Abstract: A Bowman's Refractive Index (BRI) for quantification of microdistortions in Bowman's Layer (BL) after Small Incision Lenticule Extraction (SMILE) is defined for a patient. BRI is summation of one or more areas of the OCT image of anterior edge of Bowman's layer, quantifies the smoothness of the Bowman's layer. The anterior edge of Bowman's layer is segmented into pixels. After segmentation, a 3rd order polynomial is curve fit to the segmented pixels of the edge of Bowman's layer. BRI is calculated by segmentation of the 3-Dimensional (3-D) OCT image. BRI acts as a marker for mechanical stability and is useful for diagnosis of disease and prognosis of treatments in human.

Abstract: A Bowman's Refractive Index (BRI) for quantification of microdistortions in Bowman's Layer (BL) after Small Incision Lenticule Extraction (SMILE) is defined for a patient. BRI is summation of one or more areas of the OCT image of anterior edge of Bowman's layer, quantifies the smoothness of the Bowman's layer. The anterior edge of Bowman's layer is segmented into pixels. After segmentation, a 3rd order polynomial is curve fit to the segmented pixels of the edge of Bowman's layer. BRI is calculated by segmentation of the 3-Dimensional (3-D) OCT image. BRI acts as a marker for mechanical stability and is useful for diagnosis of disease and prognosis of treatments in human.

Abstract: 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.

Abstract: The invention relates to a method to quantify human corneal tissue parameters to improve biomechanical modelling of refractive and therapeutic procedures to reduce or eliminate refractive errors and unwanted wavefront aberrations. The analysis of the preoperative biomechanical properties of cornea assists in predicting the postoperative biomechanical response of cornea. The computational models of extreme properties of the corneal tissue are conducted and the 3-D finite element models are populated with tomographic information of the cornea and artificial intelligence is trained to consider the pre-operative characteristics of the corneal tissue. The post-operative refractive procedures are simulated using the said finite element models. Finally, biomechanical deformations are prospectively estimated and method builds a deep learning approach to link clinical features to changes in biochemical outcomes.

Abstract: 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.

Abstract: A Bowman's Refractive Index (BRI) for quantification of microdistortions in Bowman's Layer (BL) after Small Incision Lenticule Extraction (SMILE) is defined for a patient. BRI is summation of one or more areas of the OCT image of anterior edge of Bowman's layer, quantifies the smoothness of the Bowman's layer. The anterior edge of Bowman's layer is segmented into pixels. After segmentation, a 3rd order polynomial is curve fit to the segmented pixels of the edge of Bowman's layer. BRI is calculated by segmentation of the 3-Dimensional (3-D) OCT image. BRI acts as a marker for mechanical stability and is useful for diagnosis of disease and prognosis of treatments in human.

Abstract: Systems and methods are provided for predicting the results of a therapeutic intervention to an eye. An imaging system is configured to provide image data representing at least a portion of the eye of the patient. An input device is configured to permit a user to design a proposed therapeutic intervention for the eye of the patient. A finite element modeling component is configured to generate a finite element model representing the condition of the eye of the patient after the proposed therapeutic intervention according to the image data, the proposed therapeutic intervention, and at least one biomechanical property of tissue comprising the eye. The generated model is constructed as to have no a priori restraints on the motion of the corneal limbus. A display is configured to display the generated model to the user.

Abstract: Systems and methods are provided for predicting the results of a therapeutic intervention to an eye. An imaging system is configured to provide image data representing at least a portion of the eye of the patient. An input device is configured to permit a user to design a proposed therapeutic intervention for the eye of the patient. A finite element modeling component is configured to generate a finite element model representing the condition of the eye of the patient after the proposed therapeutic intervention according to the image data, the proposed therapeutic intervention, and at least one biomechanical property of tissue comprising the eye. The generated model is constructed as to have no a priori restraints on the motion of the corneal limbus. A display is configured to display the generated model to the user.

Abstract: Systems and methods are provided for predicting the results of a therapeutic intervention to an eye. An imaging system is configured to provide image data representing at least a portion of the eye of the patient. An input device is configured to permit a user to design a proposed therapeutic intervention for the eye of the patient. A finite element modeling component is configured to generate a finite element model representing the condition of the eye of the patient after the proposed therapeutic intervention according to the image data, the proposed therapeutic intervention, and at least one biomechanical property of tissue comprising the eye. The generated model is constructed as to have no a priori restraints on the motion of the corneal limbus. A display is configured to display the generated model to the user.