Electronically Erasable Programmable Read Only Memory (EEPROM), flash memory, and/or any other non-transitory storage medium that can be used to store information and can be accessed by a computing device. In certain embodiments, the ablation catheter  102  includes memory that stores information unique to the ablation catheter  102  (e.g., catheter ID, manufacturer). This information can be accessed and associated with data collected as part of an ablation procedure (e.g., patient data, ablation parameters). 
     The computer-executable instructions  120 ,  132 , and  142  may include, for example, computer code, machine-useable instructions, and the like such as, for example, program components capable of being executed by the one or more processors  122 ,  134 , and  142 . Some or all of the functionality contemplated herein may be implemented in hardware and/or firmware. 
     In certain embodiments, the RF generator  114  and the computing device  124  are separate components housed in a single console  146 . 
       FIG. 2  shows an ablation catheter  200  that can be used in the system  100 . The ablation catheter  200  includes an expandable member  202  and a plurality of energy delivery elements  204  (hereinafter referred to as ablation electrodes) secured to the expandable member  202 . The ablation electrodes  204  are configured and positioned to deliver ablative energy to tissue when the expandable member  202  is inflated. As shown in  FIG. 2 , in certain embodiments, the ablation electrodes  204  are arranged in two rows, one proximal set of ablation electrodes  204  and one distal row of ablation electrodes  204 . Each of the ablation electrodes  204  is individually addressable and/or can be used with any other ablation electrode  204 . The ablation electrodes  204  can operate in a monopolar mode or bipolar mode. Sets of ablation electrodes  204  can be chosen such that the lesion is linear, a spot, a hollow circle, etc. In embodiments utilizing a monopolar mode, the system  100  may include a return pad. 
     The ablation catheter  200  includes a visualization system  206  including a camera assembly  208  and illumination sources (e.g., light-emitting diodes (LEDs)) disposed on a guide wire shaft  210 . The camera assembly  208  can include a plurality of cameras disposed at an angle relative to a longitudinal axis of the ablation catheter  200 . The cameras are configured to enable real-time imaging (e.g., video) of an ablation procedure from within the expandable member  202  including visualizing the expandable member  202 , the ablation electrodes  204 , and cardiac tissue as well as lesion formation during the ablation procedure. The illumination sources provide lighting for the cameras to visualize the ablation procedure. In certain embodiments, the surface of the ablation electrodes  204  facing the cameras is dark colored (e.g., painted black) to help make it easier to identify the ablation electrodes  204  in images (described in more detail below). 
     As mentioned above, the computing device  124  of the system  100  includes the display controller  126  that is configured to communicate with various components of the system  100  and generate a GUI for displaying via the display  128 .  FIGS. 3 and 4  show aspects of a GUI and its various features and views that can be used in the system  100  and displayed via the display  128 . Users can interact (e.g., select icons, enter data) with the GUI using one or more input devices (e.g., mouse, keyboard, touchscreen). The various icons described below can take the form of selectable buttons, indicators, images, etc., on the GUI. 
       FIG. 3  shows a GUI  300  including a first region  302  and a second region  304 . The first region  302  displays a graphical representation  306  of electrodes of an ablation catheter, and the second region  304  displays images  308  (e.g., real-time video) from the ablation catheter. The first region  302  and the second region  304  are shown as being positioned side-by-side and being circular-shaped regions. In certain embodiments, the first region  302  and the second region  304  are separate windows within the GUI  300 . 
