Abstract: Systems and methods for designing and implementing patient-specific surgical procedures and/or medical devices are disclosed. In some embodiments, a method includes receiving a patient data set of a patient. The patient data set is compared to a plurality of reference patient data sets, wherein each of the plurality of reference patient data sets is associated with a corresponding reference patient. A subset of the plurality of reference patient data sets is selected based, at least partly, on similarity to the patient data set and treatment outcome of the corresponding reference patient. Based on the selected subset, at least one surgical procedure or medical device design for treating the patient is generated.

Abstract: Systems and methods are disclosed for processing digital images to identify diagnostic tests, the method comprising receiving one or more digital images associated with a pathology specimen, determining a plurality of diagnostic tests, applying a machine learning system to the one or more digital images to identify any prerequisite conditions for each of the plurality of diagnostic tests to be applicable, the machine learning system having been trained by processing a plurality of training images, identifying, using the machine learning system, applicable diagnostic tests of the plurality of diagnostic tests based on the one or more digital images and the prerequisite conditions, and outputting the applicable diagnostic tests to a digital storage device and/or display.

Abstract: Provided is a medical image processing device, a processor device, a medical image processing method, and a computer-readable medium capable of obtaining a recognition result related to a region of interest and storing and displaying a medical image in which the recognition result matches an intention of an operator. The medical image processing device includes an image acquisition unit (40) that acquires a medical image (38), an acquired image storage unit (46A) that stores the acquired medical image, a recognition processing unit (42A) that performs recognition processing on the acquired medical image, a user input signal acquisition unit (50) that acquires a user input signal transmitted according to an operation of a user, and a selection unit (48) that selects a medical image from medical images for which a result of the recognition processing related to a region of interest is obtained in a case where the user input signal is acquired.

Abstract: Systems, methods, and other embodiments described herein relate to a multi-task model that integrates recurrent models to improve handling of multi-sweep inputs. In one embodiment, a method includes acquiring sensor data from multiple modalities. The method includes separately encoding respective segments of the sensor data according to an associated one of the different modalities to form encoded features using separate encoders of a network. The method includes accumulating, in a detector, sparse features associated with sparse sensor inputs of the multiple modalities to densify the sparse features into dense features. The method includes providing observations according to the encoded features and the sparse features using the network.

Abstract: Described here are systems and method for predicting clinically relevant brain features using geometric deep learning techniques, such as may be implemented with graph convolutional neural networks or autoencoder networks that are applied to graph representations of brain surface morphology derived from medical images. As an example, graph convolutional neural networks can be applied to brain surface morphology data derived from magnetic resonance images (e.g., T1-weighted) using surface extraction techniques in order to predict brain feature data.

Abstract: Disclosed is a system and method for presenting a graphical representation of an oral situation of a patient over time. In particular the system and method relates to a method of presenting a plurality of scans taken over time in manner where the focus on the changes in the oral situation of the patient is maintained.

Abstract: Described are systems and methods for training a machine-learning model to generate image of biological samples, and systems and methods for generating enhanced images of biological samples. The method for training a machine-learning model to generate images of biological samples may include obtaining a plurality of training images comprising a training image of a first type, and a training image of a second type. The method may also include generating, based on the training image of the first type, a plurality of wavelet coefficients using the machine-learning model; generating, based on the plurality of wavelet coefficients, a synthetic image of the second type; comparing the synthetic image of the second type with the training image of the second type; and updating the machine-learning model based on the comparison.

Abstract: Image processing technology embeds signal (e.g., digital watermarks) within imagery during a raster image process(or). One claim recites: an image processing method of embedding a signal within imagery using a raster image processing (RIP), comprising: obtaining a plurality of elements representing a signal; and modulating a plurality of print structures within the RIP according to the plurality of elements, in which said modulating varies density, and direction or angle, of the plurality of print structures, and in which said modulating introduces the signal within the imagery. Of course, other claims, combinations and technology are described too.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for generating and/or adapting automated assistant content according to a distance of a user relative to an automated assistant interface that renders the automated assistant content. For instance, the automated assistant can provide data for a client device to render. The client device can request additional data when the user relocates closer to, or further from, the client device. In some implementations, a request for additional data can identify a distance between the user and the client device. In this way, the additional data can be generated or selected according to the distance in the request. Other implementations can allow an automated assistant to determine an active user from a group of users in an environment, and determine a distance between the active user and the client device in order that any rendered content can be tailored for the active user.

Abstract: A method for visualization of digitized slides is disclosed. The method comprises retrieving, by one or more computer processors, a digitized slide, determining, by the one or more computer processors, one or more visualization components of the digitized slide, generating, by the one or more computer processors, a virtual slide corresponding to the digitized slide based on the one or more visualization components, and displaying, by the one or more computer processors, a visualization of the virtual slide.

Abstract: A spinal surgery training process includes the steps of capturing a plurality of 2D images for each of a plurality of spines, generating a curve of each spine from the respective 2D images based on locations of select vertebrae in each of the spines, grouping the spines into one of a number of groups based on similarity to produce groups of spines having similarities, performing the capturing, generating, determining and grouping steps at least once prior to surgery and at least once after surgery to produce pre-operative groups and their resultant post-operative groups, and assigning surgical methods and a probability to each of the post-operative groups indicating the probability that a spinal shape of the post-operative group can be achieved using the surgical methods. An outcome prediction process for determining surgical methods can be implemented once the training process is complete.

