The AI tool aims to solve the problem of efficient and accessible screening for Diabetic Retinopathy (DR), a leading cause of blindness in adults with diabetes. Early detection and timely intervention are crucial to prevent vision loss, but traditional screening methods often face challenges related to accessibility, cost, and the availability of trained specialists.

Diabetic Retinopathy is a complication of diabetes that affects the blood vessels in the retina. In many regions, particularly in Africa, access to ophthalmologists and specialized screening facilities is limited, resulting in delayed diagnosis and treatment. 

If Diabetic Retinopathy is not addressed, it can progress through various stages, eventually leading to severe vision loss and blindness.

This AI solution addresses the bottleneck in the DR screening process. It aims to automate the initial assessment of fundus images, enabling faster and more widespread screening, especially in areas with limited access to specialists.

• Difficulty accessing ophthalmologists or specialized clinics, particularly in rural or underserved areas.
• High costs associated with traditional screening methods.

DR screening involves manual examination of fundus images by trained ophthalmologists or optometrists.

• Increasing screening efficiency: Processing fundus images faster than manual review.
• Improving accessibility: Enabling screening in remote or underserved areas where specialists are scarce.
• Potential for lowering screening costs

Potential for earlier detection

In 2020, an estimated 103.12 million adults globally were living with Diabetic Retinopathy (DR). This number is projected to reach 160.50 million by 2045.

Tanzania faces a critical shortage of ophthalmologists, with only 69 serving a population of over 60 million, resulting in a ratio of approximately 0.8 ophthalmologists per million people (2019). This falls significantly short of the WHO recommendation of 1 per 250,000.

The number of individuals with diabetes in sub-Saharan Africa is projected to increase from 19.4 million in 2019 to 47.1 million by 2045, marking a significant rise in the population at risk for DR.

The purpose of the Automated Screening of Diabetic Retinopathy (ASDR) tool is to automatically analyze fundus images to detect the presence and severity level of diabetic retinopathy.

This AI tool was developed through AfriAI LAB

The tool utilizes deep learning, specifically convolutional neural networks (Convnets), for image analysis and classification.

The dataset used was a mixture of locally collected data and second hand data

The fundus images were validated and annotated by trained ophthalmologists who labeled each image with the corresponding DR severity level

• Image Upload: Medical professionals upload a retinal image via a web user interface.
• Automated Analysis: The uploaded image is processed by a pipeline of AI models.
• Fundus Image Detection: The first Convnet model determines if the uploaded image is a valid fundus image.

• Severity Grading: If identified as a fundus image, the second ResNet Convnet model classifies the image into one of five Diabetic Retinopathy severity levels.
• Result Display: The AI's classification result is displayed to the medical professional.

Optional Validation: If the medical professional is uncertain about the AI's result, they can submit the image for validation by a registered ophthalmologist within the system.

• Trained Technicians: To conduct initial screenings by uploading images and reviewing AI-generated results, potentially referring cases for validation.

Ophthalmologists and Optometrists: To efficiently screen patients, prioritize complex cases, and perform validation of AI results

The primary input is fundus images of the patient's retina uploaded through the user interface.

The tool provides the probability for each of the following Diabetic Retinopathy severity classes: No DR, Mild DR, Moderate DR, Severe DR, and Proliferative DR. The class with the highest probability is considered the AI's prediction.

No  , recommendations or decisions are provided by the Ophthalmologists and Optometrists after results

It dramatically improves efficiency by automating fundus image analysis for rapid and scalable screening, and can concurrently process multiple requests.

The AI tool can analyze a fundus image within seconds, significantly reducing the time required for manual review by a specialist. This can lead to faster screening results.

By automating the initial screening process, the tool can be deployed in settings with limited specialist availability, enabling broader access to DR screening in underserved communities. It can also facilitate high-throughput screening programs.

A prototype is under development.

A prototype is under development and not available to the public.

The documentation is being developed

The tool's performance might vary when applied to images acquired using different fundus camera models or under different imaging conditions.

N/A

Testing the tool on datasets that were not used for training.

The tool's accuracy in real-world conditions is currently being evaluated through ongoing tests.

Variations in image quality, illumination, and artifacts can affect the tool's performance.

How does the AI tool's performance compare to that of human graders with varying levels of experience?

Discrepancies may arise due to subtle features that are challenging for the AI to detect or due to variations in human labelling standards

Limitations in the training data amount and qality.

Continuous monitoring of the tool's performance in real-world deployments, with periodic retraining using new data .

• Facilitating large-scale screening programs for early detection of DR in the population.

Streamlining DR screening, particularly in areas with limited specialist access.

By providing data on screening coverage and the prevalence of DR in different regions.

• Increasing accessibility of screening
• potential cost reduction of screening

Reducing waiting times of screening

The tool can be deployed in other regions facing similar challenges in DR screening, provided it is validated on local datasets.

To become a widely adopted and reliable tool for automated DR screening, contributing to a significant reduction in preventable blindness.

Developing user-friendly interfaces for different user groups (e.g., specialists, technicians, community health workers).

Partnering with hospitals and clinics for real-world validation and deployment.