A pretrained tooth alignment classifier that sorts an image into one of 10 categories — what the alignment of your teeth is. Use the tooth alignment 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 14 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": "Aligned",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 tooth alignment 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.
Dental professionals can use the tooth alignment identifier to assess the precision of treatment plans for patients requiring braces or aligners. By identifying misalignments or false images of tooth positioning, orthodontists can personalize their approaches and achieve better results.
This function can help in the continuous monitoring of patients undergoing orthodontic treatment. With regular assessments of tooth positioning, practitioners can track progress and make timely adjustments to treatment plans.
By analyzing alignment data over time, this tool can enhance predictive analytics for treatment outcomes. Orthodontists can provide patients with more accurate estimates regarding the duration and effectiveness of their treatment based on previous case data.
The tooth alignment identifier can facilitate remote consultations for orthodontic practices. Patients can submit images of their teeth, and the AI can assess alignment needs, helping practitioners provide more immediate feedback without requiring an in-office visit.
Dental practices can use the tool to create educational content that visualizes the importance of proper tooth alignment. Engaging patients with clear, accurate images and aligning disease prevention messages can boost patient interest and retention.
Insurers can incorporate the tooth alignment identifier to enhance claim validation processes. By accurately assessing the need for specific treatments, insurance companies can reduce fraud and ensure that claims are only issued for legitimate alignment issues.
Researchers can utilize the identifier's data to inform studies on orthodontic treatments and technologies. By understanding common misalignments and treatment effectiveness, new techniques and products can be innovated for better patient outcomes.
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 tooth alignment 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.