Provided are a skin evaluation apparatus, a skin evaluation method, and a non-transitory computer readable recording medium storing a skin evaluation program capable of evaluating gloss of skin in consideration of both of a physical feature of the skin and an optical feature of the skin to quantitatively evaluate gloss of the skin closer to a practical point of view. The skin evaluation apparatus includes a shape information acquisition unit 22 that acquires information on a surface unevenness shape of the skin; an optical feature information acquisition unit 23 that acquires information on an optical feature of the skin; and an evaluation unit 24 that evaluates gloss of the skin on the basis of the information on the surface unevenness shape and the information on the optical feature.

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.

Accurate measurement of pupillary distance, PD, is necessary to make prescription eye glasses as well as configuring VR headsets, and using other binocular optical devices. Today, many people are ordering eyeglasses on line and obtaining their PD is often problematic for a number of reasons as the prior art fails to provide consumer friendly PD measurement systems. A disclosed eyeglass frame system comprises reference marks of known locations upon the frames. A smart phone may be used to locate the consumer's pupils, while the consumer is wearing the frames. The consumer's pupils may be marked or tagged upon a digital image of the consumer wearing the frames. By use of angles in the sight lines of the camera lens and other variable values and the known relative distances of the frame markings, a consumer's pupillary distance can be quickly and accurately derived.

Embodiments provide a deep learning framework to accurately segment liver and spleen using a convolutional neural network with both short and long residual connections to extract their radiomic and deep features from multiparametric MRI. Embodiments will provide an “ensemble” deep learning model to quantify biopsy derived liver fibrosis stage and percentage using the integration of multiparametric MRI radiomic and deep features, MRE data, as well as routinely available clinical data. Embodiments will provide a deep learning model to quantify MRE-derived liver stiffness using multiparametric MRI, radiomic and deep features and routinely-available clinical data.

Described herein are computer-implemented methods for analyzing an input image of a mouth region from a user to provide information regarding a disease or condition of the mouth region, a computing device configured to receive the input images from a user; and a trained machine learning system. In some embodiments, the computing device is configured to transmit an oral health score to the user.

There is a need for a technique to determine a presence or absence of morbidity of a lifestyle-related disease and a possibility of future morbidity (risk of morbidity) in a non-invasive manner for a subject. The present disclosure provides a blood abnormality prediction device including, a prediction unit configured to predict a presence or absence of a blood abnormality in a subject on the basis of the information of the image that captures a crown portion of a capillary, wherein the prediction unit is configured to measure one or more selected from the group consisting of an entire width, an apex width, a loop diameter, a venous limb width, and an arterial limb width of the crown portion of the capillary on the basis of the information of the image, to predict the presence or absence of the blood abnormality in the subject from a result of the measurement.

An abnormal physical condition determination system includes: an extraction unit that extracts a plurality of feature quantities indicating a condition of a target person from an image of the target person; an accumulation unit that accumulates the plurality of feature quantities as time series data; a calculation unit that calculates a relationship between each feature quantity from the plurality of feature quantities accumulated in the accumulation unit; and a determination unit that determines an abnormal physical condition of the target person on the basis of the relationship. According to such an abnormal physical condition determination system, it is possible to appropriately determine the abnormal physical condition of the target person.

Vessel perfusion and myocardial blush are determined by analyzing fluorescence signals obtained in a static region-of-interest (ROI) in a collection of fluorescence images of myocardial tissue. The blush value is determined from the total intensity of the intensity values of image elements located within the smallest contiguous range of image intensity values containing a predefined fraction of a total measured image intensity of all image elements within the ROI. Vessel (arterial) peak intensity is determined from image elements located within the ROI that have the smallest contiguous range of highest measured image intensity values and contain a predefined fraction of a total measured image intensity of all image elements within the ROI. Cardiac function can be established by comparing the time differential between the time of peak intensity in a blood vessel and that in a region of neighboring myocardial tissue both pre and post procedure.

A method of identifying and locating tissue abnormalities in a biological tissue includes irradiating an electromagnetic signal, via a probe defining a transmitting probe, in the vicinity of a biological tissue. The irradiated electromagnetic signal is received at a probe, defining a receiving probe, after the signal is scattered/reflected by the biological tissue. Blood flow information pertaining to the biological tissue is provided. Based on the received irradiated electromagnetic signal and the blood flow information, tissue properties of the biological tissue are reconstructed. A tracking unit determines the position of at least one of the transmitting probe and the receiving probe while the step of receiving is being carried out, the at least one probe defining a tracked probe. The reconstructed tissue properties are correlated with the determined probe position so that tissue abnormalities can be identified and spatially located.

A method for monitoring the positioning of the teeth including production of a three-dimensional digital initial reference model of the arches of the patient and, for each tooth, definition, from the initial reference model, of a three-dimensional digital reference tooth model; acquisition of updated image of at least one two-dimensional image of the arches in actual acquisition conditions; analysis of each updated image and production, for each updated image, of an updated map; optionally, determination, for each updated image, of rough virtual acquisition conditions approximating the actual acquisition conditions; searching, for each updated image, for a final reference model corresponding to the positioning of the teeth during the acquisition of the updated image, for each tooth model, comparison of the positionings of the tooth model in the initial reference model and in the reference model obtained at the end of the preceding steps to determine the movement of the teeth.