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.

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.

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.

An ultrasound imaging system includes a processor programmed to identify the type of tissue being imaged and to confirm that one or more system settings and/or the energy of ultrasound imaging signals delivered is set appropriately for such tissue. In one embodiment, an image obtained with the ultrasound imaging system is analyzed to determine if the tissue is ophthalmic (eye) tissue. If so, the system parameter settings and/or the transmit power of the signals produced by the ultrasound system are adjusted or maintained at a level that is appropriate for imaging such tissue.

An ultrasound imaging system includes a processor programmed to identify the type of tissue being imaged and to confirm that one or more system settings and/or the energy of ultrasound imaging signals delivered is set appropriately for such tissue. In one embodiment, an image obtained with the ultrasound imaging system is analyzed to determine if the tissue is ophthalmic (eye) tissue. If so, the system parameter settings and/or the transmit power of the signals produced by the ultrasound system are adjusted or maintained at a level that is appropriate for imaging such tissue.

An interleaved photon detection array for sampling a physical sample including a plurality of photon detectors, which may be arranged in close proximity to each other. Photon detection array includes at least a first photon detector having at least a first signal detection parameter. Interleaved photon detection array includes at least a second photon detector having at least a second signal detection parameter. Signal detection parameters of the first signal detector and the second signal detector may be heterogeneous. Interleaved photon detection array includes a control circuit coupled to the plurality of photon detectors. Control circuit receives signals from the plurality of photon detectors and renders an image of a physical sample. Additional imaging technology such as ultrasound may be combined with photon detection array.

The present disclosure relates to a method and a system for biomechanically characterising ocular tissue (C) through deformation of the ocular tissue. The method comprises: generating an acoustic stimulus for delivery onto the ocular tissue in a collinear anner with an axis of a measuring device, for producing vibration of the ocular tissue; measuring ocular tissue displacement with the measuring device at a plurality of locations of the ocular tissue; obtaining at least a biomechanical parameter by processing the ocular tissue displacements at the plurality of locations. The disclosure also relates to a method and a system for screening biomechanical abnormality of ocular tissue (C).

The present disclosure relates to a method and a system for biomechanically characterising ocular tissue (C) through deformation of the ocular tissue. The method comprises: generating an acoustic stimulus for delivery onto the ocular tissue in a collinear anner with an axis of a measuring device, for producing vibration of the ocular tissue; measuring ocular tissue displacement with the measuring device at a plurality of locations of the ocular tissue; obtaining at least a biomechanical parameter by processing the ocular tissue displacements at the plurality of locations. The disclosure also relates to a method and a system for screening biomechanical abnormality of ocular tissue (C).

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.