The present disclosure provides a pharmaceutical composition for multi-stage release of psychoactive substances including cannabinoids. The pharmaceutical composition comprises two or more staged compositions having different release profiles or different release time such that the one or more active agents in each of the two or more staged compositions are released into the subject's blood stream at different time points.

The present disclosure provides a pharmaceutical composition for multi-stage release of psychoactive substances including cannabinoids. The pharmaceutical composition comprises two or more staged compositions having different release profiles or different release time such that the one or more active agents in each of the two or more staged compositions are released into the subject's blood stream at different time points.

Computer-implemented digital image analysis methods, apparatuses, and systems for robotic installation of surgical implants are disclosed. A disclosed apparatus plans a route within an anatomy of a patient from an incision site to a surgical implant site for robotic installation of a surgical implant. The apparatus uses digital imaging data to identify less-invasive installation paths and determine the dimensions of the surgical implant components being used. The apparatus segments the surgical implant into surgical implant subcomponents and modifies the surgical implant subcomponents, such that they can be inserted using the identified less-invasive installation paths.

A computing system according to an embodiment includes at least one processor and at least one memory comprising a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the computing system to determine a plurality of implant size predictions with associated confidence levels based on one or more patient or surgical parameters, wherein each of the implant size predictions identifies a confidence level that a prospective implant of a corresponding size will fit a patient, determine whether a combined confidence level determined based on a subset of the plurality of associated confidence levels is at least a threshold value, and recommend, in response to a determination that the combined confidence level is not at least the threshold value, incorporation of at least one of an additional implant size prediction of the plurality of implant size predictions in the subset or digital templating data to improve an accuracy of an implant size estimation.

Computer implemented biometric methods and systems incorporate sensing biophysical phenomena, translating the phenomena into digital data and transmitting the data to a series of servers operating in an open feedback loop to generate a module. A biometric networking system can include a biometric monitoring cloud computing platform with AI/machine learning augmented models are generated to make user assessments, programs and confidence scores to the healthcare provider systems. The AI/machine learning models can be used by the biometric monitoring network to generate health-related AI processes that analyze relationships treatment techniques and outcomes. AI techniques can be used to calculate movement modeling and confidence scoring including support vector machines, neural networks, and decision trees. The biophysical phenomena may include biometric parameters based on data, such as medical history, exertion, sleep, temperature, cardiovascular events, respiratory events, and muscle and blood pH.

Techniques for generating an ontology based on biomarker information associated with persons to facilitate improving clinical predictions relating to medical conditions are presented. An ontology generator component (OGC) can extract clinical features associated with patients and their associated times from medical records or databases to develop clinical profiles associated with the patients and relating to a medical condition. OGC can develop an ontology relating to the medical condition, including progression and severity of biomarkers associated with the medical condition, based on the clinical profiles and domain knowledge information relating to the medical condition. OGC can determine global features relating to progression and severity associated with the medical condition based on the ontology. At a forecasting point, the global features can be extracted from the ontology and applied to a prediction model to enhance prediction of onset of, or progression of, the medical condition for a patient.

A simulation system performs microsimulations to model the impact of one or more early cancer detection screenings for a plurality of participants to simulate a randomized controlled trial (RCT). In one instance, the microsimulations are performed using parallel processing techniques. The microsimulation simulates the impact of early detection screenings on individual trajectories of the participants. In particular, while most screening modalities are for single cancer types, the microsimulation herein simulates the effect of a detection model on individual trajectories for participant populations having multiple types of cancer using, for example, multi-cancer early detection (MCED) screenings that are capable of detecting multiple types of cancer.

A system is provided for augmenting a three-dimensional (3D) model of a heart to indicate the tissue state. The system accesses a 3D model of a heart, accesses two-dimensional (2D) images of tissue state slices of the heart, and accesses source location information of an arrhythmia. The system augments the 3D model with an indication of a source location based on the source location information. For each of a plurality of the tissue state slices of the heart, the system augments a 3D model slice of the 3D model that corresponds to that tissue state slice with an indication of the tissue state of the heart represented by the tissue state information of that tissue state slice. The system then displays a representation of the 3D model that indicates the source location of the arrhythmia and the tissue state of the heart.

Methods, apparatuses, and systems for robotic insertion of a screw, a rod, or another component of a surgical implant into a patient are disclosed. Clinical data from previous surgical procedures or information received from a supervising surgeon can be leveraged to minimize the risk of harm to the patient and improve outcomes. The methods disclosed thus provide more precise placement of implanted surgical components and implants.

A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing hybrid machine learning. More specifically, the system and method produce predictions of MCI conversions to dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is trained using transfer learning. A platform may then receive a request from a clinician for a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.