Abstract: The technology described herein is directed to a fundus camera and, more specifically, to a fundus camera having a display that projects active visual alignment stimuli onto an eye of an examinee via one or more components of an optimal assembly. The active visual alignment stimuli are dynamically adjusted to guide an examinee toward optical alignment for fundus imaging.

Abstract: Systems and methods for evaluating clinical comparative efficacy using real-world health data are provided herein. The method includes obtaining health trajectories for members of a healthcare system. The method also includes identifying index events in the health trajectories, and segmenting the health trajectories with index events into sub-trajectories such that each sub-trajectory ends at a different index event. The method also includes generating a digital fingerprint for each sub-trajectory by either (i) generating a raw data vector, or (ii) applying representation learning to generate an embedding vector. The method also includes identifying sub-trajectories that are similar to a patient sub-trajectory by either (i) performing a stratified search, or (ii) performing a nearest-neighbor search on the embedding vectors of the members. The method also includes ranking treatment strategies for the patient based on outcomes of the treatment strategies, according to the identified similar sub-trajectories.

Abstract: The technology described herein is directed to a fundus camera and, more specifically, to a fundus camera having a display that projects active visual alignment stimuli onto an eye of an examinee via one or more components of an optimal assembly. The active visual alignment stimuli are dynamically adjusted to guide an examinee toward optical alignment for fundus imaging.

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: Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

Abstract: Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

Abstract: Disclosed herein are systems and methods for direct classification of biological datasets. The datasets may include raw mass spectrometry data. Some aspects include training a classifier for direct classification of raw data, and some aspects include applying the classifier.

Abstract: Disclosed herein are systems and methods for direct classification of biological datasets. The datasets may include raw mass spectrometry data. Some aspects include training a classifier for direct classification of raw data, and some aspects include applying the classifier.

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: Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

Abstract: Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

Abstract: The present disclosure relates to systems and methods for cellular imaging and identification through the use of a light sheet flow cytometer. In one implementation, a light sheet flow cytometer may include a light source configured to emit light having one or more wavelengths, at least one optical element configured to form a light sheet from the emitted light, a microfluidic channel configured to hold a sample, and an imaging device. The imaging device may be adapted to forming 3-D images of the sample such that identification tags attached to the sample are visible.

Abstract: The present application describes a system and method for implementing a causal recommender for personalized disease treatment selection using machine learning. The method includes obtaining health trajectories for patients. Each health trajectory includes sub-trajectories. Each sub-trajectory includes a treatment event and ends at a respective index event. The method further includes stratifying the sub-trajectories for each patient to form stratified patient segments. Each segment corresponds to a separate and distinct health condition and includes the sub-trajectories for patients that have the health condition. For each segment, the method includes performing pairwise causal inference analysis on one or more treatments to estimate average treatment effect (ATE) values, and performing network meta-analysis on the ATE values, thereby ranking the one or more treatments.

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: The technology described herein is directed to a fundus camera and, more specifically, to a fundus camera having a display that projects active visual alignment stimuli onto an eye of an examinee via one or more components of an optimal assembly. The active visual alignment stimuli are dynamically adjusted to guide an examinee toward optical alignment for fundus imaging.

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: The technology described herein is directed to a fundus camera and, more specifically, to a fundus camera having a display that projects active visual alignment stimuli onto an eye of an examinee via one or more components of an optimal assembly. The active visual alignment stimuli are dynamically adjusted to guide an examinee toward optical alignment for fundus imaging.

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: The present disclosure relates to systems and methods for cellular imaging and identification through the use of a light sheet flow cytometer. In one implementation, a light sheet flow cytometer may include a light source configured to emit light having one or more wavelengths, at least one optical element configured to form a light sheet from the emitted light, a microfluidic channel configured to hold a sample, and an imaging device. The imaging device may be adapted to forming 3-D images of the sample such that identification tags attached to the sample are visible.