CranioCatch
AILab Platform

CranioCatch AILab is a web-based infrastructure developed to let you perform precise segmentation (labeling) on dental radiographs. It allows you to easily prepare your own dental datasets for academic research, theses, TÜBİTAK, or BAP projects, and to train your own AI models with this data.

Our platform has a fully user-friendly and intuitive interface. After uploading your radiographs to the system and defining your labels, you can perform segmentation operations without writing a single line of code. Moreover, our expert physician and software team provides you with uninterrupted consulting support at every technical step, from your project setup to the model output.

Yes, it will be entirely yours. You can directly obtain, as an output, the AI model you trained with your own data through our platform, along with all the academic analysis metrics you have obtained. You have the right to use these models and results in your own scientific publications, articles, and theses.

Data security and confidentiality are our top priority. All dental radiographs you upload to our platform and the segmentation work you perform are encrypted and protected on our highly secure servers. Your data is never shared with any third party or institution without your permission.

The amount of data needed varies depending on the scope of your subject, the imaging type you will work with, and the diversity of the findings you focus on. In the preliminary consulting meeting we hold with our expert academic and technical team at the start of your project, we plan together the minimum number of cases required to reach your target accuracy and the most efficient workflow for you.

We fully report all the scientific performance metrics and analysis charts required for your study to be accepted in prestigious international journals.
Depending on the type of your project (labeling, classification, keypoint annotation, or segmentation), you can readily obtain from the system all the statistical data and visualized AI results requested by editors and reviewers, such as accuracy rates, precision analyses (Precision/Recall, F1-Score), performance curves (ROC/AUC, Dice/IoU), and error margin calculations (MAE).