Optimized Ablation for Persistent Atrial Fibrillation

Abstract

To optimize ablation.SOLUTION: A method and apparatus of aiding a physician in locating an area for performing ablation on patients with atrial fibrillation (AFIB) includes receiving data at a machine, from at least one device, the data including information relating to a desired location for performing the ablation, generating, by the machine, an optimal location for performing the ablation based upon the data and inputs, and providing an optimal set of ablation parameters for performing the ablation at the location output by the model, or at a location specified by the physician.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims the benefit of U.S. Provisional Patent Application No. 63 / 048,830, filed July 7, 2020, which is incorporated by reference as if fully set forth.

[0002] FIELD OF THE INVENTION The present invention relates to artificial intelligence and machine learning associated with optimizing the ablation of persistent atrial fibrillation (AFIB). [Background technology]

[0003] Atrial arrhythmias are significant contributors to cardiac comorbidities, particularly stroke, heart failure, and recurrent hospitalizations. For some arrhythmias (e.g., typical atrial flutter), procedural (ablation) workflows have been established within the EP community. For other arrhythmias (e.g., persistent atrial fibrillation), procedural (ablation) workflows (other than anatomy-based pulmonary vain isolation (PVI)) have not necessarily been established due to the complexity of this type of arrhythmia. While various characteristics exist to guide physicians to where to perform ablation in patients with atrial fibrillation (AFIB), no single characteristic can currently reliably predict the optimal location for ablation. Summary of the Invention [Means for solving the problem]

[0004] A method and apparatus for assisting a physician in locating an area to perform ablation on a patient with atrial fibrillation (AFIB) includes receiving data at a machine from at least one device, the data including information regarding a desired location for performing the ablation; generating, by the machine, an optimal location for performing the ablation based on the data and input; and providing an optimal set of ablation parameters for performing the ablation at the location output by a model or at the location specified by the physician. [Brief explanation of the drawings]

[0005] A more detailed understanding may be had from the following description, given by way of example in conjunction with the accompanying drawings, in which like reference numerals indicate similar elements and in which: [Figure 1] FIG. 1 is a block diagram of an exemplary system for remotely monitoring and communicating patient biometric indicators. [Figure 2] FIG. 1 is a system diagram of an example computing environment in communication with a network. [Figure 3] FIG. 1 is a block diagram of an example device capable of implementing one or more features of the present disclosure. [Figure 4] 4 shows a graphical depiction of an artificial intelligence system incorporating the exemplary device of FIG. 3. [Figure 5] 5 illustrates a method implemented in the artificial intelligence system of FIG. [Figure 6] Here is an example of a naive Bayes calculation probability: [Figure 7] 1 illustrates an exemplary decision tree. [Figure 8] 1 illustrates an exemplary random forest classifier. [Figure 9] 1 shows an exemplary logistic regression. [Figure 10] An exemplary support vector machine is shown. [Figure 11] 1 illustrates an exemplary linear regression model. [Figure 12]1 illustrates an exemplary K-means clustering. [Figure 13] 1 illustrates an exemplary ensemble learning algorithm. [Figure 14] 1 illustrates an exemplary neural network. [Figure 15] 1 shows a hardware-based neural network. [Figure 16] FIG. 1 is a diagram of an exemplary system capable of implementing one or more features of the subject matter of this disclosure. [Figure 17] FIG. 1 is a cardiac map of an exemplary retrospective study with locations where a physician performed ablation. [Figure 18] FIG. 1 is a schematic diagram of an exemplary workflow for performing ablation. [Figure 19] FIG. 1 is an exemplary diagram of an exemplary schematic diagram using machine learning to assist a physician in locating regions to perform ablation on a patient with atrial fibrillation (AFIB). [Figure 20] FIG. 1 is an exemplary diagram of an exemplary method of using machine learning to assist a physician in locating regions to perform ablation on a patient with AFIB. [Figure 21] 1 shows data preparation and training of the system. [Figure 22] 1 illustrates an exemplary composite architecture of the present system. [Figure 23] 1 illustrates an exemplary composite architecture of the present system. DETAILED DESCRIPTION OF THE INVENTION

[0006] The details of this application are described herein, but briefly, machine learning (ML) is used to assist physicians in locating regions to perform ablation on patients with atrial fibrillation (AFIB).

[0007] A method and apparatus for assisting a physician in locating an area to perform ablation on a patient with AFIB includes receiving data at a machine from at least one device, the data including information regarding a desired location for performing the ablation; generating, by the machine, an optimal location for performing the ablation based on the data and input; and providing an optimal set of ablation parameters for performing the ablation at the location output by the model or at the location specified by the physician.

[0008] 1 is a block diagram of an exemplary system 100 for remotely monitoring and communicating patient biometrics (i.e., patient data). In the example shown in FIG. 1, the system 100 includes a patient biometric monitoring and processing unit 102 associated with a patient 104, a local computing device 106, a remote computing system 108, a first network 110, and a second network 120.

[0009] According to one embodiment, the monitoring and processing device 102 may be a device internal to the patient's body (e.g., subcutaneously implantable). The monitoring and processing device 102 may be inserted into the patient via any applicable method, including oral injection, surgical insertion via a vein or artery, endoscopic procedure, or laparoscopic procedure.

[0010] According to one embodiment, the monitoring and processing device 102 may be a device external to the patient. For example, as described in more detail below, the monitoring and processing device 102 may include an attachable patch (e.g., attached to the patient's skin). The monitoring and processing device 102 may also include a catheter with one or more electrodes, a probe, a blood pressure cuff, a weight scale, a bracelet or smartwatch biometric tracker, a glucose monitor, a continuous positive airway pressure (CPAP) machine, or virtually any device that can provide input regarding the patient's health or biometrics.

[0011] According to one embodiment, the monitoring and processing device 102 may include both components internal to the patient and components external to the patient.

[0012] A single monitoring and processing device 102 is shown in Figure 1. However, an exemplary system may include multiple patient biometric monitoring and processing devices. A patient biometric monitoring and processing device may be in communication with one or more other patient biometric monitoring and processing devices. Additionally or alternatively, a patient biometric monitoring and processing device may be in communication with a network 110.

[0013] One or more monitoring and processing devices 102 may acquire patient biometric data (e.g., electrical signals, blood pressure, body temperature, blood glucose levels, or other biometric data) and may receive at least a portion of the patient biometric data representing the acquired patient biometric indicators, as well as additional information associated with the acquired patient biometric indicators from one or more other monitoring and processing devices 102. The additional information may be, for example, diagnostic information and / or additional information obtained from additional devices, such as wearable devices. Each monitoring and processing device 102 may process data including its own acquired patient biometric indicators as well as data received from one or more other monitoring and processing devices 102.

[0014] 1, network 110 is an example of a short-range network (e.g., a local area network (LAN) or a personal area network (PAN)). Information may be transmitted over short-range network 110 between monitoring and processing equipment 102 and local computing device 106 using any one of a variety of short-range wireless communication protocols, such as Bluetooth, Wi-Fi, Zigbee, Z-Wave, near field communication (NFC), Ultraband, Zigbee, or infrared (IR).

[0015] Network 120 may be a wired network, a wireless network, or may include one or more wired and wireless networks. For example, network 120 may be a long-range network (e.g., a wide area network (WAN), the Internet, or a cellular network). Information may be transmitted over network 120 using any one of a variety of long-range wireless communication protocols (e.g., TCP / IP, HTTP, 3G, 4G / LTE, or 5G / New Radio).

[0016] The patient monitoring and processing device 102 may include patient biometric sensors 112, a processor 114, user input (UI) sensors 116, memory 118, and a transmitter-receiver (i.e., transceiver) 122. The patient monitoring and processing device 102 may continuously or periodically monitor, store, process, and communicate any number of various patient biometric indicators over the network 110. Examples of patient biometric indicators include electrical signals (e.g., ECG signals and brain biometrics), blood pressure data, blood glucose data, and temperature data. The patient biometric indicators may be monitored and communicated for treatment across any number of various diseases, such as cardiovascular diseases (e.g., arrhythmias, cardiomyopathies, and coronary artery disease) and autoimmune diseases (e.g., type 1 and type 2 diabetes).

[0017] The patient biometric sensors 112 may include, for example, one or more sensors configured to sense a type of biometric patient biometric indicator. For example, the patient biometric sensors 112 may include electrodes configured to acquire electrical signals (e.g., cardiac signals, brain signals, or other bioelectrical signals), a temperature sensor, a blood pressure sensor, a blood glucose sensor, a blood oxygen sensor, a pH sensor, an accelerometer, and a microphone).

[0018] As described in more detail below, the patient biometric monitoring and processing device 102 may be an ECG monitor for monitoring cardiac ECG signals. The patient biometric sensor 112 of the ECG monitor may include one or more electrodes for acquiring the ECG signals. The ECG signals may be used in the treatment of various cardiovascular disorders.

[0019] In another example, the patient biometric device 102 may be a continuous glucose monitor (CGM) for continuously monitoring a patient's blood glucose levels on an ongoing basis to treat various diseases, such as type 1 and type 2 diabetes. The CGM may include subcutaneously placed electrodes that can monitor blood glucose levels from the patient's interstitial fluid. The CGM may be a component of a closed-loop system in which blood glucose data is sent to an insulin pump, for example, for calculated delivery of insulin without user intervention.

[0020] The transceiver 122 may include a separate transmitter and receiver, or alternatively, the transceiver 122 may include a transmitter and receiver integrated into a single device.

[0021] The processor 114 may be configured to store patient data, such as patient biometric data acquired by the patient biometric sensors 112, in the memory 118 and to communicate the patient data over the network 110 via the transmitter of the transceiver 122. Data from one or more other monitoring and processing devices 102 may also be received by the receiver of the transceiver 122, as described in more detail below.

