Although 3D printing is relatively new to the dental industry, 3D-printed dental casts exhort countless benefits and opportunities when creating dental products. However, after perusing recent articles on 3D printing applications in the dental industry, I noticed that there are not many promising tools or methods to inspect the 3D-printed cast accuracy and efficiency for dental technicians. Therefore, I decided to build a user-friendly and accessible device employing an object detection model to classify 3D-printed casts in the hope of assisting dental technicians in detecting cast accuracy and malfunctions.

To extrapolate and interpret dental cast categories (classes) accurately, I needed to collect data from actual 3D-printed dental casts in order to train my object detection model with notable validity. Therefore, I purchased numerous dental cast STL files identified with different tags (labels) and printed them with my SLA (HALOT-ONE) and FDM (CR-200B) 3D printers:

• Big Central Teeth
• Antagonist Teeth
• Orthodontic Superior
• Prognathous
• Strange Teeth Inferior
• Strange Teeth Superior
• Failed
• Implant

Since Sony Spresense is a high-performance development board intended for edge computing in sensor analysis, machine learning, image processing, and data filtering, I decided to utilize Sony Spresense in this project. To capture images and store them on an SD card to train my object detection model, I connected the Spresense extension board and the Spresense camera board to the Spresense main board (CXD5602). Then, I utilized a TFT LCD touch screen (ILI9341) to display the video stream and captured images. Also, I added a tiny thermal printer to the device so as to print the detection result after running my object detection model on Sony Spresense.

After completing my data set by taking pictures of 3D-printed dental casts, I built my object detection model with Edge Impulse to make predictions on dental cast accuracy categories (classes). I utilized Edge Impulse FOMO (Faster Objects, More Objects) algorithm to train my model, which is a novel machine learning algorithm that brings object detection to highly constrained devices. Since Edge Impulse is nearly compatible with all microcontrollers and development boards, I had not encountered any issues while uploading and running my model on Sony Spresense. As labels, I appended three main categories (classes) to the file names while capturing and storing pictures:

• Cast
• Failed
• Implant

After training and testing my object detection (FOMO) model, I deployed and uploaded the model on Sony Spresense. Therefore, the device is capable of detecting dental cast accuracy categories (classes) by running the model independently without any additional procedures.

Lastly, to make the device as robust, sturdy, and compact as possible while experimenting with 3D-printed dental casts, I designed a dental-themed case with a sliding side cover (3D printable).

So, this is my project in a nutshell 😃

In the following steps, you can find more detailed information on coding, capturing dental cast pictures, storing pictures on an SD card, building an object detection (FOMO) model with Edge Impulse, and running it on Sony Spresense.

🎁🎨 Huge thanks to DFRobot for sponsoring a Tiny (Embedded) Thermal Printer.

🎁🎨 If you want to purchase products from DFRobot, you can use my $5 discount coupon.

🎁🎨 Also, huge thanks to Creality for sending me a Creality CR-200B 3D Printer.

Step 1: Designing and printing a dental-themed case

Since I focused on building a user-friendly and accessible device for dental technicians in this project, I decided to design a robust and compact case allowing the user to capture dental cast images flawlessly. To avoid overexposure to dust and prevent inadvertent loose wire connections, I added a sliding side cover to the case. Also, I decided to fasten a tooth replica to the...