An electromagnetic (EM) probe for monitoring one or more biological tissues. The EM probe comprises a cup shaped cavity having an opening and an interior volume, a circumferential flange formed substantially around the cup shaped cavity, in proximity to the opening, at least one layer of a material, for absorbing electromagnetic radiation, applied over at least one of a portion of the circumferential flange and a portion of the outer surface of the cup shaped cavity, and at least one EM radiation element which performs at least one of emitting and capturing EM radiation via the interior volume.

Methods, systems, and non-transitory computer readable medium for foot differentiation scoring pertaining to an individual's differences in their two feet. A method includes receiving, from sensors of a scanning device, pressure and/or other measurement data corresponding to feet of a user. The method further includes preprocessing the pressure and/or measurement data. The method further includes generating, based on the preprocessed pressure and/or measurement data, a foot differentiation score based on differences between a left foot and a right foot of the user. The foot differentiation score may assign or correspond to a numerical rating based on how different the user's left foot and right foot are from each other.

Systems and methods for generating a three-dimensional (3D) model of a user's dental arch based on two-dimensional (2D) images of dental impressions include a model training system that provides a machine learning model using training image(s) of a dental impression of a respective dental arch and a 3D training model of the respective dental arch. A model generation system receives first image(s) of a first dental impression of a user's dental arch and second image(s), which may be of the first dental impression or a second dental impression of the dental arch. The model generation system generates a first and second 3D model of the dental arch by applying the first image(s) and second image(s) to the machine learning model.

A computer system obtains at least one 3D scan of a body surface; automatically identifies, based on the at least one 3D scan, one or more features (e.g., nose, lips, eyes, eyebrows, cheekbones, or specific portions thereof, or other features) of the body surface; and generates a digital 3D model of the body surface. The digital 3D model includes the identified features of the human body surface. In an embodiment, the step of generating of the digital 3D model is based on the at least one 3D scan and the identified features of the body surface. In an embodiment, the digital 3D model comprises a 3D mesh file. The digital 3D model can be used in various ways. For example, output of a manufacturing process (e.g., a 3D printed item, a cosmetics product, a personal care product) can be based on the digital 3D model.

A performance scanning system that operates to detect and measure surface parameters of a portion of an athlete and uses the surface parameters to determine the likelihood that the athlete's physical performance has been or will be impaired.

This invention is a smart bra for optical scanning of breast tissue which has light emitters which transmit light into breast tissue, light receivers which receive the light after it has been transmitted through the breast tissue, and expandable components which selectively move light emitters and/or receivers closer to the surface of a breast where there are air gaps.

A 3D body scanner for creating 3D body models of an outer body shape of a person includes a scanner unit, which includes at least one depth sensor configured for spatially detecting a visual field. The scanner unit is powered by electrical energy, and a platform is powered by electrical energy and configured for accommodating the person. The 3D body scanner includes an energy transmission arrangement that is configured to contactlessly transmit electrical energy between the scanner unit and the platform.

According to some embodiments there is provided a method for structured light scanning of an intra-oral scene, comprising: projecting onto the intra-oral scene a color-coded pattern comprising an arrangement of entities having edges between them; each entity comprising a different narrow band of wavelengths; and detecting the projected pattern as a plurality of pixels in an acquired image of the scene using at least two narrowband filters, wherein for each pixel of at least 95% of the pixels of an entity of interest comprising a first band of wavelengths, a contribution of light from a second band of wavelengths of an adjacent entity is less than 10%. Some embodiments relate to a scanner system for structured light scanning of an intra-oral scene.

The present specification discloses a method capable of automatically recognizing an accurate wound boundary and a method of generating a 3D wound model based on the recognized wound boundary. The method of automatically recognizing a wound boundary according to the present specification is a method of automatically recognizing a wound boundary based on artificial intelligence, and may photograph several frames of the wound to be recognized with an RGB-D camera, separating measurement information in an image, amplifying image data for learning, and passing the amplified image data through an artificial neural network. The method may include generating a three-dimensional (3D) model by performing boundary recognition post-processing on the data passing through the artificial neural network to match a two-dimensional (2D) image with the 3D model.

Various embodiments include methods and diagnostic systems implementing the methods for determining a prognostic prediction of a likelihood of success or a likelihood of complications of an electrophysiology procedure at the identified area of electrophysiological interest. Various embodiments may include generating a patient-specific three-dimensional (3D) cardiac activation and arrythmia localization model identifying an area of electrophysiological interest for performing an electrophysiology procedure to treat the arrythmia, using the 3D heart model to identify heart structures near the identified area of electrophysiological interest, determining a prognostic indication of an electrophysiology procedure performed at the identified area of electrophysiological interest based at least in part on one or more heart structures near the area of electrophysiological interest, and generating an output providing a prognostic indication of an electrophysiology procedure at the identified area of electrophysiological interest based at least in part on the determined likelihood of success.