[0022] According to one embodiment, the monitoring and processing device 102 includes a UI sensor 116, which may include a piezoelectric or capacitive sensor configured to receive user input, such as a tap or touch. For example, the UI sensor 116 may be controlled to implement capacitive coupling in response to the patient 104 tapping or touching the surface of the monitoring and processing device 102. Gesture recognition can be implemented via any one of a variety of capacitive types, such as resistive-capacitive, surface-capacitive, projected-capacitive, surface acoustic wave, piezoelectric, and infrared touch. The capacitive sensor may be positioned over a small area or length of the surface such that a tap or touch on the surface activates the monitoring device.

[0023] As described in more detail below, processor 114 may be configured to selectively respond to different tapping patterns (e.g., single tap or double tap) of a capacitive sensor, which may be UI sensor 116, so that different tasks of the patch (e.g., acquiring, storing, or transmitting data) may be initiated based on the detected pattern. In some embodiments, audible feedback may be provided to the user from processing unit 102 when a gesture is detected.

[0024] The local computing device 106 of the system 100 may be configured to communicate with the patient biometric monitoring and processing device 102 and act as a gateway to the remote computing system 108 via a second network 120. The local computing device 106 may be, for example, a smartphone, smartwatch, tablet, or other portable smart device configured to communicate with other devices via the network 120. Alternatively, the local computing device 106 may be a fixed or standalone device, such as, for example, a fixed base station including modem and / or router capabilities, a desktop or laptop computer using an executable program to communicate information between the processing device 102 and the remote computing system 108 via a wireless module in a PC, or a USB dongle. Patient biometrics may be communicated between the local computing device 106 and the patient biometric monitoring and processing device 102 via a short-range wireless network 110, such as a local area network (LAN) (e.g., a personal area network (PAN)), using short-range wireless technology standards (e.g., Bluetooth, Wi-Fi, ZigBee, Z-Wave, and other short-range wireless standards). In some embodiments, the local computing device 106 may also be configured to display the acquired patient electrical signals and information related to the acquired patient electrical signals, as described in more detail below.

[0025] In some embodiments, the remote computing system 108 may be configured to receive at least one of the monitored patient's biometric indicators and information associated with the monitored patient over the network 120, which is a long-range network. For example, if the local computing device 106 is a cellular phone, the network 120 may be a wireless cellular network, and information may be communicated between the local computing device 106 and the remote computing system 108 via a wireless technology standard, such as any of the wireless technologies mentioned above. As described in more detail below, the remote computing system 108 may be configured to provide (e.g., visually display and / or audibly provide) at least one of the patient's biometric indicators and associated information to a medical professional (e.g., a physician).

[0026] 2 is a system diagram of an example computing environment 200 in communication with network 120. In some examples, computing environment 200 is incorporated into a public cloud computing platform (such as Amazon Web Services or Microsoft Azure), a hybrid cloud computing platform (such as HP Enterprise OneSphere), or a private cloud computing platform.

[0027] As shown in FIG. 2, computing environment 200 includes a remote computing system 108 (hereinafter computer system), which is an example of a computing system on which embodiments described herein may be implemented.

[0028] The remote computing system 108 can perform various functions via the processor 220, which may include one or more processors. Functions may include analyzing monitored patient biometrics and related information and providing alerts, additional information, or instructions (e.g., via the display 266) according to physician-determined or algorithm-driven thresholds and parameters. As described in more detail below, the remote computing system 108 can be used to provide a patient information dashboard (e.g., via the display 266) to a medical professional (e.g., a physician) so that the patient information may enable the medical professional to identify and prioritize patients with more significant needs than others.

[0029] 2, computer system 210 may include a communication mechanism, such as a bus 221, or other communication mechanism for communicating information within computer system 210. Computer system 210 further includes one or more processors 220 coupled with bus 221 for processing information. Processor 220 may include one or more CPUs, GPUs, TPUs, or any other processors known in the art.

[0030] Computer system 210 also includes a system memory 230 coupled to bus 221 for storing information and instructions executed by processor 220. System memory 230 may include computer-readable storage media in the form of volatile and / or nonvolatile memory, such as read-only system memory (ROM) 231 and / or random access memory (RAM) 232. System memory RAM 232 may also include other dynamic storage device(s) (e.g., dynamic RAM, static RAM, and synchronous DRAM). System memory ROM 231 may also include other static storage device(s) (e.g., programmable ROM, erasable PROM, and electrically erasable PROM). Additionally, system memory 230 may be used to store temporary variables or other intermediate information during execution of instructions by processor 220. A basic input / output system (BIOS) 233 may include routines for transferring information that may be stored in system memory ROM 231 between elements within computer system 210, such as during start-up. RAM 232 may contain data and / or program modules that are immediately accessible to and / or presently being operated on by processor 220. System memory 230 may also include, for example, an operating system 234, application programs 235, other program modules 236, and program data 237.

[0031] The illustrated computer system 210 also includes a disk controller 240 coupled to bus 221 for controlling one or more storage devices for storing information and instructions, such as a magnetic hard disk 241 and a removable media drive 242 (e.g., a floppy disk drive, a compact disk drive, a tape drive, and / or a solid state drive). Storage devices may be added to computer system 210 using an appropriate device interface (e.g., small computer system interface (SCSI), integrated device electronics (IDE), Universal Serial Bus (USB), or FireWire).

[0032] Computer system 210 may also include a display controller 265 coupled to bus 221 to control a monitor or display 266, such as a cathode ray tube (CRT) or liquid crystal display (LCD), for displaying information to a computer user. The illustrated computer system 210 includes a user input interface 260 and one or more input devices, such as a keyboard 262 and a pointing device 261, for interacting with a computer user and providing information to processor 220. Pointing device 261 may be, for example, a mouse, trackball, or pointing stick for communicating directional information and command selections to processor 220 and for controlling cursor movement on display 266. Display 266 may provide a touchscreen interface that may enable input that complements or replaces the communication of directional information and command selections by pointing device 261 and / or keyboard 262.

[0033] Computer system 210 may perform some or each of the functions and methods described herein in response to processor 220 executing one or more sequences of one or more instructions contained in a memory, such as system memory 230. Such instructions may be read into system memory 230 from another computer-readable medium, such as hard disk 241 or removable media drive 242. Hard disk 241 may include one or more data stores and data files used by the embodiments described herein. Data store contents and data files may be encrypted for improved security. Processor 220 may also be employed in a multi-processing configuration to execute one or more sequences of instructions contained in system memory 230. In alternative embodiments, hardwired circuitry may be used in place of or in combination with software instructions. Thus, embodiments are not limited to any specific combination of hardware circuitry and software.

[0034] As mentioned above, computer system 210 may include at least one computer-readable medium or memory for retaining programmed instructions according to the embodiments described herein and for containing the data structures, tables, records, or other data described herein. As used herein, the term “computer-readable medium” refers to any non-transitory, tangible medium that participates in providing instructions to processor 220 for execution. Computer-readable media may take many forms, including, but not limited to, non-volatile media, volatile media, and transmission media. Non-limiting examples of non-volatile media include optical disks, solid-state drives, magnetic disks, and magneto-optical disks, such as hard disk 241 or removable media drive 242. Non-limiting examples of volatile media include dynamic memory, such as system memory 230. Non-limiting examples of transmission media include coaxial cables, copper wire, and fiber optics, including the wires that comprise bus 221. Transmission media can also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.

[0035] The computing environment 200 may further include a computer system 210 operating in a networked environment using logical connections to the local computing device 106 and to one or more other devices, such as a personal computer (laptop or desktop), a mobile device (e.g., a patient mobile device), a server, a router, a network PC, a peer device, or other common network node, and typically includes many or all of the elements described above with respect to the computer system 210. When used in a networked environment, the computer system 210 may include a modem 272 for establishing communications over a network 120, such as the Internet. The modem 272 may be connected to the system bus 221 via a network interface 270 or another appropriate mechanism.

[0036] Network 120 as shown in FIGS. 1 and 2 may be any network or system commonly known in the art, including the Internet, an intranet, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a direct connection or series of connections, a cellular telephone network, or any other network or medium capable of facilitating communication between computer system 610 and other computers (e.g., local computing device 106).

[0037] 3 is a block diagram of an example device 300 capable of implementing one or more features of the present disclosure. The device 300 may be, for example, a local computing device 106. The device 300 may include, for example, a computer, a gaming device, a handheld device, a set-top box, a television, a mobile phone, or a tablet computer. The device 300 includes a processor 302, a memory 304, a storage device 306, one or more input devices 308, and one or more output devices 310. The device 300 may also optionally include an input driver 312 and an output driver 314. It is understood that the device 300 may include additional components not shown in FIG. 3 , including an artificial intelligence accelerator.

[0038] In various alternatives, processor 302 includes a central processing unit (CPU), a graphics processing unit (GPU), a CPU and a GPU located on the same die, or one or more processor cores, each of which may be a CPU or a GPU. In various alternatives, memory 304 is located on the same die as processor 302 or is located separate from processor 302. Memory 304 includes volatile or non-volatile memory, such as random access memory (RAM), dynamic RAM, or a cache.

[0039] The storage device 306 includes fixed or removable storage means, such as a hard disk drive, solid state drive, optical disk, or flash drive. The input device 308 includes, but is not limited to, a keyboard, keypad, touch screen, touchpad, detector, microphone, accelerometer, gyroscope, biometric scanner, or network connection (e.g., a wireless local area network card for transmitting and / or receiving wireless IEEE 802 signals). The output device 310 includes, but is not limited to, a display, speaker, printer, haptic feedback device, one or more lights, antenna, or network connection (e.g., a wireless local area network card for transmitting and / or receiving wireless IEEE 802 signals).

