41 results about "Wrist bone" patented technology

The invention relates to a bathing device, in particular to a wrist bone fracture patient bathing device used in the orthopedics department and aims to provide a wrist bone fracture patient bathing device which can protect the wrist of a wrist bone fracture patient, prevent water from being splashed onto a wound and provide convenience for bathing and is used in the orthopedics department. According to the technical scheme, the wrist bone fracture patient bathing device used in the orthopedics department comprises a base plate, a 7-shaped plate, a water pipe, a spray head, a water outlet pipe,a frame body, a first sliding rail and the like. The 7-shaped plate is installed on the left side of the top of the base plate, the frame body is installed at the upper portion of the left side facein the 7-shaped plate, the middle of the right side of the frame body is provided with an opening, and the right side of the top of the frame body is provided with a wire guiding hole. The fractured hand stretches into a sleeve so that the hand can be protected, water can be prevented from being splashed onto the wound, and the arm of the bone fracture patient can be cleaned through a cleaning device, namely cleaning of the bone fracture patient's hand in the sleeve is not needed. The wrist bone fracture patient bathing device used in the orthopedics department is convenient, provides more convenience to use shower gel through a liquid outlet device and makes the patient in bathing cleaned thoroughly.

The invention discloses a multi-classifier wrist bone grade recognition method based on height and age. The method of the present invention is based on a deep learning network regression model based on the two information of height and age and a plurality of three-category deep learning network classification models, first obtains the approximate range of grades through the regression model, and then selects the corresponding classifier Accurate classification of bone grades. The present invention can effectively improve the accuracy of bone age grade judgment, thereby reducing the final bone age judgment error.

The invention discloses an artificial artificial arm, aiming to provide an artificial arm with multiple functions, zero error rate and no complications. It consists of artificial finger bones, metacarpal bones (10), ulna (16), radius bones (17), tendons, tendon sheaths, pulleys (4) and skin (1); the interphalangeal joints of the artificial fingers are hinged, as are the phalanx and metacarpal bones. Hinged, the metacarpal bone and the radial bone are connected by a ball-and-socket joint; the distal end of the tendon covered with a closed tendon sheath is fixed on the distal phalanx (3) of each finger bone on both sides of the palm and the back of the hand, and the middle of it is made by an artificial pulley (4). The tendon is attached to each finger bone and wrist joint, and the proximal end enters and exits from the radial bone on both sides of the palm and the back of the hand. There are two holes communicating with the inner cavity of the radius; artificial skin can be made into gloves; artificial arms pass through The self-tapping thread is screwed into the stump of the radius of the human body, and the proximal end of each artificial tendon is sutured and connected with the corresponding tendon of the human body. The invention is suitable for patients with amputation at the distal end of the forearm.

The present invention provides a method and device for detecting the bone age of a subject based on deep learning. The method includes: an input step of inputting the X-ray picture of the subject's hand and the gender of the subject; Obtain multiple key points corresponding to each bone in the hand; the extraction step is to extract the respective initial pictures of some bones from the X-ray image; the correction step is to correct the initial pictures of each bone in the partial bones to obtain Respective corrected pictures of some bones; the first determining step is to determine the growth stage information of each bone in the partial bones, and to determine the growth stage information of the carpal bones when multiple bones do not include the carpal bones; the second determining step is to The bone age of the subject is determined based on the growth stage information of each bone in the partial skeleton, or based on the growth stage information of each bone in the partial skeleton and the growth stage information of the wrist bone.

A method for bone age assessment of hand bone X-ray films based on heterogeneous data fusion network, comprising the following steps: Step 1, preprocessing the X-ray film images, extracting the wrist bone parts in the images; Step 2, constructing a convolutional neural network to extract Image features; Step 3, build a text feature extraction model; Step 4, build a fusion layer, merge image features and text features; Step 5, model training, after full convergence, save and export the model structure and weight parameters; use the fusion obtained from training Network for bone age assessment on x-rays of hand bones. The present invention can utilize the text information and the X-ray film image information to analyze the hand bone X-ray film to obtain accurate bone age.

The invention discloses a bone age mark identification assessment method and system based on deep learning and image omics. The bone age mark identification method includes the steps: performing preprocessing of window adjusting, alignment and standardization on the wrist bone image; using a bounding box to mark the bone age characteristic areas and mark the coordinates, wherein the bone age characteristic areas include a metacarphphalangeal group and a brachidium group according with a TW3 method; according to the requirement, performing augmentation processing, and inputting the wrist bone image data to a convolutional neural network of the area based on ResNet-101 to perform multi-task (positioning, classification and assessment) training at the same time; and based on the bone age characteristic areas, combining with the clinic information (demographic characteristics and inspection reports) to further train and improve the bone age assessment speed and accuracy. The bone age markidentification assessment method and system based on deep learning and image omics firstly utilize a small number of marked samples to perform preliminary training on the bone age model, and utilize the model with relatively higher positioning detection accuracy to automatically mark a large number of samples so as to realize automatic positioning, classification and bone age assessment of the bone age characteristic areas.