Human lying posture detections are disclosed. A human lying on a bed is detected by obtaining an image through a depth camera, detecting objects in the image and marking the objects in the image using 2D bounding boxes by deep learning, determining the human being in a lying posture in response to a width and a height of the 2D bounding box of the human meeting a predetermined condition, detecting one or more skin areas in the image and generating skin area 2D bounding boxes to mark each of the one or more skin areas using a skin detection algorithm, and determining the human being in the lying posture in response to the skin area 2D bounding boxes and the 2D bounding box of the bed meeting a predetermined positional relationship.

A method of distinguishing benign and malignant skin conditions includes extracting a numerical value corresponding to an order parameter from an image of skin having a pigmented region. The numerical value of the order parameter may be utilized to assess the likelihood that a skin lesion is benign or malignant. The precise value may also be utilized to assess severity, which may include detecting changes in a skin lesion over time.

Methods and systems for digitally reconstructing a patient tissue sample are described herein. In one embodiment, the method may include projecting a first structured light pattern onto the patient tissue sample, receiving a first reflection of the first structured light pattern from the patient tissue sample, and reconstructing the patient tissue sample based on the first reflection and the projected first structured light pattern. In another embodiment, the system may include a projector adapted or configured to project the first structured light onto the patient tissue sample, a charge-coupled device (CCD) adapted or configured to receive the first reflection from the patient tissue sample, and a reconstruction device adapted or configured to reconstruct the patient tissue sample based on the first reflection and the projected first structured light pattern.

Disclosed are service providing apparatus and method for skin care based on image analysis by identifying a using cosmetic used by a user by analyzing an image obtained by photographing a cosmetic container with a camera based on deep learning, predicting the user's skin type by analyzing the using cosmetic based on deep learning, and then recommending a cosmetic suitable for the user's skin type. According to the present disclosure, it is possible to enhance user's convenience and satisfaction with skin care and reduce the time and cost required for selecting cosmetics suitable for the user's skin type by analyzing the cosmetics held by the user through artificial intelligence based on deep learning to estimate the user's skin type and then automatically selecting and providing cosmetics suitable for the user's skin type from among the cosmetics held by the user according to the estimated skin type.

A convolutional neural network (CNN) and associated method for identifying basal cell carcinoma are disclosed. The CNN comprises two convolution layers, two pooling layers and at least one fully-connected layer. The first convolution layer uses initial Gabor filters that model the kernel parameters setting in advance based on human professional knowledge. The method uses collagen fiber images for training images and converts doctors' knowledge to initiate the Gabor filters as featuring computerization. The invention provides better training performance in terms of training time consumption and training material overhead.

Disclosed is a content-based image retrieval (CBIR) system and related methods that serve as a diagnostic aid for diagnosing whether a dermoscopic image correlates to a skin cancer type. Systems and methods according to aspects of the invention use as a reference a set of images of pathologically confirmed benign or malignant past cases from a collection of different classes that are of high similarity to the unknown new case in question, along with their diagnostic profiles. Systems and methods according to aspects of the invention predict what class of skin cancer is associated with a particular patient skin lesion, and may be employed as a diagnostic aid for general practitioners and dermatologists.

An image processing method is provided that automatically calculates Body Surface Area (BSA) score using machine learning techniques. A Felzenszwalb image segmentation algorithm is used to define proposed regions in each of a plurality of training set images. The training set images are oversegmented, and then each of the proposed regions in each of the plurality of oversegmented training set images are manually classified as being a lesion or a non-lesion. A Convolutional Neural Network (CNN) is then trained using the manually classified proposed regions in each of the plurality of training set images. The trained CNN is then used on test images to calculate BSA scores.

The present disclosure provides monitoring system, device and computed-implemented method for monitoring pressure ulcers. The monitoring system and the device are configured to: capture at least one image corresponding to a user; retrieve the anamnesis data of the user; and determine a pressure ulcers condition result corresponding to the at least one image and the anamnesis data according a pressure ulcers prediction model.

A method for determining healing progress of a tissue disease state includes receiving a thermal image of a target wound area from a thermal imaging system, processing the thermal image to construct an isotherm map of at least one selected area, determining a thermal index value from the isotherm map, correlating the wound thermal index value with a reference thermal index value representative of an injury-free state.

The present disclosure provides methods and apparatus for evaluating tissue structure in damaged or healing tissue. The present disclosure also provides methods of identifying a patient at the onset of risk of pressure ulcer or at risk of the onset of pressure ulcer, and treating the patient with anatomy-specific clinical interventions selected, based on thermal imaging (TI). The present disclosure also provides methods of stratifying groups of patients based on risk of wound development and methods of reducing incidence of tissue damage in a care facility. The present disclosure also provides methods to analyze trends of TI intensities to detect tissue damage before it is visible, and methods to compare bisymmetric TI intensities to identify damaged tissue.