[0040] Input driver 312 communicates with processor 302 and input device 308, allowing processor 302 to receive input from input device 308. Output driver 314 communicates with processor 302 and output device 310, allowing processor 302 to send output to output device 310. Note that input driver 312 and output driver 314 are optional components; device 300 operates in the same manner if input driver 312 and output driver 314 are not present. Output driver 316 includes an accelerated processing device ("APD") 316 coupled to display device 318. The APD accepts computational and graphic rendering commands from processor 302, processes those computational and graphic rendering commands, and provides pixel output to display device 318 for display. As described in further detail below, APD 316 includes one or more parallel processing units that perform computations according to the single-instruction-multiple-data ("SIMD") paradigm. Thus, while various functions are described herein as being performed by or in conjunction with the APD 316, in various alternatives, the functions described as being performed by the APD 316 are additionally or alternatively performed by other computing devices that are not driven by a host processor (e.g., the processor 302) and have similar capabilities to provide graphical output to the display device 318. For example, it is contemplated that any processing system that performs processing tasks according to the SIMD paradigm can perform the functions described herein. Alternatively, it is contemplated that computing systems that do not perform processing tasks according to the SIMD paradigm perform the functions described herein.

[0041] FIG. 4 shows a graphical depiction of an artificial intelligence system 200 incorporating the exemplary device of FIG. 3 . The system 400 includes data 410, a machine 420, a model 430, multiple outcomes 440, and underlying hardware 450. The system 400 operates by training the machine 420 using the data 410 while building a model 430 that enables the multiple outcomes 440 to be predicted. The system 400 may operate on the hardware 450. In such a configuration, the data 410 may be associated with the hardware 450, e.g., originating from the device 102. For example, the data 410 may be ongoing data or output data associated with the hardware 450. The machine 420 may operate as or be associated with a controller or data collection associated with the hardware 450. The model 430 may be configured to model the operation of the hardware 450 as well as model the data 410 collected from the hardware 450 to predict outcomes achieved by the hardware 450. The hardware 450 may be configured to use the predicted outcome 440 to provide a predetermined desired outcome 440 from the hardware 450 .

[0042] Figure 5 illustrates a method implemented in the artificial intelligence system 500 of Figure 4. Method 500 includes collecting data from hardware at step 510. This data may include currently collected historical data or other data from the hardware. For example, this data may include measurements taken during a surgical procedure and may be correlated with the outcome of the procedure. For example, cardiac temperature may be collected and correlated with the outcome of a cardiac procedure.

[0043] At step 520, method 500 includes training the machine on the hardware. Training may include analyzing and correlating the data collected at step 510. For example, in the cardiac case, temperature and outcome data may be trained to determine if a correlation or association exists between cardiac temperature during treatment and outcome.

[0044] Method 500 includes building a model based on the hardware-related data at step 530. Building the model may include physical hardware or software modeling, algorithmic modeling, etc., as described below. The modeling may aim to represent the collected and trained data.

[0045] Method 500 includes predicting an outcome for a hardware-related model at step 540. This outcome prediction may be based on a trained model. For example, for the heart, if a procedure results in a positive outcome when the temperature during the procedure is between 97.7 and 100.2, then for a given procedure, the outcome may be predicted based on the temperature of the heart during the procedure. This model is rudimentary and is provided for illustrative purposes to facilitate understanding of the present invention.

[0046] The present systems and methods operate to train machines, build models, and predict outcomes using algorithms. These algorithms may be used to solve the trained models and predict outcomes related to the hardware. These algorithms can generally be categorized into classification algorithms, regression algorithms, and clustering algorithms.

[0047] For example, classification algorithms are used in situations where the dependent variable to be predicted is divided into multiple classes and one class, i.e., the dependent variable, is predicted for a given input. Thus, classification algorithms are used to predict outcomes from a number of fixed, predefined outcomes. Classification algorithms may include naive Bayes algorithms, decision trees, random forest classifiers, logistic regression, support vector machines, and k-nearest neighbors.

[0048] In general, the Naive Bayes algorithm follows Bayes' Theorem and follows a probabilistic approach, it being understood that other probability-based algorithms may be used and generally operate using similar principles of probability theory as those described below for the exemplary Naive Bayes algorithm.

[0049] Figure 6 shows an example of a naive Bayes calculation of probability. The probabilistic approach of Bayes' theorem essentially means that instead of jumping directly to the data, the algorithm has a set of prior probabilities for each target class. After the data is input, the naive Bayes algorithm may update the prior probabilities to form posterior probabilities. This is done using the following formula:

[0050]

number

[0051] The Naive Bayes algorithm and the Bayes algorithm in general can be useful when one needs to predict whether an input belongs to a given list of n classes or not. Because the probability of all n classes is very low, a probabilistic approach can be used.

[0052] For example, as shown in FIG. 6, a person's decision to play golf depends on factors including the weather outside, as shown in a first data set 610. The first data set 610 lists the weather in a first column and the play outcomes associated with that weather in a second column. A frequency table 620 generates the frequency with which a particular event occurs. The frequency table 620 determines how often a person will or will not play golf in each weather condition. From this, a likelihood table is compiled and initial probabilities are generated. For example, the probability that the weather is cloudy is 0.29, but the general probability of playing is 0.64.

[0053] Posterior probabilities may be generated from likelihood table 630. These posterior probabilities may be configured to answer questions about weather conditions and whether golf will be played in those weather conditions. For example, the probability that it is sunny outside and golf will be played can be calculated using the Bayesian formula: P(Yes|Sunny) = P(Sunny|Yes) × P(Yes) / P(Sunny) It may be represented by: According to likelihood table 630, P(sunny|yes) = 3 / 9 = 0.33, P(sunny)=5 / 14=0.36, P(yes) = 9 / 14 = 0.64 is. Therefore, P(yes|clear) = 0.33 × 0.64 / 0.36 or approximately 0.60 (60%).

[0054] Generally, a decision tree is a tree structure similar to a flowchart, where each outer node represents a test of an attribute and each branch represents the result of that test. The leaf nodes contain the actual predicted label. A decision tree starts at the root of the tree and attribute values ​​are compared until a leaf node is reached. Decision trees can be used as classifiers when dealing with high-dimensional data and when little time is spent on data preparation. Decision trees can take the form of simple decision trees, linear decision trees, algebraic decision trees, deterministic decision trees, randomized decision trees, non-deterministic decision trees, and quantum decision trees. An exemplary decision tree is shown below in Figure 7.

[0055] 7 shows a decision tree that follows the same structure as the Bayesian example above when deciding whether to play golf. In the decision tree, a first node 710 examines whether the weather is sunny 712, cloudy 714, and rainy 716 as options for progressing down the decision tree. If the weather is sunny, the tree branch continues to a second node 720 that examines the temperature. In this example, the temperature at node 720 can be high 722 or normal 724. If the temperature at node 720 is high 722, a predicted outcome of "No Golf" 723 occurs. If the temperature at node 720 is normal 724, a predicted outcome of "Yes Golf" 725 occurs.

[0056] Furthermore, from the first node 710, the results Cloudy 714, Golf "Yes" 715 are generated.

[0057] From the first node, weather 710, a third node 730 (again) checks the temperature as a result of rain 716. If the temperature is normal 732 at the third node 730, the answer is "yes" 733 to play golf. If the temperature is low 734 at the third node 730, the answer is "no" 735 to not play golf.

[0058] From this decision tree, a golfer will play golf when it is cloudy 715, when it is sunny with normal temperature 725, and when it is rainy with normal temperature 733, but this golfer will not play when it is hot with sunny weather 723 or cold with rain 735.

[0059] A random forest classifier is a committee of decision trees, where each tree is given a subset of the data's attributes and makes a prediction based on that subset. The mode of the decision trees' actual predictions is taken into account to provide the final random forest answer. Random forest classifiers generally mitigate the overfitting that exists in standalone decision trees, making them more robust and accurate classifiers.

[0060] FIG. 8 illustrates an exemplary random forest classifier for classifying clothing color. As shown in FIG. 8, the random forest classifier includes five decision trees 8101, 8102, 8103, 8104, and 8105 (collectively or generally referred to as decision tree 810). Each of the trees is designed to classify clothing color. Because the individual trees generally operate as the decision trees of FIG. 7, a discussion of each of the trees and the decisions made is not provided. In this illustration, three of the five trees (8101, 8102, 8104) determine that the clothing is blue, one determines that the clothing is green (8103), and the remaining tree determines that the clothing is red (8105). The random forest receives these actual predictions of the five trees and calculates the mode of the actual predictions to provide the random forest's answer that the clothing is blue.

[0061] Logistic regression is another algorithm for binary classification tasks. It is based on the logistic function, also known as the sigmoid function. This S-shaped curve can take any real-valued value and map it between 0 and 1, asymptotically approaching these bounds. Logistic models can be used to model the probability of a particular class or event being present, such as pass / fail, win / lose, alive / dead, or healthy / sick. This can be extended to model several classes of events, such as determining whether an image contains a cat, dog, lion, etc. Each object detected in an image is assigned a probability between 0 and 1, and these probabilities sum to 1.

[0062] In a logistic model, the log odds (log of the odds) of a value labeled "1" are a linear combination of one or more independent variables ("predictors"), each of which can be binary (two classes coded by indicator variables) or continuous (any real-valued value). The corresponding probability of a value labeled "1" is labeled as such because it can vary between 0 (definitely a value of "0") and 1 (definitely a value of "1"). The logistic function is so named because it converts log odds to probabilities. The unit of measurement for the log odds scale is called a logit, another name for the logistic unit. Similar models, such as probit models, can also be used, but with a sigmoid function instead of the logistic function. A distinctive feature of the logistic model is that increasing one of the independent variables multiplicatively scales the odds of a given outcome by a constant factor, and each independent variable has its own parameters. For binary dependent variables, this generalizes the odds ratio.

[0063] In a binary logistic regression model, the dependent variable has two levels (categories). Outputs with more than two values ​​are modeled by multinomial logistic regression, and if the categories are ordered, they are modeled by ordinal logistic regression (e.g., proportional odds ordinal logistic model). Although a logistic regression model itself simply models the probability of the output with respect to the inputs and does not perform statistical classification (it is not a classifier), it can be used to create a classifier, for example, by selecting a cutoff value and classifying inputs with probabilities greater than the cutoff as one class and inputs with probabilities less than the cutoff as the other class. This is a common way to create a binary classifier.

