Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining skin pore size. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective skin area of an individual. A skin pore size model, trained with the pixel data, is operable to output, across a range of a skin pore size scale, skin pore size values associated with a degree of skin pore size. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the skin pore size model, to determine a user-specific skin pore size value of the user skin area. A user-specific electronic recommendation addressing at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.

An image analysis apparatus includes: an image input section; a region extraction section configured to specify a target element including an annular peripheral portion and a center portion that is surrounded by the peripheral portion and that is in a color different from the peripheral portion in a first image and a second image inputted from the image input section, the second image being acquired later than the first image, and configured to extract only the center portion of the target element as a region to be analyzed; and a color component extraction section configured to extract respective color component values of the extracted regions to be analyzed of the first and second images.

Provided is a highly reliable technique for the evaluation of ischemic conditions. A preferred embodiment is a nuclear medicine measurement protocol in which the administration of a radiopharmaceutical agent and radiation measurement are performed twice at rest and under stress. In the nuclear medicine measurement protocol, radiation collection is performed without radiopharmaceutical agent administration before the second radiopharmaceutical agent administration, and the result is used to correct the nuclear medicine measurement result after the second radiopharmaceutical agent administration.

During a focused-ultrasound or other non-invasive treatment procedure, the motion of the treatment target or other object(s) of interest can be tracked in real time based on (i) the comparison of treatment images against a reference library of images that have been acquired prior to treatment for the anticipated range of motion and have been processed to identify the location of the target or other object(s) therein and (ii) complementary information associated with the stage of the target motion during treatment.

A mark that identifies a lesion site is displayed on a tomographic image. A two-dimensional map and a graph are displayed regardless of whether or not the mark is displayed. The two-dimensional map represents lesion site probability distribution. The graph represents temporal change of lesion site probabilities.

A device is disclosed for diagnosing a dental condition. The device captures image data representative of a tooth of a patient based on data obtained from a hardware device that scans the tooth. The device inputs the image data into a first supervised machine learning model, and receives, as output from the first supervised machine learning model, a plurality of biomarkers, each biomarker corresponding to a different location of the tooth. The device inputs the plurality of biomarkers into a second supervised machine learning model, and receives, as output from the second supervised machine learning model, a diagnosis of a dental condition.

The disclosure provides a system for EGRT. The system may include a radiotherapy device for treating a subject. The radiotherapy device may include a scintillation camera that is directed at an ROI of the subject. The subject may be injected with a radioactive tracer or implanted with a radioactive marker before treatment. The ROI may undergo a physiological motion during the treatment. The system may deliver a treatment session to the subject by the radiotherapy device. During the treatment session, the system may acquire a target image of the ROI indicative of a distribution of the radioactive tracer or the radioactive maker in the ROI by the scintillation camera, and adapt a radiation beam to be delivered to the subject with respect to the physiological motion of the ROI by adjusting the radiation beam based on the target image.

Devices, systems, and methods herein relate to non-invasive monitoring of a patient. These systems and methods may receive one or more image signals corresponding to a skin of the patient, process the one or more image signals using a first machine learning model, and predict a physiological parameter based on the processed one or more image signals using a second machine learning model.

Data derivable from remotely detected electromagnetic radiation (16) emitted or reflected by a subject (12) is processed. The data includes physiological information. An input signal is received and indicative entities are transmitted. The indicative entities being indicative of physiological information representative of at least one vital parameter (17; 150) in a subject (12) of interest, wherein the indicative entities are detected under consideration of at least one defined descriptive model (114) describing a relation between physical skin appearance characteristics and a corresponding representation in the input signal such that non-indicative side information represented by non-indicative entities in the input signal is substantially undetectable in a resulting transmitted signal. The at least one vital parameter (17; 150) is detected from the transmitted signal including the indicative entities. The at least one vital parameter (17; 150) is extracted under consideration of detected skin color properties representing circulatory activity.

A method for estimating parameter values includes acquiring image data with an imaging apparatus, deriving a parameter model function from the image data, generating a N-dimensional grid, wherein N is a number of values of one or more non-linear terms of the derived model function, pre-calculating the one or more non-linear terms given the parameter model function and the designated values of the non-linear parameters, calculating one or more remaining model terms of the parameter model function, and displaying at least one of the one or more non-linear terms and remaining linear model terms.