Abstract: Systems, apparatuses, and methods for detecting carious lesions are described herein. In an example, the systems in an optical interrogator including a single-pixel photodetector responsive to short-wave infrared light and operatively coupled to a controller. In an example the optical interrogator includes a light engine for emitting light, a scanning mirror assembly and a single-pixel photodetector. In an example, the methods include causing the light engine to emit light having wavelengths in a range of about 900 nm to about 1,700 nm; selectively directing the light over different portions of a tooth with a scanning mirror assembly to provide scattered light; and correlating scattered light signals generated by the single-pixel photodetector in response to the scattered light with the portion of the tooth.

Abstract: Systems and methods for surgical training are provided. The system may include an operating display console for a first surgeon to view a video feed of a surgery while the first surgeon performs the surgery. The system may also include a training display console for a second surgeon to view the video feed of the surgery while the surgery is performed. The system may further include a controller that interfaces with the operating display console and the training display console. The training display console may include a training interface for pausing the video feed displayed on the training display console, and for switching from a display mode to a simulation mode for simulating one or more surgical steps of the surgery.

Abstract: Embodiments may include a method to estimate motion data based on test image data sets. The method may include receiving a training data set comprising a plurality of training data elements. Each element may include an image data set and a motion data set. The method may include training a machine learning model using the training data set, resulting in identifying one or more parameters of a function in the machine learning model based on correspondences between the image data sets and the motion data sets. The method may further include receiving a test image data set. The test image data set may include intensities of pixels in a deep-tissue image. The method may include using the trained machine learning model and the test image data set to generate output data for the test image data set. The output data may characterize motion represented in the test image data set.

Abstract: An apparatus, system and process for guiding a surgeon during a medical procedure to prevent surgical mistakes are described. The system may include a machine learning medical procedure server that generates one or more machine learning medical procedure models using, at least, medical procedure data captured during medical procedures performed at a plurality of different medical procedure systems. The system may also include a medical procedure system communicably coupled with the machine learning medical procedure server that receives a selected machine learning medical procedure model from the machine learning medical procedure server, and utilizes the selected machine learning medical procedure model during a corresponding medical procedure to control one or more operations of the medical procedure system.

Abstract: Systems and methods for surgical training are provided. The system may include an operating display console for a first surgeon to view a video feed of a surgery while the first surgeon performs the surgery. The system may also include a training display console for a second surgeon to view the video feed of the surgery while the surgery is performed. The system may further include a controller that interfaces with the operating display console and the training display console. The training display console may include a training interface for pausing the video feed displayed on the training display console, and for switching from a display mode to a simulation mode for simulating one or more surgical steps of the surgery.

Abstract: An apparatus, system and process for guiding a surgeon during a medical procedure to prevent surgical mistakes are described. The system may include a machine learning medical procedure server that generates one or more machine learning medical procedure models using, at least, medical procedure data captured during medical procedures performed at a plurality of different medical procedure systems. The system may also include a medical procedure system communicably coupled with the machine learning medical procedure server that receives a selected machine learning medical procedure model from the machine learning medical procedure server, and utilizes the selected machine learning medical procedure model during a corresponding medical procedure to control one or more operations of the medical procedure system.

Abstract: Systems and methods for acquiring images of a sample are provided. According to an aspect of the invention, a system includes a first light source that emits first light having a first wavelength as a temporally continuous beam; a second light source that emits second light having a second wavelength as a temporally modulated beam; and a scanning mirror that raster scans the first light across a sample during a raster scan period, and projects a structured light pattern of the second light onto the sample during the raster scan period. A first image of the sample is generated from at least a portion of the first light that is backscattered from the sample during the raster scan period, and a second image of the sample is generated from at least a portion of the second light that is backscattered from the sample during the raster scan period.