[0064] FIG. 9 illustrates an exemplary logistic regression. This exemplary logistic regression allows for the prediction of an outcome based on a set of variables. For example, a school's acceptance outcome can be predicted based on an individual's grade point average. The relationship between past history of grade point average and acceptance allows for predictions. The logistic regression of FIG. 9 allows analysis of the grade point average variable 920 to predict an outcome 910 defined between 0 and 1. At the lower end of the S-curve 930, the grade point average 920 predicts an outcome 910 of not being accepted. At the upper end of the S-curve 940, the grade point average 920 predicts an outcome 910 of being accepted. Logistic regression can be used to predict home values, customer lifetime value in the insurance sector, etc.

[0065] A support vector machine (SVM) can be used to sort the data by making the margin between the two classes as far apart as possible. This is called margin-maximizing separation. Unlike linear regression, which uses the entire dataset for its purpose, SVM is able to take support vectors into account while plotting the hyperplane.

[0066] FIG. 10 illustrates an exemplary support vector machine. In the exemplary SVM 1000, data can be classified into two distinct classes, represented as squares 1010 and triangles 1020. The SVM 1000 operates by drawing a random hyperplane 1030. This hyperplane 1030 is monitored by comparing the distance (shown by line 1040) between the hyperplane 1030 and the nearest data points 1050 from each class. The data points 1050 closest to the hyperplane 1030 are known as support vectors. The hyperplane 1030 is drawn based on these support vectors 1050, with the optimal hyperplane having the greatest distance from each support vector 1050. The distance between the hyperplane 1030 and the support vectors 1050 is known as the margin.

[0067] The SVM 1000 may be used for data classification by using the hyperplane 1030 to maximize the distance between the hyperplane 1030 and the support vectors 1050. Such an SVM 1000 may be used, for example, to predict heart disease.

[0068] k Nearest Neighbors (KNN) generally refers to a set of algorithms that make no assumptions about the underlying data distribution and perform reasonably short training phases. Generally, KNN uses a large number of data points divided into multiple classes to predict the classification of a new sample point. Operationally, KNN specifies an integer N with the new sample. The N entries in a model of the system that are closest to the new sample are selected. The most common classification of these entries is determined, and that classification is assigned to the new sample. KNN generally requires increasing storage space as the training set grows. This also means that estimation time increases linearly with the number of training points.

[0069] In regression algorithms, the output is continuous, so regression algorithms can be used when the target variable is a continuous variable. Linear regression is a common example of a regression algorithm. Linear regression can be used to measure true quality (such as housing costs, number of calls, or total transactions) by considering consistent variables. The connection between the variables and the outcome is found by fitting a line of best fit (hence the name linear regression). This line of best fit is known as the regression line and is expressed directly in terms Y=a×X+b. Linear regression is most often used in low-dimensional approaches.

[0070] 11 shows an exemplary linear regression model, in which a predictor variable 1110 is modeled against a measurement variable 1120. Clusters of instances of the predictor variable 1110 and the measurement variable 1120 are plotted as data points 1130. The data points 1130 are then fitted to a best-fit line 1140. Subsequent predictions then use the best-fit line 1140 to predict the predictor variable 1110 given the measurement variable 1120. Linear regression can be used to model and predict financial portfolios, salary forecasts, real estate, and transportation estimated arrival times.

[0071] Clustering algorithms may be used to model and train datasets. In clustering, inputs are assigned to two or more clusters based on feature similarity. Clustering algorithms typically learn patterns and useful insights from data without guidance. For example, unsupervised learning algorithms such as K-means clustering can be used to cluster audiences into similar groups based on interests, age, geography, etc.

[0072] K-means clustering is generally considered a simple unsupervised learning approach. It allows similar data points to be clustered together and bound together in the form of clusters. One way to bind data points together is by calculating the centroid of the data points. To determine effective clusters, K-means clustering evaluates the distance of each point from the cluster centroid. Depending on the distance between the data point and the centroid, the data is assigned to the closest cluster. The goal of clustering is to determine the unique groupings of a set of unlabeled data. The "K" in K-means represents the number of clusters formed. The number of clusters (essentially the number of classes into which new instances of data can be classified) can be determined by the user. This determination can be made, for example, using feedback and looking at the size of the clusters during training.

[0073] K-means is primarily used when the dataset has distinct and well-separated points; otherwise, the modeling may render the clusters inaccurate if they are not separated. Also, K-means can be avoided if the dataset contains a large number of outliers or if the dataset is non-linear.

[0074] FIG. 12 illustrates K-means clustering. In K-means clustering, data points are plotted and assigned a K value. For example, for K=2 in FIG. 12, the data points are plotted as shown in depiction 1210. Next, in step 1220, the points are assigned to similar centers. Cluster centroids are identified as shown in 1230. Once the centroids are identified, the points are reassigned to clusters such that the distance between the data points and the centroid of each cluster is minimized, as shown in 1240. New centroids of the clusters can then be determined as shown in depiction 1250. Once the data points have been reassigned to clusters and new centroids of the clusters have been formed, an iteration or series of iterations can occur to minimize the size of the clusters and determine the optimal centroid. Next, as new data points are measured, the new data points can be compared to the centroids and clusters and identified with those clusters.

[0075] Ensemble learning algorithms may be used. These algorithms use multiple learning algorithms to achieve better predictive performance than would be possible from any of the constituent learning algorithms alone. Ensemble learning algorithms perform the task of searching a hypothesis space to find suitable hypotheses that make good predictions for a particular problem. Even if the hypothesis space contains hypotheses that are highly suitable for a particular problem, finding a suitable hypothesis can be very difficult. Ensemble algorithms combine multiple hypotheses to form a better one. The term ensemble is typically used to refer to methods that generate multiple hypotheses using the same base learner. The broader concept of a multi-classifier system also encompasses the hybridization of hypotheses that are not derived from the same base learner.

[0076] Because evaluating the predictions of an ensemble typically requires more computation than evaluating the predictions of a single model, ensembles can be thought of as a way to compensate for poor learning algorithms by performing a lot of extra computation. Fast algorithms such as decision trees are commonly used in ensemble methods such as random forests, but slower algorithms can also benefit from ensemble methods.

[0077] Ensembles are themselves supervised learning algorithms because they can be used to make predictions after training. A trained ensemble therefore represents a single hypothesis. However, this hypothesis is not necessarily contained within the hypothesis space of the model from which it was constructed. Thus, ensembles can be shown to have more flexibility in the functions they can represent. This flexibility theoretically allows them to overfit the training data more than a single model, but in practice, some ensemble methods (especially bagging) tend to reduce the problems associated with overfitting the training data.

[0078] Empirically, ensemble algorithms tend to produce better results when there is a significant degree of diversity among the models. Therefore, many ensemble methods aim to promote diversity among the models they combine. While counterintuitive, more random algorithms (such as random decision trees) can be used to generate more powerful ensembles than highly cautious algorithms (such as entropy-reducing decision trees). However, the use of a variety of powerful learning algorithms has been shown to be more effective than using techniques that attempt to simplify models to promote diversity.

[0079] The number of component classifiers in an ensemble has a significant impact on the accuracy of prediction. This becomes even more important for online ensemble classifiers, in order to determine a priori the size of the ensemble and the volume and velocity of the big data stream. Theoretical frameworks suggest that there is an ideal number of component classifiers in an ensemble, and that having more or fewer classifiers than this number will result in a decrease in accuracy. Theoretical frameworks also suggest that using the same number of independent component classifiers as class labels will result in the highest accuracy.

[0080] Some common types of ensembles include Bayesian optimal classifiers, bootstrap aggregation (bagging), boosting, Bayesian model averaging, Bayesian model combination, and model bucketing and stacking. Figure 13 shows an exemplary ensemble learning algorithm in which bagging is performed in parallel (1310) and boosting is performed sequentially (1320).

[0081] A neural network is a network or circuit of neurons, or in the modern sense, an artificial neural network, composed of artificial neurons or nodes. The connections of biological neurons are modeled as weights. Positive weights reflect excitatory connections, while negative values ​​imply inhibitory connections. The inputs are modified by the weights and summed using linear combinations. An activation function can control the amplitude of the output. For example, the allowed range of the output is usually 0 to 1, but can also be -1 to 1.

[0082] These artificial networks can be used in predictive modeling, adaptive control, and other applications, and can be trained through datasets. Self-learning resulting from experience can occur within the network, allowing it to draw conclusions from complex and seemingly unrelated sets of information.

[0083] For completeness, a biological neural network consists of a group of chemically connected or functionally associated neurons. One neuron may be connected to many other neurons, and the total number of neurons and connections in the network may vary widely. Connections, called synapses, are usually formed from axons to dendrites, although dendritic synapses and other connections are also possible. Apart from electrical signaling, other forms of signaling result from the diffusion of neurotransmitters.

[0084] Artificial intelligence, cognitive modeling, and neural networks are information processing paradigms inspired by the way biological nervous systems process data. Artificial intelligence and cognitive modeling attempt to simulate some of the properties of biological neural networks. In the field of artificial intelligence, artificial neural networks have been successfully applied to speech recognition, image analysis, and adaptive control, and to build software agents or autonomous robots (in computer and video games).

[0085] A neural network (NN) is an interconnected group of natural or artificial neurons that uses a mathematical or computational model based on a connectionist approach to computation for information processing, in the case of artificial neurons, called an artificial neural network (ANN) or simulated neural network (SNN). In most cases, ANNs are adaptive systems that change their structure based on external or internal information flowing through the network. More practically, neural networks are nonlinear statistical data modeling or decision-making tools. They can be used to model complex relationships between inputs and outputs and to find patterns in data.

[0086] Artificial neural networks comprise networks of simple processing elements (artificial neurons) that can exhibit complex global behavior determined by the connections between the processing elements and element parameters.

[0087] One classic type of artificial neural network is the recurrent Hopfield network. The utility of artificial neural network models lies in the fact that they can be used to estimate and use functions from observations. Unsupervised neural networks can also be used to learn representations of inputs that capture salient features of the input distribution, and more recently, in deep learning algorithms that can implicitly learn distribution functions for observed data. Training with neural networks is particularly useful in applications where the complexity of the data or the task makes it impossible to design such functions manually.

