A Deep Learning Approach for Automated Detection of Shallow Anterior Chamber Depth Based on Hidden Features of Fundus Photographs
Version 1The datasets are not redistributable to researchers other than those engaged in the Institutional Review Board-approved research collaborations with the B&VIIt Eye Center, South Korea. The datasets utilized during this study are not publicly available due to reasonable privacy and security concerns. Instead, a sample anonymized fundus photography data with shallow and deep ACD is available in the publicly accessible source. Note that this was not the exact data used in the research, but is a cle
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The datasets are not redistributable to researchers other than those engaged in the Institutional Review Board-approved research collaborations with the B&VIIt Eye Center, South Korea. The datasets utilized during this study are not publicly available due to reasonable privacy and security concerns. Instead, a sample anonymized fundus photography data with shallow and deep ACD is available in the publicly accessible source. Note that this was not the exact data used in the research, but is a cle