     The graphical representation  306  includes a separate electrode icon  310  for each of the plurality of ablation electrodes of the ablation catheter. In certain embodiments, each electrode icon  310  is similarly-shaped to an actual shape of a corresponding electrode on the ablation catheter. Each electrode icon  310  can include a unique numerical indicator  312 . For example, the ablation catheter being represented by the graphical representation  306  includes twelve ablation electrodes in an outer ring and six ablation electrodes in an inner ring, and each of the electrode icons  310  is assigned an integer (e.g.,  1 - 18 ). A user can select or deselect an electrode icon  310  to respectively highlight or un-highlight the electrode icon  310  on the GUI  300 . As will be discussed in more detail below, in certain embodiments, the computing device  124  provides an initial recommendation of which electrodes (and therefore electrode icons  310 ) to activate for a desired ablation path. The initial recommendation can take the form of highlighting certain electrode icons  310  in the GUI  300 . 
     A selected electrode icon  310  will be designated, via the GUI  300 , to be an active electrode (e.g., a source electrode or a sink electrode).  FIG. 4  shows an example graphical representation  306  of electrode icons  310  some of which are selected (i.e., electrodes  1 - 6 , 12 , and  16 - 18 ) to be active electrodes and the rest of the electrode icons  310  unselected such that ablation electrodes associated with such electrode icons  310  will not be active during an ablation procedure. 
     The displayed real-time video  308  allows for visualization of an ablation procedure. The displayed real-time video  308  may include displaying video (e.g., a series of images) recorded by one or more cameras. For example, if an ablation catheter (e.g., the ablation catheter  200  of  FIG. 2 ) includes four cameras, the real-time video  308  may display video recorded from each of the four cameras. In such embodiments, the real-time video  308  can display each of the four fields of view from the cameras overlaid with at least one other field of view. 
     The GUI  300  includes a number of icons (e.g., buttons, images, combinations thereof) that are associated with and can be used to control or monitor aspects of the ablation catheter and the GUI  300  itself. 
       FIG. 3  shows the GUI  300  including icons relating to the graphical representation  306  positioned in or near the first region  302  next to the graphical representation  306 . For example, the GUI  300  includes three icons (i.e., an electrode selection icon  314 , an electrode refresh icon  316 , and a source-sink reverse icon  318 ) positioned next to the graphical representation  306  and that affect features of the ablation system  100 . The electrode selection icon  314  can be used to select a pattern from a pre-determined menu of patterns of electrode icon selections  310  (e.g., inner ring of electrode icons  310 , outer ring, all electrode icons  310 , none). As noted above and described in more detail below, in certain embodiments, the computing device  124  provides an initial recommendation of which electrodes (and therefore electrode icons  310 ) to activate for a desired ablation path. Once a pattern is selected, the selected electrode icons  310  can be highlighted on the GUI  300 . The electrode refresh icon  316  can be used to unselect any electrode icon  310  that has been selected. The source-sink reverse icon  318  can be used in a bipolar mode to reverse which electrode icons  310  correspond to a sink and which electrode icons  310  correspond to a source. 
       FIG. 3  shows the GUI  300  including icons relating to the real-time video  308  positioned in or near the second region  304  next to the real-time video  308 . For example, the GUI  300  includes three icons (i.e., a contrast icon  320 , a luminosity icon  322 , and a video refresh icon  324 ) positioned next to the real-time video  308  and that affect features of the real-time video  308 . The contrast icon  320  can be used to increase or decrease contrast of the real-time video  308 . The luminosity icon  322  can be used to modify illumination power of illumination sources in the ablation catheter. The video refresh icon  324  can be used to refresh the video feed and/or a display controller if the real-time video  308  encounters problems. 
     The GUI  300  includes a ribbon  326  with various icons relating to the ablation catheter and/or the GUI  300  itself. The ribbon  326  includes a status icon  328  indicating the system&#39;s status and a sonic/scan icon  330 , which initiates a routine for initiating an ultrasonic source and for scanning the electrodes on the ablation catheter to identify potentially faulty electrodes. For example, the ablation catheter may be placed in a bath coupled to an ultrasonic source, and once the sonic/scan icon  330  is selected, the routine can turn on the ultrasonic source for a predetermined period of time to remove air bubbles stuck to the ablation catheter before a treatment procedure. After expiration of the predetermined period of time, the routine can sequentially activate all electrodes to determine whether any electrodes or RF amplifiers are defective. If the ultrasonic source is not connected, the sonic/scan icon  330  will just initiate the scanning portion of the routine. 