[0088] Neural networks can be used in a variety of fields. Tasks to which artificial neural networks are applied tend to span the following broad categories: function approximation or regression analysis, including time series prediction and modeling; pattern and sequence recognition; classification, including novelty detection and sequential decision making; data processing, including filtering, clustering, blind signal separation, and compression.

[0089] Areas of application of ANNs include identification and control of nonlinear systems (vehicle control, process control), game playing and decision making (backgammon, chess, racing), pattern recognition (radar systems, face identification, object recognition), sequence recognition (gesture, speech, handwritten text recognition), medical diagnosis, financial applications, data mining (or knowledge discovery in databases, or "KDD"), visualization, and email spam filtering. For example, it is possible to create semantic profiles of user interests resulting from photographs trained for object recognition.

[0090] 14 shows an exemplary neural network. The neural network has an input layer represented by multiple inputs, such as 14101 and 14102. The inputs 14101, 14102 are fed into a hidden layer, shown as including nodes 14201, 14202, 14203, and 14204. These nodes 14201, 14202, 14203, and 14204 are combined to produce output 1430 in the output layer. While neural networks perform simple processing through a hidden layer of simple processing elements, nodes 14201, 14202, 14203, and 14204, these nodes can exhibit complex global behavior determined by the connections between the processing elements and element parameters.

[0091] The neural network of Figure 14 may be implemented in hardware. Referring to Figure 15, a hardware-based neural network is shown.

[0092] Cardiac arrhythmias, and atrial fibrillation in particular, remain common and dangerous conditions, particularly in the elderly population. In patients with normal sinus rhythm, the heart, consisting of atria, ventricles, and excitatory conduction tissue, is electrically excited to beat in a synchronous, patterned manner. In patients with cardiac arrhythmias, abnormal regions of cardiac tissue do not follow the synchronous beating cycle associated with normal conduction tissue, as in patients with normal sinus rhythm. In contrast, abnormal regions of cardiac tissue conduct abnormally to adjacent tissue, disrupting the cardiac cycle and resulting in asynchronous cardiac rhythms. Such abnormal conduction has previously been known to occur in various regions of the heart, such as the region of the sinoatrial (SA) node along the conduction pathways of the atrioventricular (AV) node and the bundle of His, or in the myocardial tissue forming the walls of the ventricles and atria.

[0093] Cardiac arrhythmias, including atrial arrhythmias, can be multiwavelet-reentrant, characterized by multiple asynchronous loops of electrical impulses scattered around the atria, often self-propagating. Alternatively, or in addition to multiwavelet-reentrant, cardiac arrhythmias can also have a local origin, such as when isolated regions of atrial tissue become autonomously excited in a rapid, repetitive manner.

[0094] Atrial fibrillation, a type of arrhythmia, occurs when the normal electrical impulses generated by the sinoatrial node are overwhelmed by disorganized electrical impulses originating in the atria and pulmonary veins, causing irregular impulses to be conducted to the ventricles. The resulting irregular heartbeat can persist for minutes to weeks or even years. Atrial fibrillation (AF) is often a chronic condition that carries a small increased risk of death, often from stroke. Risk increases with age. Approximately 8% of people over the age of 80 have some degree of AF. While often asymptomatic and generally not fatal in itself, AF can lead to palpitations, weakness, fainting, chest pain, and congestive heart failure. The risk of stroke increases during AF because blood can pool in the inefficiently contracting atria and left atrial appendage, potentially forming clots. The first-line treatment for AF is medication to slow the heart rate or restore normal heart rhythm. Furthermore, patients with AF are often given anticoagulants to protect against the risk of stroke. The use of such anticoagulants carries its own risks: internal bleeding. In some patients, medication is insufficient, and AF is deemed drug-refractory, meaning it is untreatable with standard pharmacological interventions. Synchronized electrical cardioversion can also be used to convert AF to a normal cardiac rhythm. Some patients with AF are treated with catheter ablation.

[0095] Catheter ablation-based treatments may involve mapping the electrical properties of cardiac tissue, particularly the endocardium and cardiac volumes, and selectively ablating the cardiac tissue through the application of energy. For example, cardiac mapping, such as generating an electrical potential map (voltage map) of wave propagation along cardiac tissue or a map of arrival times to where various tissues are located (local time activation (LAT) map), may be used to detect local dysfunction in cardiac tissue. Ablation, such as that based on cardiac mapping, can stop or modify the propagation of unwanted electrical signals from one part of the heart to another.

[0096] Ablation techniques disrupt unwanted electrical pathways by creating non-conductive lesions. Various energy delivery modalities have been previously disclosed for creating lesions, including the use of microwaves, lasers, irreversible electroporation with pulsed field ablation, and more commonly, radiofrequency energy to create conduction blocks along cardiac tissue walls. In a two-stage procedure, mapping followed by ablation, electrical activity at each point within the heart is typically sensed and measured by advancing a catheter containing one or more electrical sensors (or electrodes) into the heart and acquiring data at multiple points. These data are then used to select a target region of the endocardium where ablation will be performed.

[0097] Cardiac ablations and other cardiac electrophysiology procedures are becoming increasingly complex as physicians treat challenging conditions such as atrial fibrillation and ventricular tachycardia. Treatment of refractory arrhythmias currently relies on the use of three-dimensional (3D) mapping systems to reconstruct the anatomy of the cardiac chamber of interest.

[0098] For example, cardiologists rely on software such as the Complex Fractionated Atrial Electrogram (CFAE) module of the CARTO® 3 3D mapping system manufactured by Biosense Webster, Inc. (Diamond Bar, California) to analyze intracardiac EGM signals and determine ablation points for treating various cardiac disorders, including atypical atrial flutter and ventricular tachycardia.

[0099] There are mapping algorithms commonly used to map afib and there are mapping algorithms used to map VT. For afib, different mapping algorithms can include, but are not limited to, CFAE, ripple frequency, cycle length mapping, CARTOFINDER focal point, CARTOFINDER rotor, and low voltage zone. Other maps can include cycle length map, Carto-Finder® focal map, Carto-Finder® rotation, ripple map 4, CFAE, and ECG fractionation, as described in more detail with respect to FIG. 18. 3D maps can provide multiple pieces of information regarding the electrophysiological properties of tissues that represent the anatomical and functional substrates of these challenging arrhythmias.

[0100] Electrode catheters have been commonly used in medical practice for many years. They are used to stimulate and map electrical activity within the heart and to ablate sites of abnormal electrical activity. In use, an electrode catheter is inserted into a major vein or artery, such as the femoral artery, and then guided into a target heart chamber. A typical ablation procedure involves inserting a catheter with at least one electrode at its distal end into a heart chamber. A reference electrode is typically provided by a second catheter taped to the patient's skin or positioned within or near the heart. When RF (radio frequency) current is applied to the tip electrode of the ablation catheter, current flows through the medium (i.e., blood and tissue) surrounding the tip electrode toward the reference electrode. The distribution of the current depends on the amount of electrode surface in contact with the tissue compared to blood, which has a higher electrical conductivity than tissue. Tissue heating occurs due to the electrical resistance of the tissue. Sufficient tissue heating can cause cell destruction in the cardiac tissue, resulting in lesions within the non-conductive cardiac tissue. During this process, the electrode also heats due to conduction from the heated tissue to the electrode itself. If the electrode temperature becomes high enough, possibly exceeding 60°C, a thin, transparent film of dehydrated blood proteins can form on the electrode's surface. As the temperature continues to rise, this dehydrated layer can gradually thicken, causing blood to coagulate on the electrode surface. Because dehydrated biological material has a higher electrical resistance than endocardial tissue, the impedance to the flow of electrical energy into the tissue also increases. If the impedance becomes high enough, an impedance rise occurs, requiring the catheter to be removed from the body and the tip electrode to be cleaned.

[0101] FIG. 16 is a diagram of an example system 1620 capable of implementing one or more features of the presently disclosed subject matter. All or a portion of the system 1620 can be used to collect information for a training dataset and / or to implement a trained model. The system 1620 can include a component, such as a catheter 1640, configured to injure a tissue region of an internal organ. The catheter 1640 can also be further configured to acquire biometric data. While the catheter 1640 is shown as being a point catheter, it will be understood that any shape of catheter including one or more elements (e.g., electrodes) can be used to implement the embodiments disclosed herein. The system 1620 includes a probe 1621 having a shaft that can be navigated by a physician 1630 into a body part, such as a heart 1626, of a patient 1628 reclining on a table 1629. Multiple probes may be provided in various embodiments, and while a single probe 1621 is described herein for brevity, it will be understood that the probe 1621 may represent multiple probes. 16 , a physician 1630 can insert the shaft 1622 through the sheath 1623 while manipulating the distal end of the shaft 1622 using a remote control 1632 near the proximal end of the catheter 1640 and / or a deflector from the sheath 1623. As shown in inset 1625, the catheter 1640 can be attached to the distal end of the shaft 1622. The catheter 1640 can be inserted through the sheath 1623 in a collapsed state and then expanded within the heart 1626. As further disclosed herein, the catheter 1640 can include at least one ablation electrode 1647 and a catheter needle 1648.

[0102] According to exemplary embodiments, catheter 1640 may be configured to ablate a tissue region of a chamber of heart 1626. Inset 1645 shows a close-up of catheter 1640 inside a chamber of heart 1626. As shown, catheter 1640 may include at least one ablation electrode 1647 coupled to the body of the catheter. According to other exemplary embodiments, elements may be connected via splines that define the shape of catheter 1640. One or more other elements (not shown) may be provided and may be any element configured to perform ablation or acquire biometric data, such as an electrode, a transducer, or one or more other elements.

[0103] According to embodiments disclosed herein, an ablation electrode, such as electrode 1647, may be configured to deliver energy to a tissue region of a body organ, such as heart 1626. The energy may be thermal energy and may cause damage to the tissue region starting at the surface of the tissue region and extending through the thickness of the tissue region.

