Digital Diagnostics Inc.

An ocular alignment system for aligning a subject's eye with an optical axis of an ocular imaging device comprising one or more guide light and one or more baffle configured to mask the one or more guide light from view of the subject such that the one or more guide light is only visible to the subject when the eye of the subject is aligned with the optical axis of an ocular imaging system.

A disease diagnosis tool validates a diagnosis of a patient. The disease diagnosis tool receives a high-resolution image of a body part. The disease diagnosis tool divides the high-resolution image into a plurality of tiles and inputs a representation of each tile into an encoder portion of a model configured to perform a disease diagnosis based on the representations. The encoder has an attention mechanism. The disease diagnosis tool obtains a plurality of tokens representative of an attention of the encoder based on the attention mechanism. Each token is associated with a position of a tile of the plurality of tiles. The disease diagnosis tool generates a heat map corresponding to the image of the body part. The heat map comprises a two-dimensional image having pixels corresponding to tiles each with an amplitude based on a level of attention represented in the plurality of tokens corresponding to the tiles.

A disease diagnosis tool uses a vision transformer to determine a diagnosis of a disease from a high-resolution image of a body part. A disease diagnosis tool receives a high-resolution image of a body part. The disease diagnosis tool divides the high-resolution image into a plurality of tiles. For each tile, the disease diagnosis tool generates an embedding having a position encoding corresponding to the tile's position in the high-resolution image. The disease diagnosis tool inputs the embeddings for the tiles into a linear projection model whose output feeds a transformer and receives, as output from a model comprising the transformer, a diagnosis of a disease of the body part.

SNAPSHOT SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHER Provided is a snapshot spectral domain optical coherence tomographer comprising a light source providing a plurality of beamlets; a beam splitter, splitting the plurality of beamlets into a reference arm and a sample arm; a first optical system that projects the sample arm onto multiple locations of a sample; a second optical system for collection of a plurality of reflected sample beamlets; a third optical system projecting the reference arm to a reflecting surface and receiving a plurality of reflected reference beamlets; a parallel interferometer that provides a plurality of interferograms from each of the plurality of sample beamlets with each of the plurality of reference beamlets; an optical image mapper configured to spatially separate the plurality of interferograms; a spectrometer configured to disperse each of the interferograms into its respective spectral components and project each interferogram in parallel; and a photodetector providing photon quantification.

Provided is a snapshot spectral domain optical coherence tomographer comprising a light source providing a plurality of beamlets; a beam splitter, splitting the plurality of beamlets into a reference arm and a sample arm; a first optical system that projects the sample arm onto multiple locations of a sample; a second optical system for collection of a plurality of reflected sample beamlets; a third optical system projecting the reference arm to a reflecting surface and receiving a plurality of reflected reference beamlets; a parallel interferometer that provides a plurality of interferograms from each of the plurality of sample beamlets with each of the plurality of reference beamlets; an optical image mapper configured to spatially separate the plurality of interferograms; a spectrometer configured to disperse each of the interferograms into its respective spectral components and project each interferogram in parallel; and a photodetector providing photon quantification.

A device is disclosed for diagnosing a dental condition. The device captures image data representative of a tooth of a patient based on data obtained from a hardware device that scans the tooth. The device inputs the image data into a first supervised machine learning model, and receives, as output from the first supervised machine learning model, a plurality of biomarkers, each biomarker corresponding to a different location of the tooth. The device inputs the plurality of biomarkers into a second supervised machine learning model, and receives, as output from the second supervised machine learning model, a diagnosis of a dental condition.

A device is disclosed for diagnosing a dental condition. The device captures image data representative of a tooth of a patient based on data obtained from a hardware device that scans the tooth. The device inputs the image data into a first supervised machine learning model, and receives, as output from the first supervised machine learning model, a plurality of biomarkers, each biomarker corresponding to a different location of the tooth. The device inputs the plurality of biomarkers into a second supervised machine learning model, and receives, as output from the second supervised machine learning model, a diagnosis of a dental condition.

Systems and methods are disclosed herein for classifying one or more disease conditions. In some embodiments, an application stores a common extraction model, the common extraction model trained using training examples for a plurality of diseases. The application stores a plurality of disease classifiers, each disease classifier configured to output whether or not its respective disease is present, each disease classifier trained using training examples for its respective disease. The application receives a selection of a disease and selects a disease classifier from the plurality of disease classifiers corresponding to the disease. The application inputs an image into the common extraction model and receives, as output from the common extraction model, a set of biomarkers extracted from the image. The application inputs the set of biomarkers into the selected disease classifier, the selected disease classifier configured to output whether or not the disease is present in the image.

Systems and methods are disclosed herein for training a manifold foundational model to autonomously diagnose a new disease classification. In some embodiments, an application receives training data for a plurality of diseases, the training data for each disease including a training examples, each example having an image of a patient, a set of biomarkers indicative of a disease condition depicted in the image, and a label indicating whether the patient has the disease condition. The application trains a common extraction model using the training data for the plurality of diseases, where the common extraction model is configured to take images as input and to output biomarkers, and trains a plurality of disease classifiers, each disease classifier configured to take the output of the common extraction model as input and to output whether a respective disease for which the disease classifier is trained to detect is present in the images.

Systems and methods are disclosed herein for determining a diagnosis based on a base skin tone of a patient. In an embodiment, the system receives a base skin tone image of a patient, generates a calibrated base skin tone image by calibrating the base skin tone image using a reference calibration profile, and determines a base skin tone of the patient based on the calibrated base skin tone image. The system receives a concern image of a portion of the patient's skin, and selects a set of machine learning diagnostic models from a plurality of sets of candidate machine learning diagnostic models based on the base skin tone of the patient, each of the sets of candidate machine learning diagnostic models trained to receive the concern image and output a diagnosis of a condition of the patient.