A method for measuring regions of a tooth in a mouth including: measuring at least one surface point on a surface of the tooth with respect to an element mechanically coupled to said surface point; determining a location of at least one visible reference mechanically coupled to said surface point with respect to said element; estimating a location of said surface point with respect to said visible reference. A device used for such measuring may include a main body comprising a final optical element of an imager which defines an optical field of view directed in a first direction; and a measurement element coupled to said main body extending generally in said first direction; where a tip of said measurement element is sized and shaped to be inserted between a tooth and adjacent gingiva; where said optical field of view is sized to image at least part of a tooth.

The present invention provides for devices and methods of treating wounds, including general wounds, gum disease and gingival tissues post scaling/root planning, using a diode laser which generates a beam of light having a wavelength in the visible portion of the electromagnetic spectrum (400 nm-700 nm). Further disclosed are devices and methods capable of stimulating tissue regeneration at the site of a wound.

A method for measuring regions of a tooth in a mouth including: measuring at least one surface point on a surface of the tooth with respect to an element mechanically coupled to said surface point; determining a location of at least one visible reference mechanically coupled to said surface point with respect to said element; estimating a location of said surface point with respect to said visible reference. A device used for such measuring may include a main body comprising a final optical element of an imager which defines an optical field of view directed in a first direction; and a measurement element coupled to said main body extending generally in said first direction; where a tip of said measurement element is sized and shaped to be inserted between a tooth and adjacent gingiva; where said optical field of view is sized to image at least part of a tooth.

A tooth display measurement device includes a measurement portion, an inner surface and an outer surface. A tooth rest platform projects outwardly from the inner surface. The tooth rest platform is spaced between the upper end and the lower end, and ruler markings are printed or otherwise affixed on the outer surface. A a handle projects from the lower end of the measurement portion. The device assists in an accurate reading of tooth display as the measurement portion is inserted between the lip and the teeth of the user with the tooth rest platform abutting the biting edge of the tooth to be measured.

A periodontal endoscope includes an imaging handle having an imaging end that supports a camera sensor and an introducer including an introducer blade such that the introducer blade is selectively rotatable about the camera sensor for engaging gingival tissue in the field of view for any rotational orientation of the introducer blade relative to the camera while illuminated by an optical fiber bundle. An automatic illuminance controller controls an illumination light source at least within each frame as the frame is scanned based on dynamic range of the camera sensor such that high intensity areas of the frame receive a reduced amount of illumination light by reducing the illumination drive signal as compared to low intensity areas of the frame which receive an increased amount of illumination light by increasing the illumination drive signal relative to the high intensity areas.

An imaging system is described. The imaging system accesses first imaging data of a specimen using a first sensor device. The first imaging data comprising volumetric data. The imaging system accesses second imaging data of the specimen using a second sensor device. The second imaging data comprising surface data. The imaging system registers a common anatomical region of the specimen in the first imaging data and the second imaging data and generates a composite image based on the registered common anatomical region. The composite image indicates the volumetric data and the surface data of the specimen.

There is a need for a minimally invasive surgical treatment method for periodontitis for the removal of deep pockets, elimination of disease, creation of reattachment of the gingiva to the tooth surface and true regeneration of the attachment apparatus (new cementum, new periodontal ligament, and new alveolar bone) on a previously diseased root surface. The PerioLase® MVP-7™ including eGUI or another device capable of laser dosimetry, such as an original MVP-7™ type laser without the eGUI, achieves this with the LANAP protocol (laser-assisted new attachment procedure) and the LENAP protocol (laser excisional new attachment procedure).

Presented is a wireless portable ultrasound acquisition system for dental imaging, having an ultrasound probe with a control switch connected through a cable to a portable ultrasound acquisition system that communicates wirelessly with a smart tablet or a phone display to display the ultrasound images. The system uses ultrasound signals to create images of alveolar bone structure and boundaries of enamel, dentin and gingiva of a patient.

Dental images are processed according to a first machine learning model to determine teeth labels. The teeth labels and image are concatenated and processed using a second machine learning model to label anatomy including CEJ, JE, GM, and Bone. The anatomy labels, teeth labels, and image are concatenated and processed using a third machine learning model to obtain feature measurements, such as pocket depth and clinical attachment level. The feature measurements, anatomy labels, teeth labels, and image may be concatenated and input to a fourth machine learning model to obtain a diagnosis for a periodontal condition. Feature measurements and/or the diagnosis may be processed according to a diagnosis hierarchy to determine whether a treatment is appropriate. Machine learning models may further be used to reorient, decontaminate, and restore the image prior to processing. Machine learning models may be embodied as CNN, GAN, and cyclic GAN.