[0104] According to exemplary embodiments disclosed herein, the biological data may include one or more of LAT, electrical activity, topology, bipolar mapping, dominant frequency, impedance, etc. The local activation time may be the time point of a threshold activation corresponding to local activation calculated based on a normalized initial onset. The electrical activity may be any applicable electrical signal that can be measured based on one or more thresholds and may be detected and / or enhanced based on signal-to-noise ratio and / or other filters. The topology may correspond to the physical structure of a body part or a portion of a body part, or may correspond to changes in the physical structure for different portions of the body part or for different body parts. The dominant frequency may be a frequency or range of frequencies prevalent in a portion of a body part, and may differ in different portions of the same body part. For example, the dominant frequency of the pulmonary veins of a heart may be different from the dominant frequency of the right atrium of the same heart. The impedance may be a resistance measurement in a given region of a body part.

[0105] 16 , the probe 1621 and catheter 1640 can be connected to a console 1624. The console 1624 can include a processor 1641, such as a general-purpose computer with suitable front-end and interface circuitry 1638, for transmitting and receiving signals to and from the catheter, as well as for controlling other components of the system 1620. In some embodiments, the processor 1641 can be further configured to receive biometric data, such as electrical activity, and determine whether a given tissue region conducts electricity. According to one embodiment, the processor can be external to the console 1624, for example, located in the catheter, an external device, a mobile device, a cloud-based device, or can be a stand-alone processor.

[0106] As noted above, processor 1641 may include a general-purpose computer, which can be programmed with software to perform the functions described herein. The software may be downloaded in electronic form to the general-purpose computer, for example, over a network, or alternatively or additionally, may be provided and / or stored on non-transitory tangible media, such as magnetic, optical, or electronic memory. The exemplary configuration shown in FIG. 16 may be modified to implement embodiments disclosed herein. Embodiments of the present disclosure may be similarly applied using other system components and configurations. Additionally, system 1620 may include additional components, such as elements for sensing electrical activity, wired or wireless connectors, processing and display devices, etc.

[0107] According to one embodiment, a display connected to a processor (e.g., processor 1641) may be located at a remote location, such as a separate hospital or a separate healthcare provider network. Additionally, system 1620 may be part of a surgical system configured to obtain anatomical and electrical measurements of a patient's organs, such as the heart, and to perform cardiac ablation procedures. One example of such a surgical system is the Carto® system sold by Biosense Webster.

[0108] System 1620 can also, and optionally, acquire biometric data, such as anatomical measurements of the patient's heart, using ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), or other medical imaging techniques known in the art. System 1620 can acquire electrical measurements using a catheter, an electrocardiogram (EKG), or other sensors that measure the electrical properties of the heart. The biometric data, including the anatomical and electrical measurements, may then be stored in memory 1642 of mapping system 1620, as shown in FIG. 16 . The biometric data may be transmitted from memory 1642 to processor 1641. Alternatively, or in addition, the biometric data may be transmitted using network 1662 to server 1660, which may be local or remote.

[0109] The network 1662 may be any network or system commonly known in the art, such as an intranet, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a direct or series of connections, a cellular telephone network, or any other network or medium capable of facilitating communication between the mapping system 1620 and the server 1660. The network 1662 may be wired, wireless, or a combination thereof. Wired connections may be implemented using Ethernet, Universal Serial Bus (USB), RJ-11, or any other wired connection commonly known in the art. Wireless connections may be implemented using Wi-Fi, WiMAX, Bluetooth, infrared, cellular networks, satellite, or any other wireless connection method commonly known in the art. Additionally, several networks may operate alone or in communication with each other to facilitate communication within the network 1662.

[0110] In some cases, server 1662 may be implemented as a physical server. In other cases, server 1662 may be implemented as a virtual server, a public cloud computing provider (e.g., Amazon Web Services (AWS)).

[0111] The control console 1624 may be connected by a cable 1639 to body surface electrodes 1643, which may include adhesive skin patches that are affixed to the patient 1630. A processor in conjunction with the current tracking module may determine position coordinates of the catheter 1640 within the patient's body part (e.g., the heart 1626). The position coordinates may be based on impedance or electromagnetic fields measured between the body surface electrodes 1643 and electrodes 1648 or other electromagnetic components of the catheter 1640. Additionally or alternatively, a location pad may be placed on the surface of the bed 1629 or may be separate from the bed 1629.

[0112] Processor 1641 may include real-time noise reduction circuitry, typically configured as a field programmable gate array (FPGA), followed by an analog-to-digital (A / D) ECG (electrocardiograph) or EMG (electromyogram) signal conversion integrated circuit. Processor 1641 may communicate signals from the A / D ECG or EMG circuitry to another processor and / or may be programmed to perform one or more functions disclosed herein.

[0113] The control console 1624 may also include an input / output (I / O) communication interface that allows the control console to communicate signals to and / or from the electrodes 1647 .

[0114] During a procedure, the processor 1641 facilitates the presentation of the body part rendering 1635 to the physician 1630 on the display 1627 and can store data representing the body part rendering 1635 in the memory 1642. The memory 1642 may comprise any suitable volatile and / or non-volatile memory, such as a random access memory or a hard disk drive. In some embodiments, the medical professional 1630 may be able to manipulate the body part rendering 1635 using one or more input devices, such as a touchpad, a mouse, a keyboard, a gesture recognizer, or the like. For example, the input device may be used to change the position of the catheter 1640 so that the rendering 1635 is updated. In an alternative embodiment, the display 1627 may include a touchscreen, which may be configured to receive input from the medical professional 1630 in addition to presenting the body part rendering 1635.

[0115] Described herein is an aid to physicians in locating areas to perform ablation procedures on patients with AFIB.

[0116] When a physician initiates an ablation procedure, there are certain areas or regions that are more optimal for performing ablation than others. A model can be trained using inputs that output the optimal locations for the physician to perform ablation. For example, a mask is effectively generated, but it is not known which mask will provide the physician with the best information for performing the ablation. Therefore, a map of the actual ablation points can be taken as input to train an ML model from the mask to generate optimal ablation points.

[0117] The machine learning model can be trained by utilizing one of more of the following: cardiac maps, EGM data, contact force data, respiration data, tissue proximity data, ablation locations, ablation parameters, and treatment outcomes of retrospective procedures. Once trained, the machine learning model can predict optimal ablation locations and parameters given inputs such as cardiac maps and EGM data. In some cases, location may be important, and in other cases, parameters may be important. That is, the physician provides the desired ablation location, and given the cardiac map and desired ablation location, the system can infer optimal ablation parameters. Additionally, the system can predict success rates (e.g., 60 percent, 70 percent, etc.) at various ablation locations.

[0118] To train the system, acute results from retrospective cases can be fed into the machine learning model. Results after a blanking period of several days can be used. Results can also be tracked for long-term results, and the long-term results from retrospective cases can be fed into the machine learning model to continue training the model.

[0119] 17 is an illustration of an exemplary retrospective cardiac map 1700 having locations 1710 where a physician performed ablation. As shown in FIG. 17, multiple ablation points 1710 may have been performed using different parameters and each utilizing various strategies. Information obtained from the data discussed below associated with these ablation points 1710 can be provided to the model to assist it in learning and associating data with each outcome, allowing prediction and suggestion of current or future ablation locations and expected success at specific locations and parameters for use in ablation.

[0120] FIG. 18 is a schematic diagram of an exemplary workflow 1800 for performing ablation. Workflow 1800 includes multiple inputs 1810, such as a cycle length map 1810.1, a Carto-Finder® focal map 1810.2, a Carto-Finder® rotation 1810.3, a ripple map 1810.4, a CFAE 1810.5, and an ECG fractionation 1810.6. Other inputs 1810 may also be included, as represented as input 1810.7 in FIG. 18. As can also be seen in FIG. 18, information 1820 may be provided to an AI-based model 1830 (e.g., anatomy mesh 1820.1, mesh coloring / tags 1820.2, ablation data 1820.3, clinical outcomes and follow-up 1820.4). Each piece of information 1820 is a mapping that may include a value (real number) for each location point on the 3D mesh and may further include the start and end times of the period during which the catheter was located at that point. Thus, this timing information 1820 may include information that different points in the 3D mapping were calculated based on measurements made at different times. Furthermore, if the value for a particular point was triangulated by an algorithm rather than obtained from the catheter's passage at that point, the triangulation of the relevant time points can be used.

[0121] Input 1810 may include patient-related parameters such as age, sex, physical dimensions (of the body and / or heart and atria), medical history including medication use, and type of atrial fibrillation (one of several classes: paroxysmal, persistent, or chronic).

[0122] For example, model 1830, provided with input 1810 and information 1820, can output ablation locations 1840 as well as parameters. The output, including ablation locations 1840, is a 3D mapping of the mesh, with each point having a score between 0 and 1 indicating the strength of the recommendation as to whether the point is a good candidate for ablation.

[0123] FIG. 19 is an exemplary diagram of a schematic 1900 illustrating assisting a physician in identifying the location of an area to perform ablation on a patient with AFIB. The schematic 1900 uses machine learning (generally 1910) to allow a physician to provide an ablation location 1920.2 and not provide an ablation location 1920.1. The cardiac map and EGM signals 1920.1, 1920.2 may be provided to a machine, which can query the machine for optimal ablation locations and parameters 1930.1, 1930.2. The machine may also be provided with potential or desired ablation locations 1920.2, which may provide ablation parameters 1930.2 for the provided locations. The machine may also calculate the expected success of the ablation 1930.1, 1930.2.

