Accelerating retinal disease detection at low cost
Impact
Challenges
Work with a large variety of data types needed to diagnose eye condition
Speed up the labeling process to enrich the AI model and scale
Solutions
Versatile platform able to annotate any type of data
Customizable interfaces and reviewing features to accomodate any use case
User-friendly collaborative tools to better share data between stakeholders
Consensus and honeypot quality control feature ensure high-standard quality results
Machine learning in the loop to speed up the annotation process
Context
A European healthcare technology company focusing on innovations for ophthalmology aims to make eye screening more accessible to a wider public with accelerated speed and reduced cost. The goal is to improve care for eye patients and to detect eye disease as early as possible.
To proceed at scale, AI is adopted at the core of the eye screening model, and the company partnered with Kili Technology to ensure the accuracy of this model with clean labeled retinal data of patients.
How it all started
The mission is to improve people’s vision at a scale
Today, millions of people in the world are at risk or already have vision-threatening eye conditions that are left undetected, such as diabetic retinopathy or glaucoma. Some people even have eye conditions that are rooted in even more serious life-threatening causes such as stroke or cardiovascular disease – which also are left undetected. While traditional eye screenings are fairly costly and time-consuming, with artificial intelligence the company has the ambition to accelerate this process to provide access to the wide public and detect all possible eye conditions within seconds at low cost, at scale.
To deliver precise eye diagnosis within seconds, the company acknowledged that its AI eye screening model needed to be trained rigorously and enriched with a combination of accurately annotated data – such as retinal photography, CT scan images, and medical reports. Thus, the data annotation process is essential to perform eye screening at scale to achieve the company's mission to improve people’s vision.
Challenge
Enriching the AI eye screening model
Understanding the variety of patient data needed to diagnose eye conditions, the company realized the complexity and challenge to train the AI eye screening model. One of the challenges is finding a solution that is fully compatible with the different data types, especially for specific ones such as CT scan data. The company found it difficult to manage when they needed to host the labeling process with different tools to annotate different data, as each tool has a different flow some are more complex than the other, and the whole process became inefficient.
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I don’t think many tools can annotate our data types. There were not many that can perform up to our standards as well. Let me tell you, using different tools at once can get your project to be all over the place. It was a headache.
Another challenge is the need to speed up the labeling process, as to enrich the AI model, they need to only train rigorously in terms of rounded data types, but also terms of voluminous amount – while ensuring precise quality. When the team annotates retinal and CT scan images, these images need to be cut down into manageable sizes to prevent being lost and missing details. Therefore, the total amount of training datasets multiplied significantly, and the annotation process and ensuring quality is certainly time-consuming.
Solution
A versatile training data platform compatible with specific data types and ensuring high-standard quality results
To solve these challenges, the healthcare company benchmarked Kili against multiple players in the market, including the different ones the company adopted previously. The company considered it highly practical when it found that Kili could accommodate all the data types needed – including CT scans format. On top of that, the customizable interface and reviewing features also improved their collaboration and flow of data annotation work.
Moreover, the company viewed Kili Technology as very efficient in being fast while keeping a high standard of quality of annotation. The ability to plug in machine learning in the loop within the Kili platform for the annotation process speeds up the annotation significantly. Also, the quality management features such as consensus and honeypot facilitated the company to scrutinize the quality of data labels effectively.
Versatile platform able to annotate any type of data
User-friendly collaborative tools to better share data between stakeholders
Machine learning in the loop to speed up the annotation process
Machine learning in the loop to speed up the annotation process
Consensus and honeypot quality control feature ensure high-standard quality results
It’s very convenient and simple to use. And when it’s simple to use, you get to be extremely more productive. No more headaches.” continues Alexander in his remarks on using Kili
Impact
Our mission is achieved – and will continue
Simplifying and scaling up the data annotation process has enabled the healthcare technology company to perform fast delivery of eye diagnosis and to accurately detect potentially dangerous eye conditions early – at scale. Previously, patients who undergo eye diagnosis would receive the results in days or even weeks.
With an artificial intelligence model enriched with labeled training data, now patients the result can be obtained in real-time within a minute. It also reduces the cost of performing eye diagnosis by 40%, making it more accessible to the wider public.
It’s exciting to see our model perform at hospitals and eye clinics. People get diagnosed within a minute. Our mission is achieved and certainly will continue.
Lesson Learned
Accurately labeled images play a critical role in determining the accuracy of a cancer diagnostic tool since it involves decisions that impact people’s lives. Hence investing time and effort in data labeling is of the utmost importance to ensure the success of the project.
Flexibility to apply machine learning in the loop during the image annotation process is essential to enable improved speed for 70% time saving of data labeling
It is important to select a suitable data annotation partner to develop a cancer detection tool. Factors such as robustness, meticulous quality management, and simplicity of collaboration are key.
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