Embodiments of architecture, systems, and methods to develop a learning/evolving system to robotically perform and model one or more activities of a medical procedure where the medical procedure may include diagnosing a patient's medical condition(s), treating medical condition(s), and robotically diagnosing a patient's medical condition(s) and performing one or more medical procedure activities based on the diagnosis without User intervention where the activities may be performed in computer-based environment formed by the learning/evolving system, live, or a combination thereof.

Systems, methods, and computer-readable media for providing a decision support solution to medical professionals to optimize medical care through data monitoring and feedback treatment are provided herein. In another embodiment, a computer-implemented method for modeling patient outcomes resulting from treatment in a specific medical area includes receiving patient-specific data associated with a patient, determining a plurality of possible patient states under which the patient can be categorized, a current patient state under which the patient can be categorized and determining probabilities of the patient transitioning from any of the possible patient states to every other possible patient state.

Systems, methods, and computer-readable media for providing a decision support solution to medical professionals to optimize medical care through data monitoring and feedback treatment are provided herein. In another embodiment, a computer-implemented method for modeling patient outcomes resulting from treatment in a specific medical area includes receiving patient-specific data associated with a patient, determining a plurality of possible patient states under which the patient can be categorized, a current patient state under which the patient can be categorized and determining probabilities of the patient transitioning from any of the possible patient states to every other possible patient state.

A computer-implemented method for generating a virtual depiction of an orthodontic treatment of a patient is disclosed herein. The computer-implemented method may involve gathering a three-dimensional (3D) model modeling the patient's dentition at a specific treatment stage of an orthodontic treatment plan. An image of the patient's face and dentition may be gathered. A first set of reference points modeled on the 3D model of the patient's dentition and a second set of reference points represented on the dentition of the image of the patient may be received. The image of the patient's dentition may be projected into a 3D space to create a projected 3D model of the image of the patient's dentition. Based on a comparison of the first reference points and projections of the second set of reference points, a plurality of modified images of the patient may be constructed to depict progressive stages of a treatment plan.

A computer-implemented method for generating a virtual depiction of an orthodontic treatment of a patient is disclosed herein. The computer-implemented method may involve gathering a three-dimensional (3D) model modeling the patient's dentition at a specific treatment stage of an orthodontic treatment plan. An image of the patient's face and dentition may be gathered. A first set of reference points modeled on the 3D model of the patient's dentition and a second set of reference points represented on the dentition of the image of the patient may be received. The image of the patient's dentition may be projected into a 3D space to create a projected 3D model of the image of the patient's dentition. Based on a comparison of the first reference points and projections of the second set of reference points, a plurality of modified images of the patient may be constructed to depict progressive stages of a treatment plan.

The present disclosure provides systems and methods that include or otherwise leverage a machine-learned brain injury location model to predict locations of brain injury in a patient based on test data associated with the patient, such as, for example, behavioral test data. For example, the machine-learned brain injury location model can be trained on training data associated with a corpus of patients, where the training data includes sets of example test data (e.g., behavioral test data) respectively labeled with ground truth brain injury locations.

The present disclosure provides systems and methods that include or otherwise leverage a machine-learned brain injury location model to predict locations of brain injury in a patient based on test data associated with the patient, such as, for example, behavioral test data. For example, the machine-learned brain injury location model can be trained on training data associated with a corpus of patients, where the training data includes sets of example test data (e.g., behavioral test data) respectively labeled with ground truth brain injury locations.

A computing system comprising a central processing unit (CPU), and memory coupled to the CPU and having stored therein instructions that, when executed by the computing system, cause the computing system to execute operations to generate a radiation treatment plan. The operations include accessing a minimum prescribed dose to be delivered into and across the target, determining a number of beams and directions of the beams, and determining a beam energy for each of the beams, wherein the number of beams, the directions of the beams, and the beam energy for each of the beams are determined such that the entire target receives the minimum prescribed dose. The operations further include prescribing a dose rate and optimizing dose rate constraints for FLASH therapy, and displaying a dose rate map of the FLASH therapy.

The present disclosure relates to a computer-implemented process for generating and providing simulated user absorption information pertaining to users and based on target profiles and target situations, thereby providing user targeted and situationally targeted content recommendations. It is an object of the present disclosure to provide a technological solution to the long felt need in small scale content recommendation systems caused by the technical problem of generating situationally targeted and user profile targeted content recommendations for users of an interactive electronic system.

Methods and apparatuses for asynchronously identifying and modeling a gingiva strip from the three-dimensional (3D) dental model of the patient's dentition. These methods may reduce the time required to generate accurate 3D dental models and therefore may reduce and streamline the process of generating dental treatment plans.