A pretrained gum recession classifier that sorts an image into one of 4 categories — the severity of gum recession in dental images.. Use the gum recession 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 4 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": "Mild",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 4 gum recession 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.
This function can be integrated into dental health apps to allow users to upload photos of their gums. The application would analyze images to identify early signs of gum recession, prompting users to seek professional dental advice.
Dental clinics can utilize the image classification function to monitor patients' gum health over time. By regularly analyzing submitted images, practitioners can generate reports that track the progression of gum recession, facilitating timely interventions.
This feature can support remote consultations by enabling dental professionals to assess patients' gum conditions through submitted images. It allows for more accurate diagnostics and personalized advice without requiring in-person visits.
Dentists can use this technology in educational programs to help patients understand gum recession. By showing patients their gum health visually, they can better grasp the importance of oral hygiene and preventative care.
Insurance companies can employ this classification function to validate claims related to gum recession treatments. By analyzing submitted images as part of the claims process, they can ensure that treatment aligns with documented conditions.
Dental research institutions can implement this technology to gather data on gum recession prevalence and progression. The insights gained from analyzing large datasets can contribute to better understanding and prevention strategies in oral health.
Community health initiatives can use this function in mobile screening campaigns. By allowing participants to submit images of their gums, health workers can quickly identify those in need of further evaluation and care, improving outreach and intervention efforts.
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 gum recession 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.