Ultrasound systems having a computing device, a steering mechanism, and an ultrasound transducer are disclosed. The ultrasound transducer is configured to generate angularly discrete signals over a scan region of the ultrasound system in response to inputs from the steering mechanism. The computing device is communicatively coupled to the ultrasound transducer. The computing device includes a processor configured to receive angularly discrete ultrasound signals from the ultrasound transducer over the scan region, determine a scan line count corresponding to each of the received angularly discrete ultrasound signals, associate a TGC curve with each of the scan line counts, apply a TGC curve to each of the angularly discrete ultrasound signals as associated with the scan line count of each angularly discrete ultrasound signal, where each of the applied TGC curves defines a gain that maintains, increases, or decreases the angularly discrete ultrasound signal to which it is applied, over time.

A method, an apparatus, and a computer program for determining a refractive error of an eye of a user are provided. The method for determining the refractive error of the eye of the user, wherein the eye of the user has a choroid, includes: ascertaining at least one value for a layer thickness of the choroid of the eye of the user over at least one region of the choroid; and determining a value for the change in the refractive error of the eye only from at least two values for the layer thickness of the choroid which were each ascertained at different times for the at least one region of the choroid, wherein the at least one region is selected from a nasal perifoveal region or a nasal parafoveal region.

A method, an apparatus, and a computer program for determining a refractive error of an eye of a user are provided. The method for determining the refractive error of the eye of the user, wherein the eye of the user has a choroid, includes: ascertaining at least one value for a layer thickness of the choroid of the eye of the user over at least one region of the choroid; and determining a value for the change in the refractive error of the eye only from at least two values for the layer thickness of the choroid which were each ascertained at different times for the at least one region of the choroid, wherein the at least one region is selected from a nasal perifoveal region or a nasal parafoveal region.

A method for positioning key features of a lens based on ocular B-mode ultrasound images includes: acquiring and preprocessing the ocular B-mode ultrasound images to obtain a preprocessed B-mode ultrasound image, eyeball coordinates and lens coordinates; sending the preprocessed B-mode ultrasound image, the eyeball coordinates and the lens coordinates into a trained target detection network YOLOv3 to obtain eyeball position images and lens position images; substituting the eyeball position images and the lens position images into a trained feature extraction network group to obtain image features and feature coordinates corresponding to the eyeball position images and the lens position images, respectively; substituting the image features into a trained collaborative learning network to screen key image features; and marking a feature coordinate corresponding to the key image features on the ocular B-mode ultrasound images to complete positioning the key features of the lens.

A method for positioning key features of a lens based on ocular B-mode ultrasound images includes: acquiring and preprocessing the ocular B-mode ultrasound images to obtain a preprocessed B-mode ultrasound image, eyeball coordinates and lens coordinates; sending the preprocessed B-mode ultrasound image, the eyeball coordinates and the lens coordinates into a trained target detection network YOLOv3 to obtain eyeball position images and lens position images; substituting the eyeball position images and the lens position images into a trained feature extraction network group to obtain image features and feature coordinates corresponding to the eyeball position images and the lens position images, respectively; substituting the image features into a trained collaborative learning network to screen key image features; and marking a feature coordinate corresponding to the key image features on the ocular B-mode ultrasound images to complete positioning the key features of the lens.

The present disclosure is directed to a system and method that detects and measures a vault of an anterior segment of an eye of a patient. The system and method locate an implanted contact lens (ICL) between a cornea and a natural lens, form a B-scan of the eye based on the received ultrasound pulses, removes background pixels, such as by binarizing and thresholding, from the B-Scan from a grayscale color palette to a black/white color palette, determines, from the resulting B-scan a fiduciary location in the anterior segment of the eye, and forms using the resulting B-scan and fiduciary location, a vault map mapping a distance between an anterior segment surface and a posterior surface of the ICL along a plurality of lines drawn perpendicular to a local surface of the anterior segment surface.

The present disclosure is directed to a system and method that detects and measures a vault of an anterior segment of an eye of a patient. The system and method locate an implanted contact lens (ICL) between a cornea and a natural lens, form a B-scan of the eye based on the received ultrasound pulses, removes background pixels, such as by binarizing and thresholding, from the B-Scan from a grayscale color palette to a black/white color palette, determines, from the resulting B-scan a fiduciary location in the anterior segment of the eye, and forms using the resulting B-scan and fiduciary location, a vault map mapping a distance between an anterior segment surface and a posterior surface of the ICL along a plurality of lines drawn perpendicular to a local surface of the anterior segment surface.

The invention relates to a system and method of implementation of artificial intelligence and tomography imaging to determine the biomechanical degradation or degeneration in human cornea. The invention relates to a combination tool using artificial intelligence and tomography imaging to map the region of degeneration in the cornea. The method of artificial intelligence and corneal tomography imaging includes analysis of changes in the structure of the cornea, constructing the 3D volumes using corneal tomography, meshing the 3D volumes with the elements for biomechanical simulations by using finite element modules, application of artificial intelligence to determine the region of biomechanical degeneration in cornea. The combination tool of the invention is effective in predicting the progression of the disease by analyzing the chronic steepening of the cornea by quantitating the parameters such as increase in curvature, aberrations of the cornea.

The invention relates to a system and method of implementation of artificial intelligence and tomography imaging to determine the biomechanical degradation or degeneration in human cornea. The invention relates to a combination tool using artificial intelligence and tomography imaging to map the region of degeneration in the cornea. The method of artificial intelligence and corneal tomography imaging includes analysis of changes in the structure of the cornea, constructing the 3D volumes using corneal tomography, meshing the 3D volumes with the elements for biomechanical simulations by using finite element modules, application of artificial intelligence to determine the region of biomechanical degeneration in cornea. The combination tool of the invention is effective in predicting the progression of the disease by analyzing the chronic steepening of the cornea by quantitating the parameters such as increase in curvature, aberrations of the cornea.

During acquisition of an ultrasound image feed, ultrasound control data frames are acquired that may be interspersed amongst the ultrasound data frames. The control data frames may use consistent reference scan parameters, irrespective of the scanner settings, and may not need to be converted to image frames. The control data frames can be passed to an artificial intelligence model, which predicts the suitable settings for scanning the anatomy that is being scanned. The artificial intelligence model can be trained with a dataset containing different classes of ultrasound control data frames for different settings, where substantially all the ultrasound control data frames in the dataset are consistently acquired using the reference scan parameters.