Abstract: An apparatus and method for training and using a computing operation for digital image processing are provided. The apparatus and method may be used for 3-dimensional medical images. An exemplary method for digital image processing comprises: receiving an image displaying at least one detectable structure, determining the detectable structure; segmenting the image to obtain a segmentation mask that is associated with a geometric shape and comprises at least one quantifiable visual feature; generating a mesh based on the quantifiable visual feature; computing at least on quantifiable visual parameter based on the mesh; extracting quantifiable visual data from the image based on the quantifiable visual parameter; training the computing operation with the quantifiable visual data.

Abstract: Disclosed herein is a system and method for improving the accuracy of an object detector when trained with a dataset having a significant number of missing annotations. The method uses a novel Background Recalibration Loss (BRL) which adjusts the gradient direction according to its own activation to reduce the adverse effect of error signals by replacing the negative branch of the focal loss with a mirror of the positive branch when the activation is below a confusion threshold.

Abstract: A detection and communication module arranged to implement a face detection function and an optical wireless communication function, and including a processing unit, a transmission chain and a reception chain, the processing unit being arranged to transmit via the transmission chain a detection signal, to receive via the reception chain the detection signal following its reflection on a face surface of an individual, and to evaluate a distance between the detection and communication module and the face surface of the individual, the processing unit being further arranged to transmit via the transmission chain a transmitted optical wireless communication signal containing data to be transmitted, and to receive via the reception chain a received optical wireless communication signal.

Abstract: Herein disclosed are robotic dentistry methods comprising tooth extraction and crown preparation. Robotic tooth extraction may comprise determining if the correct tooth is targeted, based on a scanned image; determining a decay state of the tooth based on the scanned image; selecting an extraction technique determined as a function of the decay state; and extracting the tooth using the selected extraction technique. Robotic crown preparation may comprise receiving an initial reduction plan to reduce a tooth; presenting, to a dentist for approval, the initial reduction plan for approval; upon approval, reducing the tooth according to the approved initial reduction plan; upon rejection, modifying the initial reduction plan based on dentist input; receiving the modified reduction plan to reduce the tooth; presenting, to a dentist for approval, the modified reduction plan for approval; and upon approval of the modified reduction plan, reducing the tooth according to the approved modified reduction plan.

Abstract: Systems and methods are disclosed for generating synthetic medical images, including images presenting rare conditions or morphologies for which sufficient data may be unavailable. In one aspect, style transfer methods may be used. For example, a target medical image, a segmentation mask identifying style(s) to be transferred to area(s) of the target, and source medical image(s) including the style(s) may be received. Using the mask, the target may be divided into tile(s) corresponding to the area(s) and input to a trained machine learning system. For each tile, gradients associated with a content and style of the tile may be output by the system. Pixel(s) of at least one tile of the target may be altered based on the gradients to maintain content of the target while transferring the style(s) of the source(s) to the target. The synthetic medical image may be generated from the target based on the altering.

Abstract: An apparatus (1), for use in conjunction with a medical imaging device (2) having an imaging device controller (4) that displays a graphical user interface (GUI) (8) including a preview image viewport (9), includes at least one electronic processor (20) programmed to: perform an image analysis (38) on a preview image displayed in the preview image viewport to generate preview-derived image label information; extract GUI-derived image label information from the GUI excluding the preview image displayed in the preview image viewport; and output an alert (30) when the preview-derived image label information and the GUI-derived image label information are not consistent.

Abstract: According to one embodiment, an anomaly detection device includes a processor that is configured to acquire input data. The processor derives a first anomaly degree corresponding to a difference between first feature data derived from the input data using a trained deep model and second feature data derived from the input data using a trained prediction model. The processor derives a second anomaly degree corresponding to an estimated relative positional relationship between a first and second region in the image data based on the second feature data. A total anomaly degree for the input data is then calculated from the first anomaly degree and the second anomaly degree.

Abstract: The technology disclosed introduces two types of neural networks: “master” or “generalists” networks and “expert” or “specialists” networks. Both, master networks and expert networks, are fully connected neural networks that take a feature vector of an input hand image and produce a prediction of the hand pose. Master networks and expert networks differ from each other based on the data on which they are trained. In particular, master networks are trained on the entire data set. In contrast, expert networks are trained only on a subset of the entire dataset. In regards to the hand poses, master networks are trained on the input image data representing all available hand poses comprising the training data (including both real and simulated hand images).

Abstract: A system and method configured to better identify patient-specific anatomical landmarks, measure anatomical parameters and features, and predict the patient's need for surgery within a predetermined time period. In some embodiments, the system and method is configured to predict the likelihood or risk that a patient will require total hip arthroplasty. In some embodiments, the present invention includes machine learning technology Some embodiments of the present invention include a first ML machine configured to received medical images as inputs and identify anatomical landmarks as outputs; a measurement module to measure joint space width, hip dysplasia angles, and/or leg length differential; and a second ML machine configured to receive the anatomical measurements and patient demographic data as inputs and produce a risk or likelihood that the patient will require surgery within a certain time frame.