     The ribbon  326  also includes an in vivo icon  332 , which can be selected to indicate that the ablation catheter has been placed within a patient; an anatomy icon  334 , which can be used to identify the pulmonary vein (e.g., right superior, right inferior, left superior, left inferior) to be treated; a power icon  336 , which displays and allows a user to modify, via arrow buttons, a power level at which the selected ablation electrodes will be energized; a procedure timing icon  338 , which displays and allows a user to modify, via arrow buttons, the length of time the selected ablation electrodes are to be energized; an irrigation flow rate icon  340 , which can be used to control flow rates of irrigation fluid through the ablation catheter; and a fluid volume icon  342 , which indicates the amount of fluid passed through the ablation catheter since the in vivo icon  332  was selected. Once various selections are made via the icons, data associated the selections can be stored in a memory and/or sent to a computing device (e.g., the computing device  124  of  FIG. 1 ). For example, once a flow rate is selected, the selected flow rate can be sent to the computing device  124  to control an irrigation fluid pump. 
     As described above, the GUI  300  allows a user to select, via the electrode icons  310 , which electrodes on the ablation catheter will be active (e.g., either a source electrode or a sink electrode). The selected and highlighted electrode icons  310  indicate that, should an ablation procedure begin, only the ablation electrodes corresponding to the selected and highlighted electrode icons  310  will be active during the ablation procedure.  FIG. 3  shows the GUI  300  including an ablation activate/deactivate icon  344  with text stating “Assign” or one or more similar terms. Once the “Assign” ablation activate/deactivate icon  344  has been selected, the “Assign” text for the ablation activate/deactivate icon  344  is replaced with “Ablate”, “Start”, or one or more similar terms. 
     When the ablation activate/deactivate icon  344  indicates “Assign” and is selected, the computing device  124  assigns or designates the selected electrode icons  310  to be active and either a source or a sink. For example,  FIG. 4  shows the selected electrode icons  310  as being either a source electrode or a sink electrode. In  FIG. 4 , the selected electrode icons  346  numbered “ 12 ”, “ 2 ”, “ 4 ”, “ 6 ”, and “ 17 ” are indicated as being source electrodes while the other selected electrode icons  348  (i.e., those numbered “ 1 ”, “ 3 ”, “ 5 ”, “ 16 ”, and “ 18 ”) are indicated as being sink electrodes. The unselected electrode icons in  FIG. 4  (i.e., those numbered “ 7 ”, “ 8 ”, “ 9 ”, “ 10 ”, “ 11 ”, “ 13 ”, “ 14 ”, and “ 15 ”) are shown without power icons (discussed below) and without impedance values (also discussed below). In certain embodiments, the source electrodes are highlighted with a different color on the GUI  300  or are otherwise shown as being different than the sink electrodes. Further, the unselected electrode icons may be unhighlighted or faded to further visually distinguish the selected electrode icons from the unselected electrode icons. 
       FIG. 4  shows each of the source electrode icons  346  and the sink electrode icons  348  being associated with an impedance value in units of ohms (e.g., electrode icon “ 12 ” indicates 156 ohms, electrode icon “ 1 ” indicates 151 ohms). As will be discussed further below, the impedance values can be an input to determining a recommended ablation path. 
