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Designing for Hand and Wrist Anatomy
Published in Karen L. LaBat, Karen S. Ryan, Human Body, 2019
The wrist and hand contain many small bones that provide flexibility and dexterity (Figure 7.1). A network of ligaments and fascia join the bones, while tendons and muscles move the bones in relationship to each other. Following distally from the styloid processes of the radius and the ulna, (bumps you can palpate on either side of your wrist), the next set of bones are the carpalbones, commonly called the wrist. This group of eight small irregularly-shaped bones in the base of the hand bridges between the more stable bones of the hand, the metacarpals, and the radius and ulna of the forearm. The narrowest circumference of the wrist/forearm, where a bracelet or fitness or health monitoring band might be worn, is located (a) near the distal ends of the radius and ulna or (b) over the proximal row of carpal bones. The metacarpal bones are the bones of the palm of the hand and the base of the thumb. The next set, and most distal bones, are the phalanges of the fingers and thumb.
Cumulative Trauma
Published in Ronald Scott, of Industrial Hygiene, 2018
Now feel your wrist, and recognize that it is primarily a bony structure. The wrist is a relatively rigid structure made of eight carpal bones arranged in a row and connected to one another by ligaments. A closed loop is formed by connecting one end of this array with the other on the palm side by a transverse ligament. Nine tendons and the median nerve reach the hand through a passage about the diameter of a small coin between the wrist bones called the carpal tunnel. The transverse ligament does not stretch significantly, so the tunnel does not expand during heavy activity.
Cross-sectional changes of the distal carpal tunnel with simulated carpal bone rotation
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2022
Given the effectiveness of these therapeutic options and the understanding of how changes in the carpal tunnel cross-section can serve to relieve median nerve compression, it is of interest to understand how the carpal tunnel cross-section is affected by the motion of the carpal bones. To investigate this, a computational model was developed to measure changes in the distal carpal tunnel cross-section resulting from internal hamate and trapezium rotation. The specific morphological parameters of interest were the carpal arch width (CAW), carpal arch area (CAA), carpal arch height (CAH), bone arch area (BAA), bone arch height (BAH) and total cross-sectional area (CSA).
TW3-based ROI detection and classification using a chaotic ANN and DNN-EVGO architecture for an automated bone age assessment on hand X-ray images
Published in The Imaging Science Journal, 2023
Thangam Palaniswamy, Mahendiran Vellingiri, M. Ramkumar Raja
The traditional skeletal BAA technique collects the regions for bone age prediction. Some of the strategies attain the needed forecast accuracy. Regardless, deep learning systems with automated BAA methods performed well by extracting constant-size or tiny-size pictures. Including carpal bones in the measurement of bone age in children of smaller ages is required. However, several difficulties in building the evaluation method include categorization of carpal bone borders, automated classification, soft tissue variation, poor differences between skeletal structures and soft tissue, and an unclear range of bone showing. Several ways are offered to assess bone age, each with unique characteristics and limitations. Faster Region-CNN [27] has been suggested in earlier studies for increasing training and recognition time while avoiding over-fitting issues. However, restriction in terms of exploring is done in other ways. CNN-W-CTO-BAA [27] improves efficiency while decreasing rand error. The restricted GPU memory, however, limits the device’s performance. Deep Neural Network [29] enhances evaluation accuracy and is clinically applicable. It will never be able to replace a human radiologist. Lightweight U-Net [30] delivers superior segmentation results and increases the model's performance. However, it needs additional training time. SE-ResNet [9] has been proposed for enhancing estimate accuracy. It works well in quiet environments. In contrast, it is unsuitable for combining entropy-related features and discriminant data. BoNet + CNN [3] achieves improved performance and automatically diagnoses bone ageing. It is restricted owing to limited computational resources and data volume. These constraints are being explored for the future development of novel bone age assessment methods in paediatrics.
Evaluation of fixation after plating of distal radius fractures - a validation study
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2021
Takahiro Yamazaki, Yusuke Matsuura, Takane Suzuki, Seiji Ohtori
The radius, ulna, and carpal bones were simulated using 1.0 mm linear tetrahedral elements and were overlaid with 1.0 mm triangular shell elements. Areas of cartilage were modeled between bones. We expanded the carpal bone, periarticular radius, and ulnar bone regions of interest by 2 mm and modeled this expansion as cartilage. Cartilage was simulated using 1.0 mm linear tetrahedral elements without triangular shell elements. Plates and screws were simulated using 0.3 mm linear tetrahedral elements without triangular shell elements.