A pretrained plaque buildup classifier that sorts an image into one of 8 categories — the severity of plaque buildup in dental images. Use the plaque buildup 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 8 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": "Excessive",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 8 plaque buildup 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' diagnostic tools to identify plaque buildup on patients' teeth. By accurately classifying plaque presence, dentists can tailor preventive care and treatment plans more effectively, enhancing patient oral health.
The plaque buildup identifier can be utilized in telehealth platforms to analyze patient-submitted images of their teeth. This can help remote dentists provide recommendations based on the level of plaque observed, improving patient engagement and access to care.
Mobile applications designed for oral hygiene education can incorporate the function to encourage users to take better care of their teeth. By showing users if plaque is present in their images, the app can provide personalized tips and reminders to improve their routine.
This technology can be used by cleaning service providers to evaluate the needs of potential clients. By assessing plaque levels in uploaded images, they can strategize tailored cleaning sessions, helping to improve service outcomes and customer satisfaction.
Researchers can use this classification function to analyze large datasets of dental images to better understand the factors affecting plaque buildup. This data can lead to new insights into oral health trends, contributing to improved prevention strategies in dental care.
Dental schools can integrate this function into training programs to help students learn how to diagnose plaque buildup based on visual cues. This will enhance their practical training and make them more adept at identifying oral health issues in clinical settings.
Companies developing oral care products can use the plaque buildup identifier in product testing phases. By analyzing the effectiveness of toothpaste formulas against plaque, they can optimize product development and marketing claims based on real performance results.
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 plaque buildup 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.