Glaucoma Detection

Detects the evidence of Glaucoma based on RNFL thickness and visual field (VF)

Overview

Glaucoma is characterized by dysfunction and loss of retinal ganglion cells (RGCs), with resulting structural changes to the optic nerve head, as well as loss of the visual field. This machine learning models predicts the diagnosis of glaucoma based on retinal nerve fiber layer (RNFL) thickness and visual field (VF). This model is not intended for medical diagnostic purpose.

Highlights

Binary classifier that is trained on basic features from the examination records for the glaucoma and healthy controls such as ocular pressure, cornea thickness, retinal nerve fiber layer (RNFL) thickness etc. to classify the condition as glaucoma or not
Acknowledgement: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0177726

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Glaucoma Detection

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Dimension	Description	Cost/host/hour
ml.m4.xlarge Inference (Batch) Recommended	Model inference on the ml.m4.xlarge instance type, batch mode	$0.25
ml.m4.xlarge Inference (Real-Time) Recommended	Model inference on the ml.m4.xlarge instance type, real-time mode	$0.50

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Amazon SageMaker model

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Version release notes

First version released to AWS ML Marketplace

Additional details

Inputs

Summary: Download the Jupyter notebook in "Additional Resources" section and follow readme.txt provided.

Input MIME type: text/csv

Real-time inference sample input data

See Input Summary

Batch transform sample input data

See Input Summary

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