       FIG. 4  also shows each of the source electrode icons  346  and the sink electrode icons  348  including icons (e.g., power icons) indicating electrical units associated with the given electrode. For simplicity, the description below uses power in the form of Watts as the exemplary electrical unit displayed and modified via the GUI  300 , but other electrical units (e.g., various forms of electrical energy) can be displayed and modified in place of power.  FIG. 4  shows the source electrode icons  346  including a source power icon  350 , which displays the power (e.g., 8 Watts) currently assigned to the corresponding ablation electrode. Each source power icon  350  can be selected to display a power selector icon to increase or decrease the power associated with the respective source electrode.  FIG. 4  also shows each of the sink electrodes  348  including a power estimation icon  362 . Each of the power estimation icons  362  displays an estimated power associated with the given sink electrode  348 . The estimated power displayed in the power estimation icons  362  can be based, for example, on the power assigned to each of the source electrodes  346  and distances between the given sink electrode  348  and source electrodes  346 . For example, a source electrode  346  will divide its energy among the sink electrodes  348  but more of its energy will be delivered to sink electrodes  348  positioned closer to the given source electrode  346 . 
     As alluded to above, the computing device  124  can be configured to provide an initial recommendation of which ablation electrodes  204  (and therefore which electrode icons  310  to initially highlight) to activate for an ablation procedure. The recommended path helps augment the process of determining which electrodes to activate for a given ablation procedure. For example, recommending a path can make it easier and/or quicker for a physician to select which ablation electrodes  204  to activate for an ablation procedure. 
     A process  400 ,  500  of recommending a path is shown schematically in  FIG. 5  and outlined in  FIG. 7 . The analysis and steps shown in  FIGS. 5 and 7  and described below can be carried out in parallel or sequentially in various orders. And, in certain embodiments, not all analyses and steps need to be carried out to recommend a path. The process  400 ,  500  involves identifying which ablation electrodes  204  are adequately contacting tissue by visual analysis  402  (e.g., analyzing images collected by one or more cameras on the ablation catheter  200 ) and/or impedance analysis  404  (e.g., analyzing impedance measurements of the ablation electrodes  204  on the ablation catheter  200 ). 
     As mentioned above, the one or more cameras positioned on the ablation catheter  200  record images for display in the GUI  300 . Examples images  406 A-C are shown in  FIGS. 6A-C . The images  406 A-C, which can be taken over multiple cardiac cycles, can be inputted to the computing device  124  and analyzed to determine which of the ablation electrodes  204  are likely to be adequately contacting tissue. The visual analysis  402  can include first identifying or recognizing the ablation electrodes  204  in the images  406 A-C (step  502  in  FIG. 7 ). For example, the images  406 A-C can be analyzed by a neural network  408  (described in more detail below) to determine the position and boundary of any ablation electrodes  204  in the images  406 A-C. As shown in  FIGS. 6A-C , each of the ablation electrodes  202  in the images  406 A-C are surrounded by respective boxes  450 A or  450 B. These boxes  450 A and  450 B visually represent that the neural network  408  has identified or recognized the ablation electrodes  204  in the images  406 A-C. 
     Once the ablation electrodes  204  in the images  406 A-C are identified, the visual analysis  402  can include determining whether the identified ablation electrodes  204  are adequately contacting the tissue (step  504  in  FIG. 7 ). In certain embodiments, the neural network  408  determines a likelihood of contact by comparing contrast of pixels in the images  406 A-C around the identified ablation electrodes  204 . For example, pixels around the ablation electrodes  204  that are associated with a lighter color can indicate contact with tissue while pixels that are associated with a darker color can indicate the presence of blood between one of the ablation electrodes  204  and tissue. In  FIGS. 6A-6C , the boxes associated with reference number  450 A are those that the neural network  408  has determined to include ablation electrodes  204  likely be in contact with tissue, while the other boxes  450 B indicate ablation electrodes  204  that are not likely in contact with tissue. 
     In certain embodiments, the output of the visual analysis  402  is a list of ablation electrodes that are selectable for activation based on which of the ablation electrodes  204  are likely have adequate contact with tissue. In addition, each ablation electrode  204  included in the list can be associated with a level of contact or level of confidence of contact. For example, visual analysis  402  may indicate that certain ablation electrodes  204  likely have better tissue contact than other ablation electrodes  204  such as the ablation electrodes  204  surrounded by pixels with lighter color. 
