No abstract

Artificial intelligence (AI) based on convolutional neural networks (CNNs) has a great potential to enhance medical workflow and improve health care quality. Of particular interest is practical implementation of such AI-based software as a cloud-based tool aimed for telemedicine, the practice of providing medical care from a distance using electronic interfaces.Methods: In this study, we used a dataset of labeled 35,900 optical coherence tomography (OCT) images obtained from age-related macular degeneration (AMD) patients and used them to train three types of CNNs to perform AMD diagnosis.Results: Here, we present an AI- and cloud-based telemedicine interaction tool for diagnosis and proposed treatment of AMD. Through deep learning process based on the analysis of preprocessed optical coherence tomography (OCT) imaging data, our AI-based system achieved the same image discrimination rate as that of retinal specialists in our hospital. The AI platform's detection accuracy was generally higher than 90% and was significantly superior (p < 0.001) to that of medical students (69.4% and 68.9%) and equal (p = 0.99) to that of retinal specialists (92.73% and 91.90%). Furthermore, it provided appropriate treatment recommendations comparable to those of retinal specialists.Conclusions: We therefore developed a website for realistic cloud computing based on this AI platform, available at https://www.ym.edu.tw/~AI-OCT/. Patients can upload their OCT images to the website to verify whether they have AMD and require treatment. Using an AI-based cloud service represents a real solution for medical imaging diagnostics and telemedicine.

SummaryX-linked juvenile retinoschisis (XLRS), linked to mutations in the RS1 gene, is a degenerative retinopathy with a retinal splitting phenotype. We generated human induced pluripotent stem cells (hiPSCs) from patients to study XLRS in a 3D retinal organoid in vitro differentiation system. This model recapitulates key features of XLRS including retinal splitting, defective retinoschisin production, outer-segment defects, abnormal paxillin turnover, and impaired ER-Golgi transportation. RS1 mutation also affects the development of photoreceptor sensory cilia and results in altered expression of other retinopathy-associated genes. CRISPR/Cas9 correction of the disease-associated C625T mutation normalizes the splitting phenotype, outer-segment defects, paxillin dynamics, ciliary marker expression, and transcriptome profiles. Likewise, mutating RS1 in control hiPSCs produces the disease-associated phenotypes. Finally, we show that the C625T mutation can be repaired precisely and efficiently using a base-editing approach. Taken together, our data establish 3D organoids as a valid disease model.