GoKawiil
Tech News
Dyson launched its CameraJet toothbrush, a $499 device combining a camera-guided water flosser with brushing, at a Paris event in early September, then began selling it in US stores on September 8. Within a day, customers reported water leaking into the device's battery compartment, and the product has since become unavailable for purchase, with Dyson declining to explain why.
James Dyson introduced the Dyson CameraJet in Paris, a toothbrush equipped with a 100k pixel macro lens camera and machine learning that detects gaps between teeth in real time. The device uses this data to fire precision jets of mouthrinse into those gaps while also brushing with variable sonic oscillation designed to keep bristles moving against tooth surfaces.
Dyson has unveiled the CameraJet, a $499 toothbrush that uses a tiny built-in camera and an AI system called Gap Optical Targeting to spot gaps between teeth in real time. The device then fires a targeted burst of mouthrinse into those gaps to dislodge plaque, while an accompanying app shows coverage maps and brushing feedback without storing any images. The product is the result of a six-year collaboration between hundreds of Dyson engineers and dental professionals, including researchers at the National University of Singapore.
Dyson has unveiled the CameraJet, a $500 toothbrush that combines brushing, camera-guided gap detection, and water-jet flossing in one device. A tiny 0.1-megapixel lens under the brush head works with machine learning trained on 470,000 dental images to spot gaps between teeth and fire small bursts of fluid to floss them in real time. A companion MyDyson app lets users view their mouth live, get brushing feedback, and adjust intensity and jetting modes, though Dyson says no images are stored.
Dyson has introduced the CameraJet, an electric toothbrush that combines bristles with an integrated water flosser guided by a tiny embedded camera. The camera captures 28 images per second to detect gaps between teeth and fires a cone-shaped jet of rinse into them within 100 milliseconds, using a machine-learning model trained on roughly 470,000 dental images gathered over six years of development.