A pretrained corneal clarity classifier that sorts an image into one of 10 categories — the level of corneal clarity in various eye conditions. Use the corneal clarity API immediately, no training required, then adapt it to your own data when you need more.
Drop in a photo and get the prediction back. No signup, no setup.
A sample of the 12 labels this pretrained classifier chooses between.
Need a label that isn't here? Clone the classifier into your Nyckel console and edit the label set to fit your data.
Once you've added this classifier to your console, you get your own copy of it behind your own endpoint. Invoke it with any HTTP client:
curl
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer $NYCKEL_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "https://example.com/photo.jpg"}'Python
import requests
# Get an access token: https://www.nyckel.com/docs/api/overview/authentication/
token = "YOUR_ACCESS_TOKEN"
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer " + token},
json={"data": "https://example.com/photo.jpg"},
)
print(response.json())Example response
{
"labelName": "Blurred",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 corneal clarity categories, served on Nyckel's own infrastructure — your image stays on Nyckel.
Send an image URL or file to the invoke endpoint; the response is a label with a confidence score.
Clone it, then correct predictions and add your own samples in the console — Nyckel retrains automatically, turning this into a custom model tuned to your data.
The 'corneal clarity' identifier can assist ophthalmologists in diagnosing conditions related to corneal opacities or cloudiness. By accurately classifying the clarity of the cornea, practitioners can provide more targeted treatment plans and monitor progression over time.
This function can be integrated into telemedicine platforms to enhance remote eye examinations. By allowing healthcare providers to assess corneal clarity from images submitted by patients, it increases accessibility to eye care, especially in remote or underserved areas.
Eye clinics can utilize the 'corneal clarity' identifier as part of automated screening tools for initial evaluations. This reduces the workload on medical staff and speeds up the identification of patients who may require further in-depth analysis for their corneal health.
The function can support research institutions focusing on corneal diseases by providing consistent and reliable metrics for clarity assessments. This data can help in the development of new therapies and improve the understanding of various corneal conditions.
By using the corneal clarity classification in educational tools, healthcare providers can visually explain to patients the status of their corneal health. This visual representation can enhance understanding and compliance with treatment procedures, leading to better health outcomes.
Surgeons can leverage the 'corneal clarity' identifier during pre-operative evaluations for procedures such as LASIK or corneal transplants. Reliable assessments of corneal clarity help in determining patient eligibility and planning tailored surgical approaches.
Insurance companies can implement this function to streamline claims related to corneal treatments. By having a consistent identification of corneal conditions, it can facilitate more accurate billing practices and reduce disputes over treatment necessity.
A zero-shot classifier uses a large foundation model's general knowledge to pick between your labels — no task-specific training, so new or edited labels work immediately. A Nyckel-trained classifier has been trained on labeled examples and runs on Nyckel's own infrastructure, which typically makes it faster, cheaper per call, and more accurate on data that resembles its training set. The "Under the hood" section on this page shows which kind this classifier is, and any classifier can be adapted into a trained one by adding your own examples.
Honestly: we can't know in advance — it depends on your data stream and how closely it resembles what this classifier has seen. The reliable way to find out is to measure it on your own data: start invoking the classifier with real traffic, or upload and annotate a set of images in the console — make sure they look like your production data, not idealized examples. Nyckel's evaluation metrics then show you exactly how it performs on that data before you rely on it.
No classifier is perfect, so Nyckel is built around the correction loop: invokes can be captured for review, you confirm or correct predictions in the console, and corrections become training data. Over time the model adapts to your data distribution — accuracy on your traffic improves with use rather than staying fixed.
No. This corneal clarity classifier works out of the box — clone it into your console and you'll have your own API endpoint in under a minute. Training data only enters the picture when you want to adapt it: your corrected predictions and uploaded samples improve the model, and you can also edit the label set to match your needs.
Trying the classifier on this page is free with no signup. Cloning it requires a free account, and the free tier covers your first API calls each month — see nyckel.com/pricing for current limits and paid tiers.
Add this pretrained classifier to your Nyckel console — you'll get a live API endpoint in under a minute, and a path to a custom model when you need one.