Scientists from University of Pittsburgh have developed a novel algorithm to allow for comparisons between optical coherence tomography (OCT) or OCT angiography (OCTA) scans taken at various time points. This algorithm can generate 3D structural change maps between scans allowing clinicians to monitor disease progression in patients with retinal disease.
Description
OCT/A scans are now routinely used in eye clinics to acquire high resolution 3D retinal and choroidal images and can determine retinal thickness and subretinal fluid volume. Changes in these structures can be indicative of various retinal diseases. This novel algorithm is designed to assist clinicians in analyzing and comparing OCT/A scans collected at baseline and follow up timepoints. Easier comparison of OCT/A scans should allow for earlier diagnosis of retinal disease, better understanding of disease progression, reduced risk of vision loss, and improvements in treatment.
Applications
• Central serous chorioretinopathy (CSCR)
• Retinal diseases
Advantages
Current methods to compare changes in retinal thickness or subretinal fluid volume usually require complex processing such as retinal layer segmentation due to variables such as pupil entrance size or eye movement during OCT/A scans. Such limitations can impact on diagnosis, assessment of disease progression and/or treatment efficacy.
This novel algorithm eliminates many of these pitfalls through elimination of the need for time-consuming and error-prone retinal layer segmentation. By aligning the entire volume of the eye, a more comprehensive analysis of the entire retina and choroid is achieved. The algorithm can also detect changes that may be invisible in traditional analysis approaches and could be used to screen healthy patients improving early detection rates of various retinal diseases.
Invention Readiness
An algorithm has been developed using OCT/A scans from central serous chorioretinopathy (CSCR) patients. The algorithm has been shown to effectively compare OCT scans acquired over follow up visits without the need for prior layer segmentation, and accurately calculated three-dimensional (3-D) structural change maps for these patients. The algorithm also detected subtle retinal and choroidal changes which may have been missed with traditional analysis.
IP Status
https://patents.google.com/patent/WO2024206275A1