     The neural network  408 , generally speaking, is a computational model based on structures and functions of biological neural networks. The neural network  402  can be implemented under a variety of approaches, including a convolutional neural network (CNN) approach, among others. CCNs evaluate data (e.g., images) in the form of multiple arrays, breaking the data into a series of stages and examining the data for learned features.  FIG. 8  is a simplified visual representation of an example implementation of an image analysis CNN  600 . The CNN  600  (and therefore the neural network  402 ) can include additional features (e.g., layers). 
     An image  602  is inputted to the CNN  600 , which abstracts the image  602  in a first convolution layer  604  to identify learned features. In a second convolution layer  606 , the image  602  is transformed into a plurality of images in which the learned features are each accentuated in respective sub-images  608 . The images sub-images  608  are further processed to focus on features of interest, and further processing isolates portions  510  of the images including the features of interest. The output layer  612  of the CNN  500  receives values from the last non-output layer and classifies the features of interest based on the data received from the last non-output layer. 
     The neural network  408  can be “trained” using supervised or unsupervised approaches. For example, a set of “training data” (e.g., known inputs and known outputs) can be used to train the neural network  408 . Using a supervised approach, the input training data can be images collected from an ablation catheter that are classified with output data (e.g., the existence/boundary of ablation electrodes; an indication of whether the ablation electrodes appear to contact tissue). The known inputs and outputs are fed into an untrained neural network or a partially-trained neural network (e.g., a neural network trained to recognize objects but not necessarily ablation electrodes), which processes that data to train itself to resolve/compute results for additional sets of data with new inputs and unknown outputs. Using unsupervised approaches, the input training data can similarly be images collected from an ablation catheter which, instead of manual or semi-automatic classifying, are compared against actual lesion characteristics resulting from the active ablation electrodes in the images. As a result, under supervised or unsupervised approaches, the trained neural network can predict outputs (e.g., existence/boundary of ablation electrodes along with an indication of whether the ablation electrodes appear to contact tissue) from a set of new inputs (e.g., new images taken from an ablation catheter). 
     The process  400 / 500  further includes the impedance analysis  404  where impedance values measured by the ablation electrodes  204  are analyzed (step  506  in  FIG. 7 ). The impedance values can be collected and inputted to the computing device  124 . For example, the GUI  300  can include a button that initiates a routine that involves activating the ablation electrodes  204  for a period of time to determine the impedance values measured by each ablation electrode  204 . The computing device  124  can compare the measured impedance values to a predetermined threshold range to determine which ablation electrodes  204  are likely to be adequately contacting tissue. For example, if the predetermined threshold range is 50-250 ohms, ablation electrodes  204  measuring impedance values below 50 ohms or above 250 ohms will be determined to not be adequately contacting tissue. The computing device  124  determines a list of ablation electrodes  204  that measure impedance values within the predetermined threshold range. 
     The computing device  124  uses the outputs of the vision-based analysis and the impedance-based analysis to determine a list  410  of ablation electrodes  204  that are selectable for activation (step  508  in  FIG. 7 ). In certain embodiments, selectable ablation electrodes must be determined to have adequate contact with tissue under both the vision-based analysis  402  and the impedance-based analysis  404 . For example, if the vision-based analysis  402  determines that a given ablation electrodes  204  is likely contacting tissue but that same ablation electrode  204  is associated with an impedance outside the predetermined threshold range, that ablation electrode  204  will be determined to be un-selectable. In certain embodiments, additional analysis can be used to generate a list of selectable ablation electrodes  204 . For example, if one of the ablation electrodes  204  is determined to be defective during the sonic/scan routine, that ablation electrode  204  can be identified as an being un-selectable. The list of selectable electrodes  410  is used as an input to ablation path analysis  412 , which the computing device  124  uses to determine a recommended ablation path (step  510  in  FIG. 7 ). 