[0124] The following provides further details regarding exemplary embodiments. A high-density basket catheter can be utilized to collect intracardiac EGMs from all LAs for a specific time period (e.g., 1 minute). The system can be configured to provide information to the neural network if afib terminates immediately or several days after the event, and other ablations involving the physician can also be provided to the neural network. Such delayed reporting can capture afib situations, including those in which afib does not terminate immediately but requires a multi-day recovery period for complete resolution of the arrhythmia. In such procedures, it may be difficult to identify which ablation site was the critical site, but it is known that one of the ablation sites was the critical site. The neural network can be trained to detect the critical ablation site. For example, given two patients with similar cardiac activity, if the physician for the first patient performed ablations at locations A and B and the physician for the second patient performed ablations at locations B and C, the neural network can learn that this activity is strongly correlated with B (and not A and C).

[0125] Data clusters for recommending an ablation strategy can include, for example, ripple frequency maps (ripple percentage and peak), fragmentation index, cycle length maps, CFAE, Finder, fractionation maps, complexity maps, voltage maps, clinical ablation parameters (site, index, etc.), and clinical outcomes (acute and / or after a blanking period of several days and / or after long-term follow-up).

[0126] The model can be trained on laboratory and clinical outcomes to validate successful cases. Data for input in training the machine model includes maps generated during a specific physician's ablation procedure, ablation data collected during the procedure, ablation catheter type, 3D location of the ablation point, power used for ablation, point ablation duration, irrigation, catheter stability, parameters for the region of ablation to validate transmural ablation based on "predicted" tissue width, images generated during ablation based on different CARTO Maps LAT, voltage, Visitag, etc. taken at several fixed views, and the results of the procedure. Respiration, respiration prediction and indicators, ACL current, and TPI are inputs. Typically, the data includes intracardiac EGM signals, body surface ECG signals, catheter applied force, tissue thickness measured via CT or MRI, ripple percentage and peaks calculated in ripple maps, fragmentation / fractionation / ECG complexity index, cycle length maps, CFAE location, focal activity calculated by electric rotors and Carto-Finder®, 3D location of the ablation point, ablation time, power / temperature, tag index value of the ablation, radius and / or depth of the ablation as predicted by the algorithm, type of ablation catheter, irrigation level during ablation, catheter stability level during ablation, parameters regarding the area of ​​ablation to verify transmural ablation based on "predicted" tissue width, different CARTO Maps taken in several fixed views. Images generated during ablation based on LAT, voltage, Visitag, etc., pulsed field ablation characteristics such as voltage amplitude, pulse width, interphase delay, interpulse delay, pulse cycle length, and clinical outcome (acute, or after about 2 days of blanking period, or after a longer follow-up period, e.g., 12 months) can be obtained.

[0127] Data may be transferred to a storage device in the cloud in real time (during a case). Data may be transferred to a storage device in the cloud after a case, either automatically or when requested by the user. The system can transfer data to a storage device in the cloud when the computer is idle. Users may manually transfer data to a training center, and the model is trained on all past cases stored, for example, on a workstation machine.

[0128] The AI ​​model may be supervised ML, may use reinforcement learning, and / or may use convolutional neural networks if images are analyzed. The model can be constructed in various ways. For example, if a case has a desired outcome, train it with the ablation parameters as output and all other parameters as input. Provide the model with the ablation parameters as output and all other parameters as input, and perform reinforcement learning using the actual outcome. Provide the model with all parameters as input and the outcome as output, and train the model to predict the outcome. All parameters and ablation locations of cases with a desired outcome may be used as input, and the ablation parameters may be the output.

[0129] Additionally, a tagging index, also called an ablation index, may be calculated, with an exemplary formula being: where K, a, b, and c are constants, CF is the applied contact force of the ablation catheter, P is the constant power applied during ablation, and t is the duration of the ablation.

[0130]

number

[0131] Again, the model can be trained by having clinical outcomes fed into the system. Either acute results can be used, and / or results after a blanking period of several days (e.g., 2 days), or results after a longer follow-up period (e.g., 12 months) can be used. The system can suggest ablation locations and parameters. Alternatively, the physician can provide the system with the planned ablation locations, and the system can suggest only the ablation parameters along with the expected percentage success of this ablation. The physician can provide the system with both the ablation locations and also the ablation parameters to be used, and the system can predict the expected results (the ablation parameters may include the ablation power in watts, the target temperature for ablation in degrees Celsius, the ablation duration in seconds, or a single value summarizing all the ablation parameters using some formula, such as the Tagg index formula described above).

[0132] The model learns which inputs are most important and which are less important. The model can suggest ablation locations on the tissue or ablation strategies (recommended temperature, power, ablation index / tag index, ablation duration, etc.). Locations may be marked on a computer screen on a map of the heart.

[0133] Training may be performed on one or more CPU, GPU, or TPU processors, FPGA chips, or dedicated ASICs that perform deep learning computations. It should also be noted that the success of an ablation may be unknown until the ablation is performed. For example, the catheter may move during the ablation, and some ablation parameters may be unknown until the ablation is performed. The proposed algorithm can integrate previous information with the set of ablations achieved to propose the next ablation step once the information is known.

[0134] Calculations may be performed, for example, within a CARTO workstation machine, on a server within the hospital, on a server and in the cloud, in an area owned by the hospital.

[0135] In accordance with the above, Figure 20 is an exemplary diagram of an exemplary method 2000 for using machine learning to assist a physician in locating areas to perform ablation on a patient with AFIB. In step 2010, the physician provides information regarding the ablation to the machine, as described above. If the physician does not provide a location to the machine in step 2020, the method proceeds to step 2030, where the machine uses input from the physician in Figure 19 described above to analyze based on previous ablations to determine the location of the ablation, as well as parameters to utilize during the ablation.

[0136] If the physician provides location information in step 2020, the method proceeds to step 2040, where the machine uses input from the physician as described above in FIG. 19 to analyze based on previous ablations and determine parameters to use during the ablation.

[0137] In step 2050, the machine provides the physician with information about the ablation to be performed. That is, if the physician did not provide a location, the machine provides both location information and parameters. If the physician provided an ablation location, the machine provides parameters without providing a location. In either case, the machine can also provide the physician with a preferred strategy based on the input and a comparison with strategies previously used for ablation according to the data provided above.

[0138] FIG. 21 illustrates data preparation and training for the present system. Data 2110 from previous patient cases is used to train the system for each such case. This data may include 3D mapping 2110.1 (described in detail hereinabove) and patient parameters 2110.2 (described in detail hereinbelow), such as age, gender, and medical history, used as input to the system described herein and data used to calculate the desired output. This data may include ablation data 2110.3 and clinical outcomes 2110.4. Ablation data 2110.3 may include information such as the location of the ablation performed and an indication of the treatment quality of each ablation point, including, for example, stability, power, force, impedance, and impedance change during ablation. Ablation data 2110.3 may include discounting ablation points with poor treatment tolerance. Such discounting can include, for example, removing the weight of such points (100% discounting) or minimizing them by discounting them by 10, 20, 25, 50, 66%, etc. The discount percentage can be determined according to the Ablation Index defined above in paragraph

[0054] , or according to a similar index based on one or several parameters related to treatment quality.

[0139] Clinical outcomes 2110.4 may include weighting prior data according to the duration of treatment results. For example, a case with an AFIB recurrence 3 months after treatment may be assigned a lower weight than a case with an AFIB recurrence 12 months after treatment.

[0140] Preprocessing 2130 may be performed to conform to a desired output format, i.e., a 3D mapping of a mesh, with each point having a score between 0 and 1 indicating the strength of the recommendation as to whether the point is a good candidate for ablation. Calculating this score may rely on the "ablation index" calculated by CARTO®, for example, by discounting ablation points with poor treatment quality (e.g., point instability, ablation that is not deep enough, or does not use enough power). A particular patient's data set may be weighted according to the duration of the treatment outcome. For example, a case with afib recurrence 3 months after treatment has a lower weight than a case with afib that recurred only 12 months after treatment. After preprocessing 2130, a desired output 2140 may be included and provided to train the neural network in step 2150. Additionally, input 2110 may be directly fed into the training as a training set using system input 2120 and provided to train the neural network in step 2150. Once training 2150 is performed, the neural network is trained (2160). The dataset may be divided into a training set, a validation set, and a test set. The training set and validation set may be used during system development, and the test set is used to evaluate the accuracy of the system. Cross-validation may also be used to improve performance.

[0141] 22 and 23 show an exemplary composite architecture of the system. A mapping may be input to the system in step 2310, as described herein. Flattening may occur in step 2320. Flattening involves "flattening" the 3D mapping of the cardiac mesh, i.e., converting the data points on the left atrium surface into a 2D mapping (in a similar way that the 3D surface of the Earth can be projected onto a 2D map), for example, by stereographic projection or other types of projection such as orthogonal projection, Mercator projection, etc.

[0142] Flattening provides the system with points on the mesh as potential candidates for ablation, and the decision depends on the input mapping values ​​in the region surrounding the candidate point, rather than the entire mapping. The computation uses a CNN. The CNN described above can be successfully trained and optimized to identify certain types of geometric "features" (e.g., slanted lines, circles) based on the immediate surrounding region, regardless of the specific location where the feature appears in the 2D image.

[0143] There are many possible neural architectures, such as those described above, that may be used to compute the desired output. Depending on the nature of the data, more complex architectures may be desirable.

[0144] As an example, the NN architecture is the output of a preprocessing stage that flattens 2320 a 3D input mapping into a 2D mapping. Thus, the input to map merge 2200 from flattening stage 2320 includes N mappings, each a 2D "image" of size HxW that provides one real value.

[0145] Map Merge 2200 combines input mappings using linear combinations. For example, it uses one neural layer of size HxW, where neuron (i,j) is fed with the value of location (i,j) in each of N images. This configuration may be sufficient if each point in each input map provides a reasonably reliable indication of good ablation by itself, and if simple linear combination / averaging of the input maps is sufficient for integration. The advantage is that it is easier to train with less data compared to more complex models. However, as explained below, the results may not be accurate, and a more complex model may be required.

[0146] Additional layers (deeper networks) can be used to represent more complex functions. Larger layers can be used to capture more nuances in the data. There may also be separate processing on each input map before combining the information. Other types of layer architectures, such as fully connected, CNN, max pooling, etc., can also be used.

