Abstract: An apparatus and method for generating clinical decision support is disclosed. The apparatus includes at least a processor and a computer-readable storage medium communicatively connected to the at least a processor, wherein the computer-readable storage medium contains instructions configuring the at least processor to receive user data, generate a fused feature vector correlating the user data to a plurality of clinical outcomes by training a plurality of deep neural networks (DNNs) to output a first set of feature vectors, a second set of feature vectors and a third set of feature vectors, fusing the first, second, and third set of features vectors to form the fused feature vector, generate a procedural output using the fused feature vector, and display the procedural output through a user interface.

Abstract: A system for determining position signals of electrodes using a retrained machine-learning model includes at least a catheter including a plurality of electrodes configured to collect a plurality of potential signals and a magnetic sensor configured to collect magnetic data, and at least a computing device including a memory. The processor receives a first training set, wherein the first training set includes patient-agnostic data; receives a second training set, wherein the second training set includes patient-specific data, trains a mapping machine-learning model using the first training set, retrains the mapping machine-learning model using the second training set, receives at least a first signal, wherein the first signal includes a potential signal of the plurality of potential signal and the magnetic data, and generates, using the retrained machine-learning model, as a function of the at least a first signal, a first position signal for an electrode of the plurality of electrodes.

Abstract: An apparatus for labeling a plurality of time series data is disclosed. The apparatus includes at least processor and a memory communicatively connected to the processor. The memory instructs the processor to receive a plurality of time series data. The memory instructs the processor to generate a plurality of time series segments for each time series represented within the plurality of time series data. The memory instructs the processor to identify one or more segment attributes for each time series segment. The memory instructs the processor to classify each time series segment of the plurality of time series segments to at least one time series label as a function of the one or more segment attributes. The memory instructs the processor to generate at least one labeled time series segment for each time series segment of the plurality of time series segments as a function of the classification.

Abstract: An apparatus and method for generating clinical decision support is disclosed. The apparatus includes at least a processor and a computer-readable storage medium communicatively connected to the at least a processor, wherein the computer-readable storage medium contains instructions configuring the at least processor to receive user data, generate a fused feature vector correlating the user data to a plurality of clinical outcomes by training a plurality of deep neural networks (DNNs) to output a first set of feature vectors, a second set of feature vectors and a third set of feature vectors, fusing the first, second, and third set of features vectors to form the fused feature vector, generate a procedural output using the fused feature vector, and display the procedural output through a user interface.

Abstract: Apparatus for correcting machine learning model predictions and methods used therein include a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to receive a cardiac signal having a plurality of segments, generate, for at least a segment of the plurality of segments, a label representing at least a signal feature, generate at least an automated annotation as a function of the label using an annotation machine learning model, generate, using a correction module, at least a correction upon detecting an absence of annotations, receive, using a user interface, an input from a user, create a user annotation within the cardiac signal using the input, update the cardiac signal and the annotation machine learning model as a function of the correction and the user annotation, and display, using the user interface, the updated cardiac signal to the user.

Abstract: Described herein are systems and methods for responding to user input using an agent orchestrator. A system may include a cardiac catheter, a user interface, and a computing device configured to receive, from the user interface, a first user input, receive, from the catheter, the procedure data, determine, using an agent orchestrator, a first agent selection datum, wherein determining the first agent selection datum includes generating the first agent selection datum as a function of the first user input using a trained agent selection machine learning model, using a first agent corresponding to the first agent selection datum, determine a first agent output, wherein determining the first agent output includes inputting into the first agent the procedure data, and receiving, as an output from the first agent, the first agent output, and display, using the user interface, the first agent output.

Abstract: Apparatus for generating electro-anatomical mapping and methods used therein include a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to receive input data, generate, using at least a machine learning model, an electro-anatomical mapping as a function of the input data, and display the electro-anatomical mapping using a user interface, wherein receiving the input data includes receiving, from an imaging device, at least a medical image and receiving, from a signal capturing device, at least an electrogram, wherein the at least a machine learning model is trained using electro-anatomical mapping training data including exemplary medical images and exemplary electrograms as input correlated to exemplary electro-anatomical mappings as output.

