A pretrained jaw alignment classifier that sorts an image into one of 5 categories — what type of jaw alignment it has. Use the jaw 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 5 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 5 jaw 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.
The jaw alignment identifier can aid orthodontists by automatically assessing the alignment of a patient's jaw through analysis of X-ray images. This function can help in detecting misalignments or abnormalities, streamlining the diagnostic process and ensuring timely treatment decisions.
By utilizing the jaw alignment identifier, dental professionals can create customized treatment plans based on precise measurements of a patient's jaw alignment. The function can analyze individual variations, allowing for more effective and tailored orthodontic interventions.
After orthodontic treatment, the jaw alignment identifier can assess the success of the procedure by comparing pre- and post-treatment images. This feature enables practitioners to provide evidence-based reports to patients regarding their treatment outcomes.
The jaw alignment identifier can be integrated into telehealth platforms, allowing dental professionals to perform remote consultations. Patients can upload images for analysis, enabling professionals to evaluate jaw alignment without the need for in-person visits, making dental care more accessible.
Researchers can leverage the jaw alignment identifier in clinical studies to gather data on different jaw alignments and treatment outcomes. This data can foster advancements in orthodontic techniques and lead to better understanding of jaw-related conditions.
The identifier can generate visual reports for patients, illustrating their jaw alignment issues and proposed treatment plans. By allowing patients to visualize their conditions and potential results, it enhances understanding and engagement in their treatment process.
Dental schools can incorporate the jaw alignment identifier into their curriculum as a training tool for students. This interactive tool can help them practice diagnosing jaw alignment issues and understanding the nuances of orthodontic assessments in a simulated environment.
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 jaw 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.