A pretrained corneal thickness classifier that sorts an image into one of 10 categories — the corneal thickness category it belongs to. Use the corneal thickness 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": "Above Average",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 corneal thickness 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 thickness identifier can be integrated into ophthalmology clinics to aid in diagnosing various eye conditions such as keratoconus and glaucoma. By providing accurate measurements, it helps doctors make informed decisions regarding treatment options and patient management.
Surgeons can utilize the corneal thickness identifier to evaluate patients before executing refractive surgeries such as LASIK. Accurate measurements of corneal thickness are crucial for determining candidacy as well as minimizing postoperative complications, ensuring better surgical outcomes.
This function can be implemented in telemedicine platforms, allowing remote consultations to assess patients' corneal health. Patients can report their corneal thickness data, enabling eye care professionals to monitor conditions without requiring in-person visits.
The identifier can serve as a valuable tool in clinical trials and research studies focused on corneal conditions. Researchers can analyze data regarding corneal thickness across different populations to identify trends, risk factors, and the effectiveness of treatments.
Eye care providers can use the corneal thickness identifier results to educate patients about their corneal health. By visually showing patients their corneal measurements and explaining their significance, it can enhance patient understanding and adherence to treatment plans.
Insurance companies can leverage the corneal thickness identifier to better assess risks associated with eye health for underwriting policies. By understanding a patient's corneal thickness, insurers can set premiums more accurately based on potential future medical expenses.
The corneal thickness identifier can assist optometrists in fitting custom contact lenses. Accurate corneal thickness measurements can lead to a better fit, improving comfort and efficacy, and ultimately enhancing the patient’s wearing experience.
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 thickness 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.