Ophthalmic imaging examination is the main basis for early screening, evaluation and diagnosis of eye diseases. In recent years, with the improvement of computer data analysis ability, the deepening of new algorithm research and the popularization of big data platform, artificial intelligence (AI) technology has developed rapidly and become a hot topic in the field of medical assistant diagnosis. The advantage of AI is accurate and efficient, which has great application value in processing image-related data. The application of AI not only helps to promote the development of AI research in ophthalmology, but also helps to establish a new medical service model for ophthalmic diagnosis and promote the process of prevention and treatment of blindness. Future research of ophthalmic AI should use multi-modal imaging data comprehensively to diagnose complex eye diseases, integrate standardized and high-quality data resources, and improve the performance of algorithms.
Objective To analyze the pathogenic gene and clinical phenotypes of a family affected with rare sector retinitis pigmentosa (sector RP). Methods A retrospective clinical study. A patient with sector RP diagnosed in Renmin Hospital of Wuhan University and his parents were included in the study. Detailed medical history was collected; best corrected visual acuity (BCVA), fundus color photography, autofluorescence (AF), visual field, optical coherence tomography (OCT), electroretinogram, fluorescein fundus angiography (FFA), indocyanine green angiography (ICGA) examination were performed. The peripheral venous blood of the patient and his parents were collected, and DNA was extracted. A whole exon sequencing was used for the proband. The mutations were verified by targeted Sanger sequencing and quantitative polymerase chain reaction. Bioinformatics analysis and cosegregation analysis were performed. ResultsThe proband, a 17-year-old male, had presented with gradually decreased vision in the past 2 years with BCVA of 0.4 in both eyes. Retinal vessels attenuation and macular dystrophy without obvious pigmentation on the fundus were observed. AF showed, in bilateral eyes, a symmetrical hypo-autofluorescent region only in the inferonasal quadrant and “petal-like” hyper-AF macula. The visual field examination showed defects in the superotemporal quadrant corresponding to the affected retina. OCT showed loss of the photoreceptor layer except for the foveal region. Electroretinogram examination presented reduced scotopic wave peaks and extinct photopic response. FFA and ICGA showed the atrophy retinal pigment epithelium around the optic disk and in the inferior retina. The clinical phenotypes of the parents were normal. The whole exon sequencing identified one mutation in SPATA7 gene, c.1112T>C (p.Ile371Thr) in exon10 and a copy number variation in trans. The missense mutation resulted in the change of isoleucine to threonine at amino acid 371 in the encoded SPATA7 protein, and the mother carried this heterozygous mutation c.1112T>C. According to the guidelines of the American College of Medical Genetics and Genomics (ACMG) criteria and guidelines for classification of genetic variants, the missense mutation was classified as the uncertain significance. The CNV, originating from his father, contributed to the loss of exon10 and was confirmed as the likely pathogenic variant. ConclusionsThe macula can be involved in sector RP, leading to the macular dystrophy. The missense variant in SPATA7 gene, c.1112T>C (p.Ile371Thr), might be a pathogenic mutation site in this pedigree.