ABSTRACT
BACKGROUND: The examination of the optic nerve head (optic disc) is mandatory in patients with headache, hypertension, or any neurological symptoms, yet it is rarely or poorly performed in general clinics. We recently developed a brain and optic nerve study with artificial intelligence-deep learning system (BONSAI-DLS) capable of accurately detecting optic disc abnormalities including papilledema (swelling due to elevated intracranial pressure) on digital fundus photographs with a comparable classification performance to expert neuro-ophthalmologists, but its performance compared to first-line clinicians remains unknown. METHODS: In this international, cross-sectional multicenter study, the DLS, trained on 14,341 fundus photographs, was tested on a retrospectively collected convenience sample of 800 photographs (400 normal optic discs, 201 papilledema and 199 other abnormalities) from 454 patients with a robust ground truth diagnosis provided by the referring expert neuro-ophthalmologists. The areas under the receiver-operating-characteristic curves were calculated for the BONSAI-DLS. Error rates, accuracy, sensitivity, and specificity of the algorithm were compared with those of 30 clinicians with or without ophthalmic training (6 general ophthalmologists, 6 optometrists, 6 neurologists, 6 internists, 6 emergency department [ED] physicians) who graded the same testing set of images. RESULTS: With an error rate of 15.3%, the DLS outperformed all clinicians (average error rates 24.4%, 24.8%, 38.2%, 44.8%, 47.9% for general ophthalmologists, optometrists, neurologists, internists and ED physicians, respectively) in the overall classification of optic disc appearance. The DLS displayed significantly higher accuracies than 100%, 86.7% and 93.3% of clinicians (n = 30) for the classification of papilledema, normal, and other disc abnormalities, respectively. CONCLUSIONS: The performance of the BONSAI-DLS to classify optic discs on fundus photographs was superior to that of clinicians with or without ophthalmic training. A trained DLS may offer valuable diagnostic aid to clinicians from various clinical settings for the screening of optic disc abnormalities harboring potentially sight- or life-threatening neurological conditions.
Subject(s)
Deep Learning , Optic Disk , Papilledema , Humans , Optic Disk/diagnostic imaging , Artificial Intelligence , Retrospective Studies , Cross-Sectional StudiesABSTRACT
Polypoidal choroidal vasculopathy (PCV), a subtype of 'wet' age-related macular degeneration (AMD), constitutes up to 55% of cases of wet AMD in Asian patients. In contrast to the choroidal neovascularization (CNV) subtype, the genetic risk factors for PCV are relatively unknown. Exome sequencing analysis of a Han Chinese cohort followed by replication in four independent cohorts identified a rare c.986A>G (p.Lys329Arg) variant in the FGD6 gene as significantly associated with PCV (P = 2.19 × 10(-16), odds ratio (OR) = 2.12) but not with CNV (P = 0.26, OR = 1.13). The intracellular localization of FGD6-Arg329 is distinct from that of FGD6-Lys329. In vitro, FGD6 could regulate proangiogenic activity, and oxidized phospholipids increased expression of FGD6. FGD6-Arg329 promoted more abnormal vessel development in the mouse retina than FGD6-Lys329. Collectively, our data suggest that oxidized phospholipids and FGD6-Arg329 might act synergistically to increase susceptibility to PCV.
Subject(s)
Guanine Nucleotide Exchange Factors/genetics , Mutation, Missense , Wet Macular Degeneration/genetics , Cells, Cultured , China , Cohort Studies , Endothelium, Vascular/cytology , Endothelium, Vascular/metabolism , Ethnicity , Gene Expression Profiling , Guanine Nucleotide Exchange Factors/metabolism , Humans , Polymorphism, Single Nucleotide , Subcellular Fractions/metabolismABSTRACT
Glaucoma is the second leading cause of blindness. Glaucoma can be diagnosed through measurement of neuro-retinal optic cup-to-disc ratio (CDR). Automatic calculation of optic cup boundary is challenging due to the interweavement of blood vessels with the surrounding tissues around the cup. A Convex Hull based Neuro-Retinal Optic Cup Ellipse Optimization algorithm improves the accuracy of the boundary estimation. The algorithm's effectiveness is demonstrated on 70 clinical patient's data set collected from Singapore Eye Research Institute. The root mean squared error of the new algorithm is 43% better than the ARGALI system which is the state-of-the-art. This further leads to a large clinical evaluation of the algorithm involving 15 thousand patients from Australia and Singapore.
