Abstract: A computer implemented method for training a whole medical image foundation model, including: receiving a plurality of medical image datasets; extracting local sections of image data from the plurality of medical image datasets; obtaining one or more causal variables associated with the local sections and/or patient; training one or more self-supervised learning models based on the local sections of image data and the causal variables; combining the one or more trained self-supervised learning models with a deep learning network configured to combine a latent representation of the local sections of image data from the one or more trained self-supervised learning models into a patient-level representation; and combining, with the one or more trained self-supervised learning models and the deep learning network, at least one further network or function configured to accept the patient-level representation as input, the at least one further network or function operable to perform one or more patient-specific pre

Abstract: A computer-implemented method for predicting cardiovascular disease risk, the method including: receiving a first patient history data comprising imaging data and/or non-imaging data for a patient at a first time point; selecting prediction report parameters defining a type of cardiovascular event and a risk prediction time scale; processing the first patient history data using a trained machine learning model configured to predict disease onset, progression, and/or regression over time, wherein the trained machine learning model is trained using patient subsets created based on patient history characteristics and outcomes; generating a risk prediction for the selected type of cardiovascular event over the selected risk prediction time scale based on the processed first patient history data; generating a risk prediction report based on the risk prediction; and outputting the risk prediction report.

Abstract: Reel devices, systems, and related methods are disclosed. The reel devices are modular and include an automatic shift assembly that shifts to provide a mechanical advantage when used to tighten a cord. For instance, the reel devices are configured to provide a first drive ratio and automatically transition to a second drive ratio in response to a torque force. The reel devices include a drive assembly and a shift assembly. The drive assembly includes a cycloidal gear.

Abstract: A computer-implemented method for processing medical images may comprise: receiving image data for a patient; based on the received image data, determining: a patient-specific epicardial adipose tissue (EAT) metric or a patient-specific pericoronary adipose tissue (PCAT) metric, and at least one other patient-specific metric, and using the EAT metric or the PCAT metric, and the at least one other patient-specific metric, to determine a risk score for the patient or to classify a disease state of the patient.

Abstract: Reel devices, systems, and related methods are disclosed. The reel devices are modular and include an automatic shift assembly that shifts to provide a mechanical advantage when used to tighten a cord. For instance, the reel devices are configured to provide a first drive ratio and automatically transition to a second drive ratio in response to a torque force. The reel devices include a drive assembly and a shift assembly. The drive assembly includes a cycloidal gear.

Abstract: A computer-implemented method for processing electronic images to quantify coronary microvascular disease may include receiving imaging data of one or more captured electronic images. A first set of the imaging data may have been captured prior to an administration of one or more pharmacological agents, and a second set of the imaging data may have been captured subsequently. The method may further include providing the imaging data and a set of patient data to a machine-learning model. The machine-learning model may have been trained to identify coronary microvascular disease (CMD) features within the captured imaging data and the set of patient data and output one or more CMD measures and/or a predicted CMD endotype. The method may further include transmitting, to a user device, the one or more CMD measures and/or the predicted CMD endotype.

Abstract: Reel devices, systems, and related methods are disclosed. The reel devices are modular and include an automatic shift assembly that shifts to provide a mechanical advantage when used to tighten a cord. For instance, the reel devices are configured to provide a first drive ratio and automatically transition to a second drive ratio in response to a torque force. The reel devices include a drive assembly and a shift assembly. The drive assembly includes a cycloidal gear.

Abstract: Reel devices, systems, and related methods are disclosed. The reel devices are modular and include an automatic shift assembly that shifts to provide a mechanical advantage when used to tighten a cord. For instance, the reel devices are configured to provide a first drive ratio and automatically transition to a second drive ratio in response to a torque force. The reel devices include a drive assembly and a shift assembly. The drive assembly includes a cycloidal gear.

Abstract: Reel devices, systems, and related methods are disclosed. The reel devices are modular and include an automatic shift assembly that shifts to provide a mechanical advantage when used to tighten a cord. For instance, the reel devices are configured to provide a first drive ratio and automatically transition to a second drive ratio in response to a torque force. The reel devices include a drive assembly and a shift assembly. The drive assembly includes a cycloidal gear.