     Because not all theoretically-possible paths are valid or desirable paths, the computing device  124  can apply various rules and/or preferences to eliminate certain paths and/or give more or less weight to certain paths as part of determining the recommended ablation path. One example rule is that two adjacent electrodes cannot both be sources, so paths with adjacent sources are eliminated from consideration when this rule is applied. Another example rule is that sinks cannot be associated with an estimated power value above a predetermined threshold, so paths resulting in too much power at one or more of the ablation electrodes  204  are eliminated from consideration when this rule is applied. For the uneliminated paths, certain preferences can be applied to give more or less weight to paths with preferred characteristics. An example preference is that the sinks in a given path would have similar (e.g., balanced) estimated power values, so paths resulting in similar estimated power values are weighted higher than others. Another example preference is that the recommended ablation path is “closed” (i.e., forms a closed circuit with a circumferential shape) rather than “open,” so such paths are weighted higher than others. Another example preference could include weighting the visual-based analysis differently than the impedance-based analysis. As mentioned above, the visual analysis may include values indicating the estimated level of contact of each selectable ablation electrodes  204 . Ablation electrodes  204  associated with higher levels of contact can be given a higher preference in the ablation path analysis  412 . 
     Given the constraints and preferences, the computing device  124  can determine a recommended ablation path. In certain embodiments, the computing device  124  applies graph theory approaches to determine the recommended ablation paths. When applying graph theory, the ablation electrodes can be represented as nodes (or vertices) connected by edges (e.g., undirected edges) in the model. For example,  FIG. 9  shows a graph  700  of ablation electrodes  204  connected by edges  702  (only some of which are associated with reference number  702  in  FIG. 9 ). Like the electrode icons  310  in the GUI  300 , each of the ablation electrodes  204  are represented and shown to have a unique numerical identifier (e.g., “ 1 ”, “ 2 ”, through “ 18 ”). Applying graph theory, the constraints, and the preferences, the computing device  124  can determine a recommended ablation path. In certain embodiments, the computing device  124  can apply another trained neural network (e.g., using multilayer feedforward network approaches, recurrent neural network approaches) to determine a recommended ablation path. 
     The recommended ablation path from the ablation path analysis  412  can be communicated to the display controller  126 . The display controller  126  can cause certain electrode icons  310  to become highlighted in the GUI  300  like that shown in  FIG. 4 . The highlighted electrode icons  310  are those associated with the ablation electrodes  204  within the recommended ablation path. In addition, the computing device  124  can automatically assign which ablation electrodes are sources and which are sinks and assign a recommended power value to the sources. 
     The recommended ablation path and associated parameters (e.g., sources, sinks, power) can be modified by a user via the GUI  300 . Once the ablation path and associated parameters are established, the user can select the ablation activate/deactivate icon  344  (see  FIG. 3 ). Selecting the ablation activate/deactivate icon  344  initiates and/or stops energy delivery to the ablation electrodes  204  of the ablation catheter  200 . Once the activate/deactivate icon  344  is pressed to initiate energy delivery, a graphic in the activate/deactivate icon  344  changes (e.g., changes to a stop sign). Further, once the activate/deactivate icon  344  is selected to initiate energy delivery, a signal is transmitted to an RF generator (e.g., the RF generator  114  of  FIG. 1 ) and/or an RF generator controller (e.g., the RF generator controller  116  of  FIG. 1 ) to start delivering energy to the selected ablation electrodes  204  of the ablation catheter  200 . 
     Various modifications and additions can be made to the exemplary embodiments discussed without departing from the scope of the present invention. For example, while the embodiments described above refer to particular features, the scope of this invention also includes embodiments having different combinations of features and embodiments that do not include all of the described features. Accordingly, the scope of the present invention is intended to embrace all such alternatives, modifications, and variations as fall within the scope of the claims, together with all equivalents thereof.