In a general aspect, enhancement of artificial intelligence algorithms using 3D data is described. In some aspects, input data of an object is stored in a storage engine of a system. The input data includes first-order primitives and second-order primitives. A plurality of features of the object is determined by operation of an analytics engine of the system, based on the first-order primitives and the second-order primitives. A tensor field is generated by operation of the analytics engine of the system. The tensor field includes an attribute set, which includes one or more attributes selected from the first-order primitives, the second-order primitives, or the plurality of features. The tensor field is processed by operation of the analytics engine of the system according to a series of artificial intelligence algorithms to generate output data representing the object.

A first electronic device according to various embodiments may include: a display; a communication module comprising communication circuitry; a camera module including at least one camera; and a processor. The processor may be configured to: identify a request for measuring a skin condition of a user in a state in which the first electronic device is cradled on a second electronic device; acquire, based on information of the camera module and information regarding at least one light-emitting element included in the second electronic device, control information for controlling output of the at least one light-emitting element; control output of light from the at least one light-emitting element of the second electronic device based on the control information; acquire at least one image including at least a part of a body of the user through the camera module while light is output through the at least one light-emitting element controlled based on the control information; and provide information regarding the skin condition of the user using the at least one image.

A margin assessment method is provided. Under cooperation of harmonic generation microscopy (HGM) and a deep learning method, the margin assessment method can instantaneously and digitally determine whether a 3D image group generated by an HGM imaging system is a malignant tumor or the surrounding normal skin, so as to assist in determining margins of a lesion.

There is shown and described a deep learning based system and method for skin diagnostics as well as testing metrics that show that such a deep learning based system outperforms human experts on the task of apparent skin diagnostics. Also shown and described is a system and method of monitoring a skin treatment regime using a deep learning based system and method for skin diagnostics.

A kit for a fiducial marker-based registration of preinterventional image data to an intra-interventional scene is disclosed. The kit comprises: at least one first component, wherein the first component is configured for attachment to a body of a patient, wherein the first component comprises at least one base part of an arresting mechanism, at least one second component, wherein the second component comprises at least one fiducial which is localizable in the preinterventional image data, wherein the second component comprises at least one first counterpart of the arresting mechanism, at least one third component, wherein the third component comprises at least one mounting unit for mounting a spatially localizable tracking object on the third component, wherein the third component comprises at least one second counterpart of the arresting mechanism;
wherein the second component and the third component are selectively attachable to the first component by establishing a releasable mechanical connection between the base part and the first counterpart or between the base part and the second counterpart.

Disclosed is a care device recommendation server providing a service for recommending a skin care device for a user, the care device recommendation server including: a data management unit configured to obtain face images of the user from a shin measurement device including a camera and a display; a skin condition determination unit configured to determine a first skin condition of the user corresponding to a first time point at which the face images are obtained, based on the obtained the face images; a care device recommendation unit configured to determine the skin care device for the user among a plurality of care devices included in a care device DB based on the determined first skin condition; a care device control value-providing unit configured to provide a first control value of the skin care device corresponding to the first skin condition to each of a user terminal of the user and the skin measurement device; a care device control unit configured to remotely control the care device such that the skin care device is driven by the control value; and a history provision unit configured to provide information about a previous skin condition of the user, which corresponds to each of the face images of the user obtained from the data management unit, to the user terminal.

The present invention relates to a deep learning-based multiple skin lesion detection system, a multiple lesion detection method, and a computer-readable recording medium that has a program for implementing same recorded thereon. The system according to the present invention enables accurate classification and detection of various skin lesions having similar characteristics, on the basis of a context-dependent decision-making structure in which the local spatial correlation between various skin lesions in skin is considered.

Kits, diagnostic systems and methods are provided, which measure the distribution of colors of skin features by comparison to calibrated colors which are co-imaged with the skin feature. The colors on the calibration template (calibrator) are selected to represent the expected range of feature colors under various illumination and capturing conditions. The calibrator may also comprise features with different forms and size for calibrating geometric parameters of the skin features in the captured images. Measurements may be enhanced by monitoring over time changes in the distribution of colors, by measuring two and three dimensional geometrical parameters of the skin feature and by associating the data with medical diagnostic parameters. Thus, simple means for skin diagnosis and monitoring are provided which simplify and improve current dermatologic diagnostic procedures.

The present disclosure provides a method of medical procedure using augmented reality for superimposing a patient's medical images (e.g., CT or MRI) over a real-time camera view of the patient. Prior to the medical procedure, the patient's medical images are processed to generate a 3D model that represents a skin contour of the patient's body. The 3D model is further processed to generate a skin marker that comprises only selected portions of the 3D model. At the time of the medical procedure, 3D images of the patient's body are captured using a camera, which are then registered with the skin marker. Then, the patient's medical images can be superimposed over the real-time camera view that is presented to the person performing the medical procedure.

In various embodiments, a method for determining a homogeneity of complexion is provided. The method may include provision of a digital image on which skin is portrayed and which is parameterized in a color space which is defined by a parameter set in which one of the parameters is a hue, identifying and/or defining at least one skin examination area in the transformed image, calculating a hue distribution in the at least one skin examination area, and determining at least one homogeneity value for the complexion based on the calculated hue distribution.