Abstract: An apparatus for the generation of an impedance model of a biological chamber is disclosed. The apparatus includes a catheter assembly comprising a plurality of electrodes arranged into one or more constraint pairs. The apparatus includes a processor and a memory communicatively connected to the processor. The memory instructs the processor to receive tank data. The memory instructs the processor to generate a plurality of relative configurations of the plurality of electrodes as a function of the tank data using a compliant configuration generator. The memory instructs the processor to record voltage data from the plurality of electrodes within a biological chamber as a function of the plurality of relative configurations. The memory instructs the processor to map a plurality of impedance metrics for each relative configuration as a function of the voltage data. The memory instructs the processor to generate an impedance model as a function of the map.

Abstract: A system for correct navigation of an instrument inside a human body, comprising a reference instrument disposed at a first region of an anatomical part, a roving instrument disposed at a second region of the anatomical part, and a control circuit coupled with the reference instrument and the roving instrument, configured to receive a plurality of magnetic locations and a plurality of first impedance locations from the reference instrument, generate a localization model as a function of the plurality of magnetic locations and the plurality of first impedance locations, receive a second impedance location from the roving instrument, and generate an updated second impedance location of the roving instrument, using the localization model, as a function of the second impedance location.

Abstract: Described herein is an apparatus and method for predicting Pulsed Field Ablation (PFA) durability. An apparatus may include at least a processor; and a memory communicatively connected to the at least processor, wherein the memory contains instructions configuring the at least processor to receive a training dataset comprising a plurality of example PFA device parameters correlated to a plurality of example PFA outcomes; train a PFA durability machine learning model using the training dataset; receive a PFA device parameter; and generate a PFA durability datum as a function of the PFA device parameter using a trained PFA durability machine learning model.

Abstract: Provided herein are systems and methods for providing digital guidance in an underlying computer application. In one exemplary implementation, a method includes setting a rule or rules, in a computing device, in advance of digital guidance content creation, for detecting, upon later playback of the content, page elements of the underlying computer application that are associated with the content. The exemplary method further includes recording, in the computing device, steps of the digital guidance content as the steps are created by a content author, and automatically applying, in the computing device, the previously set rule or rules for detecting page elements, and thereby assigning strong attributes to the page elements. The method further includes saving, in the computing device, the content steps along with the strong attributes of the page elements associated with the content steps.

Abstract: Provided herein are systems and methods for providing digital guidance in an underlying computer application. In one exemplary implementation, a method includes recording, in a computing device, steps of digital guidance content as the steps are created by a content author. The exemplary method also includes automatically segmenting, in the computing device, the digital guidance content as it is being created such that the digital guidance content is only associated with segments of the underlying computer application where the content is relevant. The exemplary method further includes making the digital guidance content available for playback to an end user on a computing device only when the end user is in a segment of the underlying computer application that is relevant to the digital guidance content.

Abstract: Provided herein are systems and methods for providing digital guidance in an underlying computer application. In one exemplary implementation, a method includes setting a rule or rules, in a computing device, in advance of digital guidance content creation, for detecting, upon later playback of the content, page elements of the underlying computer application that are associated with the content. The exemplary method further includes recording, in the computing device, steps of the digital guidance content as the steps are created by a content author, and automatically applying, in the computing device, the previously set rule or rules for detecting page elements, and thereby assigning strong attributes to the page elements. The method further includes saving, in the computing device, the content steps along with the strong attributes of the page elements associated with the content steps.

Abstract: Provided herein are systems and methods for providing digital guidance in an underlying computer application. In one exemplary implementation, a method includes recording, in a computing device, steps of digital guidance content as the steps are created by a content author. The exemplary method also includes automatically segmenting, in the computing device, the digital guidance content as it is being created such that the digital guidance content is only associated with segments of the underlying computer application where the content is relevant. The exemplary method further includes making the digital guidance content available for playback to an end user on a computing device only when the end user is in a segment of the underlying computer application that is relevant to the digital guidance content.