Automated retinal imaging and trend analysis – a tool for health monitoring
Karin Roesch, Tristan Swedish, Ramesh Raskar MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA Abstract: Most current diagnostic devices are expensive, require trained specialists to operate and gather static images with sparse data points. This leads to preventable diseases...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
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Dove Medical Press
2017
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/91c6df8a45c441c5a63e671eea5b0b6f |
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| Sumario: | Karin Roesch, Tristan Swedish, Ramesh Raskar MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA Abstract: Most current diagnostic devices are expensive, require trained specialists to operate and gather static images with sparse data points. This leads to preventable diseases going undetected until late stage, resulting in greatly narrowed treatment options. This is especially true for retinal imaging. Future solutions are low cost, portable, self-administered by the patient, and capable of providing multiple data points, population analysis, and trending. This enables preventative interventions through mass accessibility, constant monitoring, and predictive modeling. Keywords: next-generation imaging technology, early disease indicators, predictive health assessment, predictive analysis, mass accessibility |
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