A pretrained gum health classifier that sorts an image into one of 10 categories — the health status of your gums. Use the gum health 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 20 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": "Abscessed Gums",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 gum health 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 clinics to assess patients' gum health during routine check-ups. By analyzing images taken of the gums, dentists can identify early signs of gum disease, allowing for timely intervention.
Dental practitioners can use this function in telehealth platforms to evaluate patients' gum health remotely. Patients can submit images of their gums for analysis, enabling dentists to provide advice or treatment recommendations without an in-person visit.
Developers can incorporate the gum health identifier into mobile health apps designed for oral care. Users can take photos of their gums, and the app can provide feedback or reminders for best practices in maintaining gum health.
Dental schools and training programs can use this function as a teaching tool for students. By analyzing different gum conditions through images, students can learn to recognize various gum health issues and improve their diagnostic skills.
Insurance companies can utilize this function to analyze images submitted with claims related to gum treatments. Automating the classification of gum health from images can streamline claim processing and reduce the potential for fraudulent claims.
Companies developing new oral care products can use this function to test the efficacy of their products on gum health. By comparing image analyses before and after product use, they can provide empirical evidence of their product’s benefits.
Health organizations can leverage this function for community health initiatives focused on oral hygiene. By analyzing images collected from public screenings, they can assess the overall gum health of populations and tailor educational content for improving public awareness.
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 health 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.