Retinal diseases, such as age-related macular degeneration (AMD), are the leading cause of blindness in the elderly population. Since no known cures are currently present, it is crucial to diagnose the condition in its early stages so that disease progression is monitored. Systems and methods for detecting and mapping the mechanical elasticity of retinal layers in the posterior eye are disclosed herein. A system including confocal shear wave acoustic radiation force optical coherence elastography (SW-ARF-OCE) is provided, wherein an ultrasound transducer and an optical scan head are co-aligned to facilitate in-vivo study of the retina. In addition, an automatic segmentation algorithm is used to isolate tissue layers and analyze the shear wave propagation within the retinal tissue to estimate mechanical stress on the retina and detect early stages of retinal diseases based on the estimated mechanical stress.

Retinal diseases, such as age-related macular degeneration (AMD), are the leading cause of blindness in the elderly population. Since no known cures are currently present, it is crucial to diagnose the condition in its early stages so that disease progression is monitored. Systems and methods for detecting and mapping the mechanical elasticity of retinal layers in the posterior eye are disclosed herein. A system including confocal shear wave acoustic radiation force optical coherence elastography (SW-ARF-OCE) is provided, wherein an ultrasound transducer and an optical scan head are co-aligned to facilitate in-vivo study of the retina. In addition, an automatic segmentation algorithm is used to isolate tissue layers and analyze the shear wave propagation within the retinal tissue to estimate mechanical stress on the retina and detect early stages of retinal diseases based on the estimated mechanical stress.

An intraocular pressure (IOP) measurement system. An ultrasound pressure sensor is implantable in an eye, wherein the sensor has a sealed cavity that changes shape as a function of IOP of the eye. An ultrasound transmitter emits an incident ultrasound beam. A receiver produces an output signal in response to receiving a reflected ultrasound beam. A spectrometer is configured to estimate the IOP of the eye based on processing the output signal of the receiver. Other aspects are also described and claimed.

An intraocular pressure (IOP) measurement system. An ultrasound pressure sensor is implantable in an eye, wherein the sensor has a sealed cavity that changes shape as a function of IOP of the eye. An ultrasound transmitter emits an incident ultrasound beam. A receiver produces an output signal in response to receiving a reflected ultrasound beam. A spectrometer is configured to estimate the IOP of the eye based on processing the output signal of the receiver. Other aspects are also described and claimed.

The invention relates to a computer-assisted method for position determination for an intraocular lens supported by machine learning. The method comprises providing a scan result for an eye. The scan result here represents an image of an anatomical structure of the eye. The method further comprises use of a trained machine learning system for the direct determination of a final location of an intraocular lens to be fitted, wherein digital data of the scan of the eye is used as the input data for the machine learning system.

A disposable biocompatible (and preferably hypoallergenic) flexible cover for use with a tip of a probe of a contact ophthalmological instrument. The outer surface of the tip (at the closed end) of the cover is configured to match the surface of the cornea, while its inner surface is preferably dimensioned to be substantially congruent with the surface of the tip of the probe with which the cover is used. A method for use of the same.

A disposable biocompatible (and preferably hypoallergenic) flexible cover for use with a tip of a probe of a contact ophthalmological instrument. The outer surface of the tip (at the closed end) of the cover is configured to match the surface of the cornea, while its inner surface is preferably dimensioned to be substantially congruent with the surface of the tip of the probe with which the cover is used. A method for use of the same.

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.

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.

Disclosed herein are methods and systems of evaluating test-retest precision using fractional rank precision or mean-average precision, comprising: a) collecting a test and a retest result of each subject, wherein the results are described in feature space(s) and collected from a vision test machine; b) selecting, a first test result of a first subject; c) calculating distances from the first test result to the retest result of each subject; d) assessing, a similarity between the first test result and the retest result of each subject by ranking the distances in a non-descending order; e) assessing a rank precision for the first subject based on a rank of a distance from the first test result to the retest result of the first subject; f) repeating b), c), d), and e) for each subject; and evaluating, the test-retest precision based on the rank precision for each of the plurality of subjects.