Abstract: Systems and methods for surgical training are provided. The system may include an operating display console for a first surgeon to view a video feed of a surgery while the first surgeon performs the surgery. The system may also include a training display console for a second surgeon to view the video feed of the surgery while the surgery is performed. The system may further include a controller that interfaces with the operating display console and the training display console. The training display console may include a training interface for pausing the video feed displayed on the training display console, and for switching from a display mode to a simulation mode for simulating one or more surgical steps of the surgery.

Abstract: Systems, apparatuses, and methods for detecting carious lesions are described herein. In an example, the systems in an optical interrogator including a single-pixel photodetector responsive to short-wave infrared light and operatively coupled to a controller. In an example the optical interrogator includes a plurality of lasers for emitting laser light, a scanning mirror assembly and a single-pixel photodetector. In an example, the methods include causing the plurality of lasers to emit the laser light having wavelengths in a range of about 900 nm to about 1,700 nm; selectively directing the laser light over different portions of a tooth with a scanning mirror assembly to provide scattered light; and correlating scattered light signals generated by the single-pixel photodetector in response to the scattered light with the portion of the tooth.

Abstract: An imaging system includes an image sensor having an array of sensor pixels, a multi-chromatic illuminator adapted to independently generate a plurality of illumination colors each having a different finite spectral range, and a controller coupled to the image sensor and to the multi-chromatic illuminator. The controller includes logic to time-multiplexing the multi-chromatic illuminator between the illumination colors. Each of the illumination colors independently illuminates for at least a corresponding one of non-overlapping durations of illumination of the illumination colors. The controller includes further logic to acquire chromatic sub-images with the image sensor and combine the chromatic sub-images into a composite color image. Each of the chromatic sub-images is acquired during a different one of the non-overlapping durations of illumination.

Abstract: An imaging system (e.g., hyperspectral imaging system) receives an indication to compare a first object and a second object (e.g., two anatomical structures or organs in a medical environment). The imaging system accesses a classification vector for the first object and the second object, the classification vector having been extracted by separating a plurality of collected reflectance values for the first object from a plurality of collected reflectance values for the second object. A set of optimal illumination intensities for one or more spectral illumination sources of the imaging system is determined based on the extracted classification vector. The first and second objects are illuminated with the determined illumination intensities. A high-contrast image of the first and second objects is provided for display, such that the two objects can be readily distinguished in the image. The intensity of pixels in the image is determined by the illumination intensities.

Abstract: A system for improving imaging during a surgical procedure includes an image sensor coupled to a controller. The image sensor is coupled to capture a video, including a first image frame with a smoke occlusion due to surgical smoke, of the surgical procedure. The first image frame includes a plurality of pixels each having an imaged color representing a view of the tissue affected by the smoke occlusion. The controller stores instructions that when executed causes the system to perform operations. The operations include determining an estimated true color of each of the plurality of pixels. The estimated true color is closer to an unoccluded color of the tissue than the imaged color. The operations also include generating, in response to determining the estimated true color, a desmoked first image frame with a reduced amount of the smoke occlusion relative to the first image frame.

Abstract: Embodiments may include a method to estimate motion data based on test image data sets. The method may include receiving a training data set comprising a plurality of training data elements. Each element may include an image data set and a motion data set. The method may include training a machine learning model using the training data set, resulting in identifying one or more parameters of a function in the machine learning model based on correspondences between the image data sets and the motion data sets. The method may further include receiving a test image data set. The test image data set may include intensities of pixels in a deep-tissue image. The method may include using the trained machine learning model and the test image data set to generate output data for the test image data set. The output data may characterize motion represented in the test image data set.

Abstract: Embodiments may include a method to estimate motion data based on test image data sets. The method may include receiving a training data set comprising a plurality of training data elements. Each element may include an image data set and a motion data set. The method may include training a machine learning model using the training data set, resulting in identifying one or more parameters of a function in the machine learning model based on correspondences between the image data sets and the motion data sets. The method may further include receiving a test image data set. The test image data set may include intensities of pixels in a deep-tissue image. The method may include using the trained machine learning model and the test image data set to generate output data for the test image data set. The output data may characterize motion represented in the test image data set.

