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Purpose: To characterize the genotypic and phenotypic spectrum of foveal hypoplasia (FH). Design: Multicenter, observational study. Participants: A total of 907 patients with a confirmed molecular diagnosis of albinism, PAX6, SLC38A8, FRMD7, AHR, or achromatopsia from 12 centers in 9 countries (n = 523) or extracted from publicly available datasets from previously reported literature (n = 384). Methods: Individuals with a confirmed molecular diagnosis and availability of foveal OCT scans were identified from 12 centers or from the literature between January 2011 and March 2021. A genetic diagnosis was confirmed by sequence analysis. Grading of FH was derived from OCT scans. Main Outcome Measures: Grade of FH, presence or absence of photoreceptor specialization (PRS+ vs. PRS–), molecular diagnosis, and visual acuity (VA). Results: The most common genetic etiology for typical FH in our cohort was albinism (67.5%), followed by PAX6 (21.8%), SLC38A8 (6.8%), and FRMD7 (3.5%) variants. AHR variants were rare (0.4%). Atypical FH was seen in 67.4% of achromatopsia cases. Atypical FH in achromatopsia had significantly worse VA than typical FH (P < 0.0001). There was a significant difference in the spectrum of FH grades based on the molecular diagnosis (chi-square = 60.4, P < 0.0001). All SLC38A8 cases were PRS– (P = 0.003), whereas all FRMD7 cases were PRS+ (P < 0.0001). Analysis of albinism subtypes revealed a significant difference in the grade of FH (chi-square = 31.4, P < 0.0001) and VA (P = 0.0003) between oculocutaneous albinism (OCA) compared with ocular albinism (OA) and Hermansky–Pudlak syndrome (HPS). Ocular albinism and HPS demonstrated higher grades of FH and worse VA than OCA. There was a significant difference (P < 0.0001) in VA between FRMD7 variants compared with other diagnoses associated with FH. Conclusions: We characterized the phenotypic and genotypic spectrum of FH. Atypical FH is associated with a worse prognosis than all other forms

Purpose: To characterize the genotypic and phenotypic spectrum of foveal hypoplasia (FH). Design: Multicenter, observational study. Participants: A total of 907 patients with a confirmed molecular diagnosis of albinism, PAX6, SLC38A8, FRMD7, AHR, or achromatopsia from 12 centers in 9 countries (n = 523) or extracted from publicly available datasets from previously reported literature (n = 384). Methods: Individuals with a confirmed molecular diagnosis and availability of foveal OCT scans were identified from 12 centers or from the literature between January 2011 and March 2021. A genetic diagnosis was confirmed by sequence analysis. Grading of FH was derived from OCT scans. Main Outcome Measures: Grade of FH, presence or absence of photoreceptor specialization (PRS+ vs. PRS–), molecular diagnosis, and visual acuity (VA). Results: The most common genetic etiology for typical FH in our cohort was albinism (67.5%), followed by PAX6 (21.8%), SLC38A8 (6.8%), and FRMD7 (3.5%) variants. AHR variants were rare (0.4%). Atypical FH was seen in 67.4% of achromatopsia cases. Atypical FH in achromatopsia had significantly worse VA than typical FH (P < 0.0001). There was a significant difference in the spectrum of FH grades based on the molecular diagnosis (chi-square = 60.4, P < 0.0001). All SLC38A8 cases were PRS– (P = 0.003), whereas all FRMD7 cases were PRS+ (P < 0.0001). Analysis of albinism subtypes revealed a significant difference in the grade of FH (chi-square = 31.4, P < 0.0001) and VA (P = 0.0003) between oculocutaneous albinism (OCA) compared with ocular albinism (OA) and Hermansky–Pudlak syndrome (HPS). Ocular albinism and HPS demonstrated higher grades of FH and worse VA than OCA. There was a significant difference (P < 0.0001) in VA between FRMD7 variants compared with other diagnoses associated with FH. Conclusions: We characterized the phenotypic and genotypic spectrum of FH. Atypical FH is associated with a worse prognosis than all other forms

Quality assessment of agricultural produce is a crucial step in minimizing food stock wastage. However, this is currently done manually and often requires expert supervision, especially in smaller seeds like corn. We propose a novel computer vision-based system for automating this process. We build a novel seed image acquisition setup, which captures both the top and bottom views. Dataset collection for this problem has challenges of data annotation costs/time and class imbalance. We address these challenges by i.) using a Conditional Generative Adversarial Network (CGAN) to generate real-looking images for the classes with lesser images and ii.) annotate a large dataset with minimal expert human intervention by using a Batch Active Learning (BAL) based annotation tool. We benchmark different image classification models on the dataset obtained. We are able to get accuracies of up to 91.6% for testing the physical purity of seed samples.