A detection result output method, an electronic device, and a medium are provided. The detection result output method includes: obtaining first image information of a first object, where the first image information includes skin information of the first object; outputting a target detection result in a case that a matching degree between a first image and a target image meets a first preset condition; and outputting a first detection result in a case that the matching degree between the first image and the target image does not meet the first preset condition. The target detection result is a detection result corresponding to the target image, and the first detection result is a detection result corresponding to the first image.

Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, determine reflectance intensity values for at least a subset of the pixels, determine one or more quantitative features of the subset of the plurality of pixels based on the reflectance intensity values, and generate a predicted or assessed healing parameter associated with the wound over a predetermined time interval.

In some embodiments, the system is directed to medical assessment software for analyzing one or more medical conditions and enabling communication between a medical professional and a patient. In some embodiments, the system includes one or more graphical user interfaces configured to enable a medical professional to execute one or more of scheduling a virtual appointment, view a virtual schedule, check patients in/out, enter new patients into the system, request patient recorded outcomes, and view patient progress. In some embodiments, the system is configured to implement an artificial intelligence (AI) algorithm configured to identify one or more unique features within the one or more images and use the one or more unique features as one or more fiducials during an analysis of the one or more images. In some embodiments, the analysis includes a determination of whether an abnormal condition associated with an area of skin is progressing toward healing.

According to various embodiments, a wrinkle detection service providing server for providing a wrinkle detection method based on an artificial intelligence may include a data pre-processor for obtaining a skin image of a user from a skin measurement device and performing pre-processing based on feature points based on the skin image; a wrinkle detector for inputting the skin image pre-processed through the data pre-processing into an artificial neural network and generating a wrinkle probability map corresponding to the skin image; a data post-processor for post-processing the generated wrinkle probability map; and a wrinkle visualization service providing unit for superimposing the post-processed wrinkle probability map on the skin image and providing a wrinkle visualization image to a user terminal of the user.

A head-mounted wearable device includes a frame mountable on a head of a user; an infrared imaging device arranged to image a face of the user when the frame is mounted on the head of the user; and a computing system configured to perform operations including causing the infrared imaging device to capture an image of the face of the user using infrared light received at the infrared camera and initiating a biometric authentication process based on the image. The head-mounted wearable device may include a visible-light imaging device to image the face of the user with the computing system configured to perform operations including causing the visible-light imaging device to capture a second image of the face of the user using visible light received at the visible-light imaging device, with the biometric authentication process being based in part on the second image.

A method for identifying a skin abnormality including using an imaging device, the imaging device including a lighting member for directing light toward a target surface, the lighting member including a plurality of lighting elements, and a filter member positioned between the lighting member and the target surface, the filter member including a plurality of filter elements, capturing a plurality of images of the target surface, each of the plurality of images captured when illuminating a different one or more of the plurality of lighting elements, compiling the plurality of images into a data package, transmitting the data package to a server for processing the data package, and determining, at the server, a presence of an abnormality.

Local image enhancement processing is executed on an image obtained by imaging a transcription material. The enhanced image is divided into a plurality of patch images and input to a machine learning identifier. The patch images after segmentation output from the machine learning identifier are combined to generate a likelihood map image of skin ridges from the whole image based on a result of the segmentation. Binarization processing is executed on the likelihood map image to generate a binary image. A skin ridge region is extracted based on the binary image to calculate the area of the skin ridge region.

A wearable treatment and analysis module is provided. The module is positioned on or near a body surface region of interest. The module provides remote access to sensor data, treatment administration, and/or other health care regimens via a network connection with a user device and/or management system.

A biological information detection device includes: a video capture unit, a blood flow analysis unit, a local pulse wave detection unit, a pulse wave propagation velocity calculation unit, and a blood pressure estimation unit. The video capture unit obtains video information on a face of a living body. The blood flow analysis unit analyzes video data of at least three skin areas in the video information, as blood flow information. The local pulse wave detection unit is provided for each skin area to calculate pulse information based on the blood flow information sequenced chronologically. The pulse wave propagation velocity calculation unit calculates a pulse wave propagation velocity based on a phase difference between pieces of the pulse information at each skin area calculated by the local pulse wave detection unit. The blood pressure estimation unit estimates blood pressure based on the pulse wave propagation velocity.

A system that measures the motion of a camera traveling over a living subject by reference to images taken from the camera, while measuring the subject's shape, pose and motion. The system can segment each image into pixels containing the subject and pixels not containing the subject, and ascribe each pixel containing the subject to a precise location on the subject's body. In one embodiment, a computer connected to a camera and an Inertial Measurement Unit (IMU) provides estimates of the camera's location, attitude and velocity by integrating the motion of the camera with respect to features in the environment and on the surface of the subject, corrected for the motion of the subject. The system corrects accumulated errors in the integration of camera motion by recognizing the subject's body shape in the collected data.