Abstract: An apparatus for labeling time-series data using machine learning models, comprising a processor and a memory containing instructions configuring the processor to receive time-series data, identify a plurality of time-series segments from received time-series data, pre-train at least a classifier using labeled training data, annotate, at a labeling module, each time-series segment of the plurality of time-series segments with at least one segment identification, retrain the at least a classifier using the annotated plurality of time-series segments, generate, using the at least a classifier, one or more segment identifications at each time-series segment subsequently identified based on continuous time-series data, and display a visual representation of the continuous time-series data with the segment identifications on a user interface.

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: System for observing medical conditions and methods used therein include a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to receive, from a data repository, a plurality of reference electronic health records and a plurality of reference cardiac data elements, generate medical training data, train at least an observation machine learning model using the generated medical training data, receive a query pertaining to a subject, wherein the query includes at least a query cardiac data element and at least a query electronic health record, and output at least an observation outcome as a function of the query using the at least an observation machine learning model.

Abstract: An apparatus of generating a three-dimensional (3D) model of a patient's organ, comprising a processor and a memory containing instructions configuring the processor to receive a first set of images of a patient's organ, determine a first set of shape parameters as a function of the first set of images, generate a first 3D model of the patient's organ as a function of the first set of shape parameters, calculate a level of uncertainty at each location on the first 3D model of the patient's organ, receive a second set of images of the patient's organ corresponding to a high uncertainty region of the first 3D model, and determine a second set of shape parameters as a function of the first set of images and the second set of images, and generate a second 3D model of the patient's organ as a function of the second set of shape parameters.

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: A method for calculating conduction velocity of a cardiac activation wavefront from electrophysiological (“EP”) data points generated by a mapping system during a mapping procedure for a heart is provided. The method comprises for each EP data point comprising a local activation time, and position data defining a location within the heart corresponding to the local activation time: defining a neighborhood of EP points comprising the EP data point and a selection of neighboring EP data points; representing local activation time as a function f(x,y), where x and y our coordinates within a hyperplane defined to contain the neighborhood of EP data points based on the position data for each EP data point in the neighborhood; and calculating conduction velocity at the EP data point as a norm function of a gradient for the function f(x,y).

Abstract: An apparatus for the generation of a medical report is disclosed. The apparatus includes at least processor and a memory communicatively connected to the processor. The memory instructs the processor to receive a user query. The memory instructs the processor to receive a user profile comprising a plurality of medical tests. The memory instructs the processor to generate testing data as a function of the plurality of medical tests using an encoder. The memory instructs the processor to generate textual data that is representative of the testing data using a querying transformer model (Q-former). The memory instructs the processor to generate a medical report as a function of the user query and the textual data using a report large language model (LLM).

Abstract: An apparatus (and/or method) for training machine-learning model to make determinations from mismatched channel signals, wherein the apparatus includes a processor and memory communicatively connected to the processor. The memory containing instructions configuring the processor to receive a first set of signals using a first number of channels, process the first set of signals to simulate a second set of signals from a second number of channels, train a signal conversion model using the first set of signals and the second set of signals, and output a set of converted signals having the first number of channels using the third set of signals and the trained signal conversion model.

Abstract: Provided herein are methods, systems, and computer program products for the detection of pulmonary hypertension comprising receiving voltage-time data of a plurality of leads of an electrocardiograph of a subject; generating a feature vector from the voltage-time data; providing the feature vector to a pretrained learning system; and receiving from the pretrained learning system an indication of the presence or absence of pulmonary hypertension in the subject.

Abstract: An apparatus for generating a three-dimensional (3D) model of cardiac anatomy via machine-learning, wherein the apparatus includes a process and a memory containing instructions configuring the processor to receive a set of images of a cardiac anatomy pertaining to a subject, generate an 3D data structure representing the cardiac anatomy as a function of the set of images using a cardiac anatomy modeling model, generate an initial 3D model of the cardiac anatomy, refine the generated initial 3D model of the cardiac anatomy as a function of the 3D data structure representing the cardiac anatomy, and generate a subsequent 3D model of the cardiac anatomy as a function of the refinement.

Abstract: A system for training machine learning models with unlabeled electrocardiogram signals, the system including a memory containing instructions configurating a processor to receive a plurality of electrocardiogram (ECG) data in a textual format, create one or more overlapping temporal patches from the plurality of ECG data, mask at least one temporal patch from the one or more overlapping temporal patches, pretrain an ECG machine learning model to predict the at least one masked temporal patch from the one or more overlapping temporal patches, adjust one or more parameter values of the ECG machine learning model as a function of the at least one predicted masked temporal patch and the at least one masked temporal patch and train the ECG machine learning model as a function of the one or more parameter values and a labeled set of ECG training data.

Abstract: Described herein are systems and methods for signal digitization. A system may include a camera; a network interface device; a user interface; and a computing device configured to, using the camera, capture an image of a signal; determine a signal metric as a function of the image of the signal; and using the user interface, display the signal metric to a user; wherein the system is communicatively connected to a repository of deidentified patient health information.

Abstract: An apparatus for generating an electrocardiogram verification set is disclosed. The apparatus includes processor and a memory communicatively connected to the processor. The memory instructs the processor to receive digital ECG data. The memory instructs the processor to convert the digital ECG data into analog ECG data. The memory instructs the processor to generate an ECG validation set as a function of the analog ECG data. The memory instructs the processor to validate a diagnostic machine learning model as a function of the ECG validation set. Validating the diagnostic machine learning model includes iteratively training the diagnostic machine learning model using diagnostic training data. Validating the diagnostic machine learning model includes generating performance data based on the ECG validation set. Validating the diagnostic machine learning model includes accepting the diagnostic machine learning model as a function of a comparison between the performance data and a validation threshold.

Abstract: An apparatus for generating a diagnostic label is disclosed. The apparatus includes at least a processor and memory communicatively connected to the at least a processor. The memory instructs the processor to receive a plurality of electrocardiogram signals and a plurality of electronic health records from a user. The memory instructs the processor to generate a plurality of structured electronic health records using the plurality of electronic health records. The memory instructs the processor to generate a plurality of representations as a function of the plurality of electrocardiogram signals and the plurality of structured electronic health records using a representation machine learning model. The memory instructs the processor to generate a diagnostic label as a function of the plurality of representations. The memory instructs the processor to display the diagnostic label using a display device.

Abstract: An apparatus and method for validating, using user input, labeled data generated by a prediction generator. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The processor receives at least a potential signal, generates, processed data using the at least a potential signal, trains a prediction generator on a plurality of labeled training data, wherein the plurality of labeled training data comprises the processed data associated with at least an annotation, generates, using the prediction generator, a plurality of labeled data as a function of dynamic data, displays the plurality of labeled data to a user interface of a graphical user interface, receives a user input associated with the plurality of labeled data, and retrains the prediction generator using the user input.

Abstract: In some embodiments, an apparatus for generating a three-dimensional (3D) model with an overlay may include at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive a set of ultrasonic images of a structure; generate a set of shape parameters representing the structure's shape as a function of the set of ultrasonic images and a shape identification model trained on a training dataset comprising historical ultrasonic images correlated with historical computed tomography scan data; generate a 3D model of the structure based on the set of shape parameters; generate a map by determining a level of uncertainty at each location of a plurality of locations on the 3D model; and overlay the map onto the 3D model.