[0147] Returning to the input to map merge 2200 from the flattening stage 2320, in the exemplary network, the input includes, for example, N=8 2D mappings (each obtained by flattening the output of a different algorithm 2320, such as CFAE, cycle length map, etc.). Each such mapping is processed separately in layer 1 2210 by a different CNN grid. Each of the input N images may be processed using an individualized convolutional model, i.e., each input image may be provided to a separate CNN. The output of each CNN is an image that provides a preliminary output recommendation map based on just one input map. Further layers may be applied separately to each map "track" according to standard techniques of deep learning for image processing, i.e., CNNs, max pooling, and other paradigms.

[0148] In an embodiment, the output of layer 1 2210 may include N=8 convolutional mappings that are fed to layer 2 2220. These mappings may be provided to combination layer 3 2230, which has depth k=5, to enable non-linear combination. Combination layer 2230 receives the input maps and / or outputs of previous separate processing layers and combines them into a single representation. Combining may be done using simple linear combinations or more complex combinations, i.e., non-linear combinations, and / or using CNNs, max pooling, and other standard image processing layers.

[0149] For example, the system may be augmented by using two layers: a first layer (not shown) of Layer 3 of size HxWxk (i.e., a 2D layer HxW with "thickness" k>1), and a second layer (not shown) of Layer 3 of size HxW. Each of the k neurons at position (i,j) in the first layer is fed N signals from position (i,j) from each of N input maps, and its output is given to neuron (i,j) in the second layer. Thus, a nonlinear combination of N values ​​at each point can be represented.

[0150] Finally, the k combination layers are merged into an output map by layer 4 2240. Here, the values ​​of N=8 and k=5 are merely exemplary values, and more (or possibly fewer) layers than shown may be used depending on the nature of the data and the required accuracy of the output.

[0151] Patient parameters such as age, gender, medication, medical history, and type of atrial fibrillation can affect the results. A separate model can be trained based on some of the input patient parameters. Alternatively, patient inputs may be provided to layers within the NN architecture to help learn differences based on these parameters.

[0152] Different patients' hearts may be different, which may result in variability in the recorded data. According to one embodiment, the system may need to be trained in batches, each of which is limited to a single patient's data. Data needs to be collected from at least a certain number of patients to make the training of the system robust.

[0153] Even after the system is ready and deployed in hospitals, additional data may be accumulated. It should be added to the training dataset, and the system should be retrained to continually improve its accuracy. Specifically, data from additional procedures can provide feedback by considering the success assessment of ablations performed according to the system's recommendations.

[0154] While features and elements are described above in particular combinations, those skilled in the art will understand that each feature or element can be used alone or in combination with other features and elements. In addition, the methods described herein may be implemented in a computer program, software, or firmware embodied in a computer-readable medium for execution on a computer or processor. Examples of computer-readable media include electronic signals (transmitted via wired or wireless connections) and computer-readable storage media. Examples of computer-readable storage media include, but are not limited to, read-only memory (ROM), random-access memory (RAM), registers, cache memory, semiconductor memory devices, magnetic media such as internal hard disks and removable disks, magneto-optical media, and optical media such as CD-ROM disks and digital versatile disks (DVDs). A processor in association with software may be used to implement a radio frequency transceiver for use in a WTRU, UE, terminal, base station, RNC, or any host computer.

[0155] [Embodiment] (1) A method for assisting a physician in locating an area for ablation in a patient with atrial fibrillation (AFIB), comprising: receiving data at the machine from at least one device, said data including information regarding a desired location for performing ablation; generating, by the machine, an optimal location for performing the ablation based on the data and inputs; providing an optimal location for performing the ablation output by the model; and Including, method. (2) The method of embodiment 1, wherein the receiving includes a cardiac map and the providing includes the optimal ablation location and parameters. (3) The method of embodiment 1, wherein the receiving includes cardiac maps and EGM signals, and the providing includes the optimal ablation location and parameters. (4) The method of embodiment 1, wherein the receiving includes a cardiac map and EGM signals and a preferred ablation location, and the providing includes the parameters for ablation at the preferred ablation location. (5) The method of embodiment 1, wherein the receiving includes a cardiac map and EGM signals and a preferred ablation location and the ablation parameters, and the providing includes the parameters for ablation at the preferred ablation location.

[0156] (6) The method of embodiment 1, wherein the system is trained with acute outcomes of retrospective cases fed into the machine learning model. (7) The method of embodiment 1, wherein the training data includes any of the map generated during the ablation procedure for the particular physician, the ablation data collected during the procedure, the ablation catheter type, the 3D location of the ablation point, the power used for ablation, the point ablation duration, irrigation, catheter stability, parameters related to the area of ​​the ablation for verifying transmural ablation based on the "predicted" tissue width, and / or the results of the procedure. (8) The method of embodiment 1, wherein the data cluster for recommending an ablation strategy includes any of clinical ablation parameters including a ripple frequency map with ripple percentage and peak, a fragmentation index, a cycle length map, a CFAE, a Finder, a fractionation map, a complexity map, a site and index, and clinical outcomes including acute, after a blanking period of several days, and after long-term follow-up. (9) The method of embodiment 1, wherein the data cluster for recommending an ablation strategy includes patient parameters including at least one of age, gender, medication, and medical history. (10) The method of embodiment 1, wherein the data cluster for recommending an ablation strategy comprises cardiac mapping including at least one of a ripple frequency map with ripple percentage and peak, a fragmentation index, a cycle length map, a CFAE, a Finder, a fractionation map, a complexity map, anatomical mapping including CT, MRI, or ultrasound of the heart, clinical ablation parameters including site and index, and clinical outcomes including acute, after a blanking period of several days, and after long-term follow-up.

[0157] (11) A system for assisting a physician in locating an area to perform ablation on a patient with atrial fibrillation (AFIB), comprising: a first step of receiving data from at least one device, the data including information regarding a desired location for performing ablation; a second step of generating an optimal location for performing the ablation based on the data and inputs; Equipped with the system outputs an optimal location for performing the ablation. system. (12) The system described in embodiment 11, wherein the first step receives a cardiac map and the system outputs the optimal ablation location and parameters. (13) The method of embodiment 11, wherein the first step receives a cardiac map and EGM signals, and the system outputs the optimal ablation location and parameters. (14) The method of embodiment 11, wherein the first step receives a cardiac map and EGM signals and a preferred ablation location, and the system outputs the parameters for ablation at the preferred ablation location. (15) The method of embodiment 11, wherein the first step receives a cardiac map and EGM signals and a preferred ablation location and the ablation parameters, and the system outputs the parameters for ablation at the preferred ablation location.

[0158] (16) The method of embodiment 11, wherein the system is trained with acute outcomes of retrospective cases fed into the machine learning model. (17) The method of embodiment 11, wherein the training data includes any of the map generated during the ablation procedure for the particular physician, the ablation data collected during the procedure, the ablation catheter type, the 3D location of the ablation point, the power used for ablation, the point ablation duration, irrigation, catheter stability, parameters related to the area of ​​the ablation for verifying transmural ablation based on the "predicted" tissue width, and / or the results of the procedure. (18) The method of embodiment 11, wherein the data cluster for recommending an ablation strategy includes any of clinical ablation parameters including a ripple frequency map with ripple percentage and peak, a fragmentation index, a cycle length map, a CFAE, a Finder, a fractionation map, a complexity map, a site and index, and clinical outcomes including acute, after a blanking period of several days, and after long-term follow-up. (19) The method of embodiment 11, wherein the data cluster for recommending an ablation strategy includes patient parameters including at least one of age, gender, medication, and medical history. (20) The method of embodiment 11, wherein the data cluster for recommending an ablation strategy comprises cardiac mapping including at least one of a ripple frequency map with ripple percentage and peak, a fragmentation index, a cycle length map, a CFAE, a Finder, a fractionation map, a complexity map, anatomical mapping including CT, MRI, or ultrasound examination of the heart, clinical ablation parameters including site and index, and clinical outcomes including acute, after a blanking period of several days, and after long-term follow-up.

Claims

1. 1. A system for assisting a physician in locating an area for ablation in a patient with atrial fibrillation (AFIB), comprising: receiving means for receiving data from at least one device, said data including information regarding ablation locations desired by the physician, said data including a cardiac map; generating means for generating recommended ablation locations and recommended ablation parameters based on the data using a machine learning model; providing means for providing the recommended ablation locations and the recommended ablation parameters to the physician; Equipped with the machine learning model is trained with training data consisting of (i) three-dimensional (3D) mapping, (ii) ablation data, (iii) patient parameters, and (iv) clinical outcomes of past cases; the recommended ablation parameters include at least one of ablation power, ablation target temperature, ablation duration, and tissue contact force; system.

2. The system of claim 1 , wherein the data includes an EGM signal.

3. The system of claim 1 , wherein the training data includes any of the following: ablation catheter type, 3D location of ablation points, power used for ablation, point ablation duration, and / or irrigation level.

4. The system of claim 1 , wherein the patient parameters include at least one of age, gender, and medical history.

5. 1. A method for assisting a physician in locating an area for ablation in a patient with atrial fibrillation (AFIB), comprising: receiving data from at least one device on a machine for implementing a machine learning model, the data including information regarding ablation locations desired by the physician, the data including a cardiac map; generating, with the machine learning model, recommended ablation locations and recommended ablation parameters based on the data; providing the recommended ablation locations and the recommended ablation parameters to the physician; Including, the machine learning model is trained with training data consisting of (i) three-dimensional (3D) mapping, (ii) ablation data, (iii) patient parameters, and (iv) clinical outcomes of past cases; the recommended ablation parameters include at least one of ablation power, ablation target temperature, ablation duration, and tissue contact force; method.

6. The method of claim 5 , wherein the data includes an EGM signal.

7. The method of claim 5 , wherein the training data includes any of the following: ablation catheter type, 3D location of ablation points, power used for ablation, point ablation duration, and / or irrigation level.

8. The method of claim 5 , wherein the patient parameters include at least one of age, sex, and medical history.

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