Abstract: A system for improving imaging during a surgical procedure includes an image sensor coupled to a controller. The image sensor is coupled to capture a video, including a first image frame with a smoke occlusion due to surgical smoke, of the surgical procedure. The first image frame includes a plurality of pixels each having an imaged color representing a view of the tissue affected by the smoke occlusion. The controller stores instructions that when executed causes the system to perform operations. The operations include determining an estimated true color of each of the plurality of pixels. The estimated true color is closer to an unoccluded color of the tissue than the imaged color. The operations also include generating, in response to determining the estimated true color, a desmoked first image frame with a reduced amount of the smoke occlusion relative to the first image frame.

Abstract: An imaging system includes an image sensor having an array of sensor pixels, a multi-chromatic illuminator adapted to independently generate a plurality of illumination colors each having a different finite spectral range, and a controller coupled to the image sensor and to the multi-chromatic illuminator. The controller includes logic to time-multiplexing the multi-chromatic illuminator between the illumination colors. Each of the illumination colors independently illuminates for at least a corresponding one of non-overlapping durations of illumination of the illumination colors. The controller includes further logic to acquire chromatic sub-images with the image sensor and combine the chromatic sub-images into a composite color image. Each of the chromatic sub-images is acquired during a different one of the non-overlapping durations of illumination.

Abstract: A system for improving imaging during a surgical procedure includes an image sensor coupled to a controller. The image sensor is coupled to capture a video, including a first image frame with a smoke occlusion due to surgical smoke, of the surgical procedure. The first image frame includes a plurality of pixels each having an imaged color representing a view of the tissue affected by the smoke occlusion. The controller stores instructions that when executed causes the system to perform operations. The operations include determining an estimated true color of each of the plurality of pixels. The estimated true color is closer to an unoccluded color of the tissue than the imaged color. The operations also include generating, in response to determining the estimated true color, a desmoked first image frame with a reduced amount of the smoke occlusion relative to the first image frame.

Abstract: An imaging system (e.g., hyperspectral imaging system) receives an indication to compare a first object and a second object (e.g., two anatomical structures or organs in a medical environment). The imaging system accesses a classification vector for the first object and the second object, the classification vector having been extracted by separating a plurality of collected reflectance values for the first object from a plurality of collected reflectance values for the second object. A set of optimal illumination intensities for one or more spectral illumination sources of the imaging system is determined based on the extracted classification vector. The first and second objects are illuminated with the determined illumination intensities. A high-contrast image of the first and second objects is provided for display, such that the two objects can be readily distinguished in the image. The intensity of pixels in the image is determined by the illumination intensities.

Abstract: An endoscope apparatus includes a fiber optic cable with a proximal end and a distal end opposite the proximal end. The endoscope apparatus also includes a light source optically coupled to the proximal end of the fiber optic cable to emit visible light and excitation light into the fiber optic cable for output from the distal end. The light source is configured to emit both the visible light and the excitation light simultaneously, and a wavelength of the excitation light is outside a wavelength spectrum of the visible light. An image sensor is configured to receive a reflection of the visible light as reflected visible light.

Abstract: An imaging system (e.g., hyperspectral imaging system) receives an indication to compare a first object and a second object (e.g., two anatomical structures or organs in a medical environment). The imaging system accesses a classification vector for the first object and the second object, the classification vector having been extracted by separating a plurality of collected reflectance values for the first object from a plurality of collected reflectance values for the second object. A set of optimal illumination intensities for one or more spectral illumination sources of the imaging system is determined based on the extracted classification vector. The first and second objects are illuminated with the determined illumination intensities. A high-contrast image of the first and second objects is provided for display, such that the two objects can be readily distinguished in the image. The intensity of pixels in the image is determined by the illumination intensities.