Method for assisting corneal severity identification using unsupervised machine learning

A method for assisting corneal severity identification, the method comprising:

• obtaining a corneal configuration data set of a cornea to be examined by a tomography;
• visualizing the corneal configuration data set of the cornea to be examined along with a number of pre-existing corneal configuration data sets of disorder corneas, disorder-suspect corneas and normal corneas obtained by the tomography using t-distributed Stochastic Neighbor Embedding in a two or three dimensional map, and
• judging corneal severity from the map;
• wherein the corneal severity is at least one selected from the group consisting of keratoconus severity, bullous keratopathy severity, walleye severity, keratoleukoma severity, keratohelcosis severity, herpes corneae severity, corneal chemical burn severity, corneal thermal burn severity, and degeneratio corneae severity.

A method for assisting corneal severity identification, the method comprising:

• obtaining a corneal configuration data set of a cornea to be examined by a tomography;
• visualizing the corneal configuration data set of the cornea to be examined along with a number of pre-existing corneal configuration data sets of disorder corneas, disorder-suspect corneas and normal corneas obtained by the tomography using t-distributed Stochastic Neighbor Embedding in a two or three dimensional map, and
• judging corneal severity from the map;
• wherein the corneal configuration data set comprises at least one of a 2D analysis of ACA viewing surface, a 2D analysis of CCT/ACD viewing surface, STAR360°, analysis of lens morphology on 2D Result, analysis of lens morphology on 3D Result, analysis of corneal morphology and reference point.

A method for assisting corneal severity identification, the method comprising:

• obtaining a corneal configuration data set of a cornea to be examined by a tomography;
• visualizing the corneal configuration data set of the cornea to be examined along with a number of pre-existing corneal configuration data sets of disorder corneas, disorder-suspect corneas and normal corneas obtained by the tomography using t-distributed Stochastic Neighbor Embedding in a two or three dimensional map, and
• judging corneal severity from the map;
• wherein the corneal configuration data set comprises at least one of DSI, OSI, CSI, SD_P(4 mm), CV_P(4 mm), ACP(3 mm), RMS_E(4 mm), SR_E(4 mm), SR_H(4 mm), CSI_T, SD_T(4 mm), and CV_T(4 mm).

A method for assisting corneal severity identification, the method comprising:

• obtaining a corneal configuration data set of a cornea to be examined by a tomography;
• visualizing the corneal configuration data set of the cornea to be examined along with a number of pre-existing corneal configuration data sets of disorder corneas, disorder-suspect corneas and normal corneas obtained by the tomography using t-distributed Stochastic Neighbor Embedding in a two or three dimensional map, and
• judging corneal severity from the map;
• wherein the tomography is an anterior eye part optical coherence tomography.

A method for assisting corneal severity identification, the method comprising:

• obtaining a corneal configuration data set of a cornea to be examined by a tomography;
• visualizing the corneal configuration data set of the cornea to be examined along with a number of pre-existing corneal configuration data sets of disorder corneas, disorder-suspect corneas and normal corneas obtained by the tomography using t-distributed Stochastic Neighbor Embedding in a two or three dimensional map, and
• judging corneal severity from the map;
• wherein the tomography is a swept-source optical coherence tomography.