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PurposeTo qualitatively and quantitatively characterise the genotypes and phenotypes of Bietti’s crystalline dystrophy (BCD) in a cohort of patients.DesignCross-sectional and observational study.MethodsClinically confirmed BCD patients were recruited for genotyping and phenotyping. Multiple retinal imaging modalities were employed. Atrophy in the fovea was adopted as major consideration for staging strategy, while percentage area of autofluorescence (AF) atrophy (PAFA) in the macula was determined for quantitation.ResultsIn 74 clinically diagnosed BCD patients, c.802–8_810del17insGC was shown the predominant variant of the CYP4V2gene (allele frequency 55.4%). Sixty-two cases (123 eyes) with full imaging data were classified according to a modified criterion into stages 1 (n=8, 6.50%), 2A (n=9, 7.32%), 2B (n=17, 13.82%), 3A (n=30, 24.39%) and 3B (n=59, 47.97%). The eyes of the stage 2B were particularly deemed ‘high risk’ due to atrophy near fovea, while in stage 3A, though with remarkable foveal atrophy, preserved retinal pigment epithelium/photoreceptor islands near the fovea were found in 14 eyes. A tendency of increase in PAFA with age was found (rs=0.31, p=0.014). Significant PAFA increase was shown through stages 1 to 3B, and best-corrected visual acuity (BCVA, Logarithm of the Minimum Angle of Resolution) was shown to moderately correlate with PAFA (rs=0.56, p<0.001).ConclusionThe PAFA might be an efficient biomarker for BCD severities correlating with BCVA. The highly heterogeneous chorioretinopathy and BCVA of BCD cases appear to be associated with disease stages, progression types and patients’ ages. Foveal involvement should be of a major concern for consideration of potential therapeutic intervention.

Supplemental Digital Content is Available in the Text.Complicated macular neovascularization (MNV) in Bietti crystalline dystrophy (BCD) was shown common with bilateral involvement on multimodal retinal imaging. The distribution and activities of the MNVs seemed to be associated with the primary chorioretinal compromise.

Leveraging deep learning-based techniques to classify diseases has attracted extensive research interest in recent years. Nevertheless, most of the current studies only consider single-modal medical images, and the number of ophthalmic diseases that can be classified is relatively small. Moreover, imbalanced data distribution of different ophthalmic diseases is not taken into consideration, which limits the application of deep learning techniques in realistic clinical scenes. In this paper, we propose a Multimodal Multi-disease Long-tailed Classification Network (M2LC-Net) in response to the challenges mentioned above. M2LC-Net leverages ResNet18-CBAM to extract features from fundus images and Optical Coherence Tomography (OCT) images, respectively, and conduct feature fusion to classify 11 common ophthalmic diseases. Moreover, Class Activation Mapping (CAM) is employed to visualize each mode to improve interpretability of M2LC-Net. We conduct comprehensive experiments on realistic dataset collected from a Grade III Level A ophthalmology hospital in China, including 34,396 images of 11 disease labels. Experimental results demonstrate effectiveness of our proposed model M2LC-Net. Compared with the state-of-the-art, various performance metrics have been improved significantly. Specifically, Cohen's kappa coefficient κ has been improved by 3.21%, which is a remarkable improvement.