development-of-a-surgical-technique-for-subretinal-implants-in-rats

Development of a Surgical Technique for Subretinal Implants in Rats

Retinal degeneration, such as age-related macular degeneration (AMD), is a leading cause of blindness worldwide. A myriad of approaches have been ...

Keck School of Medicine of the University of Southern California

Tsinghua University

To evaluate the feasibility of a new technique for the implantation of ultrathin substrates containing stem cell-derived retinal pigment epithelium (RPE) cells into the subretinal space of retina-degenerate Royal College of Surgeon (RCS) rats.

A novel approach for subretinal implantation of ultrathin substrates containing stem cell-derived retinal pigment epithelium monolayer.

To evaluate cell survival and tumorigenicity of human embryonic stem cell-derived retinal pigment epithelium (hESC-RPE) transplantation in immunocompromised nude rats. Cells were transplanted as a cell suspension (CS) or as a polarized monolayer plated on a parylene membrane (PM).

Subretinal implantation of retinal pigment epithelial cells derived from human embryonic stem cells: improved survival when implanted as a monolayer.

Epiretinal membrane (ERM) is a common ophthalmological disorder of high prevalence. Its symptoms include metamorphopsia, blurred vision, and decreased visual acuity. Early diagnosis and timely treatment of ERM is crucial to preventing vision loss. Although optical coherence tomography (OCT) is regarded as a de facto standard for ERM diagnosis due to its intuitiveness and high sensitivity, ophthalmoscopic examination or fundus photographs still have the advantages of price and accessibility. Artificial intelligence (AI) has been widely applied in the health care industry for its robust and significant performance in detecting various diseases. In this study, we validated the use of a previously trained deep neural network based-AI model in ERM detection based on color fundus photographs. An independent test set of fundus photographs was labeled by a group of ophthalmologists according to their corresponding OCT images as the gold standard. Then the test set was interpreted by other ophthalmologists and AI model without knowing their OCT results. Compared with manual diagnosis based on fundus photographs alone, the AI model had comparable accuracy (AI model 77.08% vs. integrated manual diagnosis 75.69%, χ = 0.038, P = 0.845, McNemar's test), higher sensitivity (75.90% vs. 63.86%, χ = 4.500, P = 0.034, McNemar's test), under the cost of lower but reasonable specificity (78.69% vs. 91.80%, χ = 6.125, P = 0.013, McNemar's test). Thus our AI model can serve as a possible alternative for manual diagnosis in ERM screening.

Article	Year
A novel approach for subretinal implantation of ultrathin substrates containing stem cell-derived retinal pigment epithelium monolayer. Ophthalmic research\| PubMed ID: 22868580	2012
Subretinal implantation of retinal pigment epithelial cells derived from human embryonic stem cells: improved survival when implanted as a monolayer. Investigative ophthalmology & visual science\| PubMed ID: 23833067	2013
Artificial intelligence-based detection of epimacular membrane from color fundus photographs. Scientific reports\| PubMed ID: 34588493	2021

Yuntao Hu

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