Explainable machine learning reveals a compact, low-dimensional geometric basis underlying sheet and skeletal TPMS architected materials - Nature

We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply. AbstractAlthough retinal fundus imaging is a non-invasive tool to assess systemic and fundus pathology, current deep learning algorithms are mostly single-task, lack clinical interpretability and are unable to handle acquisition variation between different domains. These constraints limit their clinical utility: when a patient comes to the clinic for fundus screening, they could also be diabetic, have glaucoma, AMD, and increased cardiovascular risk, with each disease having retinal vascular biomarkers that require a patient to be tested for in the clinic, which must be managed within a framework of common, interpretable, and generalizable models rather than a pipeline of independent models.