Systems and methods for remotely monitoring hospital patients are provided. Medical devices may capture biometric data associated with a patient positioned in an ICU environment, and a remote display device positioned external to the ICU environment may display an indication of the health of the patient based on the captured biometric data. The indication of the health of the patient may include a graph mapping normalized health statuses associated with the patient over time. Each normalized health status may be based on normalizing a particular type of biometric data based on a medical protocol, so each data point of the graph includes an indication of a normalized health status associated with the patient at a given time. The indication of the health of the patient may include an animated three-dimensional that is dynamically updated to reflect biometric data associated with the patient captured at a time selected by a user.

Systems/techniques that facilitate AI-based region-of-interest masks for improved data reconstructions are provided. In various embodiments, a system can access a set of two-dimensional medical scan projections. In various aspects, the system can generate a set of two-dimensional region-of-interest masks respectively corresponding to the set of two-dimensional medical scan projections. In various instances, the system can generate a region-of-interest visualization based on the set of two-dimensional region-of-interest masks and the set of two-dimensional medical scan projections. In various cases, the system can generate the set of two-dimensional region-of-interest masks by executing a machine learning segmentation model on the set of two-dimensional medical scan projections.

Systems and methods are disclosed for treating teeth to correct for malocclusions. This may be accomplished by applying a series of labels to a digital dental model and applying a rolling ball process to identify tooth boundaries separating one tooth from a neighboring tooth and to also determine the crown/gum margin. The user may further assign regions to the dental model to indicate hard regions and soft regions. With the dental model labeled and defined, the user may then generate a treatment plan for moving the labeled and defined tooth or teeth relative to one another to correct for any malocclusions. Upon approval of the treatment plan, a series of 3D printed dental appliances or aligners to be worn in series by the patient may be fabricated to ultimately move the tooth or teeth to a desired position.

The present disclosure provides a system and method for PET image reconstruction. The method may include processes for obtaining physiological information and/or rigid motion information. The image reconstruction may be performed based on the physiological information and/or rigid motion information.

Disclosed are a system, method, and computer program product for generating pruned tractograms of neural fiber bundles. The method includes receiving scan data produced by diffusion imaging of at least a portion of a brain from a magnetic-resonance imaging (MRI) device. The method also includes generating an initial tractogram by mapping neuronal fiber pathways of a target fiber bundle of the scan data. The method further includes generating a density map using a set of tracts from the initial tractogram, identifying each tract that passes through a segment of the density map more than once, and setting a contribution of said tract to a unique tract count of the segment equal to a threshold pruning value. The method further includes generating a pruned tractogram by identifying a segment having a unique tract count less than or equal to the threshold pruning value and excluding the segment from the pruned tractogram.

A computer-implemented method includes receiving a 3D model of upper teeth (U1) of a patient (P) and a 3D model of lower teeth (L1) of the patient (P1), and receiving a plurality of 2D images, each image representative of at least a portion of the upper teeth (U1) and lower teeth (L1) of the patient (P). The method also includes determining, based on the 2D images, a spatial relationship between the upper teeth (U1) and lower teeth (L1) of the patient (P).

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.

An example method includes obtaining, a virtual model of a portion of an anatomy of a patient obtained from a virtual surgical plan for an orthopedic joint repair surgical procedure to attach a prosthetic to the anatomy; identifying, based on data obtained by one or more sensors, positions of one or more physical markers positioned relative to the anatomy of the patient; and registering, based on the identified positions, the virtual model of the portion of the anatomy with a corresponding observed portion of the anatomy.

A visualization system including multiple light sources, an image sensor configured to detect imaging data from the multiple light sources, and a control circuit is disclosed. At least one of the light sources is configured to emit a pattern of structured light. The control circuit is configured to receive the imaging data from the image sensor, generate a three-dimensional digital representation of the anatomical structure from the pattern of structured light detected by the imaging data, obtain metadata from the imaging data, overlay the metadata on the three-dimensional digital representation, receive updated imaging data from the image sensor, and generate an updated three-dimensional digital representation of the anatomical structure based on the updated imaging data. The visualization system can be communicatively coupled to a situational awareness module configured to determine a surgical scenario based on input signals from multiple surgical devices.

Embodiments of the present disclosure provide a software program that displays both a volume as images and segmentation results as surface models in 3D. Multiple 2D slices are extracted from the 3D volume. The 2D slices may be interactively rotated by the user to best follow an oblique structure. The 2D slices can “cut” the surface models from the segmentation so that only half of the models are displayed. The border curves resulting from the cuts are displayed in the 2D slices. The user may click a point on the surface model to designate a landmark point. The corresponding location of the point is highlighted in the 2D slices. A 2D slice can be reoriented such that the line lies in the slice. The user can then further evaluate or refine the landmark points based on both surface and image information.