Update: Deep-Learning for Detecting Diabetic Retinopathy #Google #Diabetes #Ophthalmology #HealthcareAI #MachineLearning #DeepLearning #ComputerVision @googleAI

Update on Google’s diabetic retinopathy project below! Take a look at the original post here.

Photo from Google AI Blog Post “Improving the Effectiveness of Diabetic Retinopathy Models”. Caption: “On the left is a fundus image graded as having proliferative (vision-threatening) DR by an adjudication panel of ophthalmologists (ground truth). On the top right is an illustration of our deep learning model’s predicted scores (“P” = proliferative, the most severe form of DR). On the bottom right is the set of grades given by physicians without assistance (“Unassisted”) and those who saw the model’s predictions (“Grades Only”).”


For the last several years Google has been working with clinics in India to develop a deep learning model to predict the severity of diabetic retinopathy (DR). In more advanced stages, DR can lead to vision loss and requires clinical intervention. Best practice suggests regular screening before the patient’s vision is impaired. In India, there are many patients that need this type of screening and a shortage of eye care specialists. That’s where deep learning comes in!

Back in 2016, Google trained a deep neural network classifier trained on anonymized retinal images to identify ‘Referrable DR’. The results were published in JAMA with the intent to improve the model and performance measurements. The last several years have shown improvements in model evaluation and proving out use cases. Several months ago the first prospective study for the project was published titled “Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India”.

The study utilized several different neural networks aimed at predicting the severity of DR from fundus images. During the study, additional improvements were made to the model with the addition of higher quality images, hyperparameter tuning and the use of the Inception-v4 neural network architecture.

During the course of the prospective data collection period, we made additional improvements to the model, including tuning the models with adjudicated data as reported by Krause et al. The improvements can be summarized as (1) larger training sets, (2) better hyperparameter exploration (tuning), (3) larger input image resolution, and (4) using the improved Inception-v4 neural network architecture. We graded the images using the model from Krause et al retrospectively at the conclusion of the study.
The study results indicate that the deep learning algorithm is able to automate DR grading to expand screening programs.
This study shows that the automated DR system generalizes to this population of Indian patients in a prospective setting and demonstrates the feasibility of using an automated DR grading system to expand screening programs.

Adafruit publishes a wide range of writing and video content, including interviews and reporting on the maker market and the wider technology world. Our standards page is intended as a guide to best practices that Adafruit uses, as well as an outline of the ethical standards Adafruit aspires to. While Adafruit is not an independent journalistic institution, Adafruit strives to be a fair, informative, and positive voice within the community – check it out here: adafruit.com/editorialstandards

Join Adafruit on Mastodon

Adafruit is on Mastodon, join in! adafruit.com/mastodon

Stop breadboarding and soldering – start making immediately! Adafruit’s Circuit Playground is jam-packed with LEDs, sensors, buttons, alligator clip pads and more. Build projects with Circuit Playground in a few minutes with the drag-and-drop MakeCode programming site, learn computer science using the CS Discoveries class on code.org, jump into CircuitPython to learn Python and hardware together, TinyGO, or even use the Arduino IDE. Circuit Playground Express is the newest and best Circuit Playground board, with support for CircuitPython, MakeCode, and Arduino. It has a powerful processor, 10 NeoPixels, mini speaker, InfraRed receive and transmit, two buttons, a switch, 14 alligator clip pads, and lots of sensors: capacitive touch, IR proximity, temperature, light, motion and sound. A whole wide world of electronics and coding is waiting for you, and it fits in the palm of your hand.

Have an amazing project to share? The Electronics Show and Tell is every Wednesday at 7pm ET! To join, head over to YouTube and check out the show’s live chat – we’ll post the link there.

Join us every Wednesday night at 8pm ET for Ask an Engineer!

Join over 36,000+ makers on Adafruit’s Discord channels and be part of the community! http://adafru.it/discord

CircuitPython – The easiest way to program microcontrollers – CircuitPython.org

Maker Business — “Packaging” chips in the US

Wearables — Enclosures help fight body humidity in costumes

Electronics — Transformers: More than meets the eye!

Python for Microcontrollers — Python on Microcontrollers Newsletter: Silicon Labs introduces CircuitPython support, and more! #CircuitPython #Python #micropython @ThePSF @Raspberry_Pi

Adafruit IoT Monthly — Guardian Robot, Weather-wise Umbrella Stand, and more!

Microsoft MakeCode — MakeCode Thank You!

EYE on NPI — Maxim’s Himalaya uSLIC Step-Down Power Module #EyeOnNPI @maximintegrated @digikey

New Products – Adafruit Industries – Makers, hackers, artists, designers and engineers! — #NewProds 7/19/23 Feat. Adafruit Matrix Portal S3 CircuitPython Powered Internet Display!

Get the only spam-free daily newsletter about wearables, running a "maker business", electronic tips and more! Subscribe at AdafruitDaily.com !

No Comments

No comments yet.

Sorry, the comment form is closed at this time.