Digital intraoral scanning systems are all based on optical scanning technology principles, and can be broadly categorized into two types based on the light source used: laser-based and visible light-based.
Laser-based intraoral scanning systems primarily utilize parallel confocal imaging and laser triangulation techniques. Parallel confocal imaging sends a parallel laser beam through the scanning head and projects it onto the object being scanned. After illuminating the target at a specific focal length, the beam is reflected and collected by a laser detector through a pinhole, ultimately constructing a three-dimensional image. A representative product is the iTero system. Laser triangulation technology utilizes a red laser beam and micromirrors oscillating at a high frequency to capture a series of still images from multiple angles, thereby generating a three-dimensional model. Representative products include the E4D system and the Planmeca system.
Visible light-based intraoral scanning systems employ techniques including structured light imaging and laser triangulation, static image acquisition, video capture, and high-speed optical slicing. The combination of structured light imaging and laser triangulation helps in continuously capturing images to accurately identify the three-dimensional surface morphology of teeth. A representative product is the CS3500. Static image acquisition technology employs a technique called active triangulation, which uses the intersection of three linear light beams to locate and acquire data in three-dimensional space. Representative products include the Cerec Omnicam/Bluecam system and the Langcheng DL-202. Active wavefront sampling technology in video capture can capture three-dimensional data from video sequences and perform real-time modeling; a representative product is the Lava COS system. High-speed optical slicing technology improves scanning speed during continuous image capture; a representative product is the Trios system.
New intraoral scanners integrating optical technologies such as near-infrared imaging (NIRI), quantitative optically induced fluorescence (QLF), and optical coherence tomography (OCT) have emerged. Simultaneously, artificial intelligence (AI) technology is increasingly being integrated into intraoral scanners, for example, in applications such as automated caries scoring and classification systems. Intraoral scanners (IOS) use a handheld camera to capture images in Standard Surface Subdivision Language (STL) format.
