Assessing hand grasp in patients with systemic sclerosis using the 16-grasp test: Preliminary results from a multidisciplinary study group.

BACKGROUND: Reports on hand dysfunction and rehabilitation in SSc are quite scarce in the literature and mainly focus on functional assessment tools, such as the Duruoz Hand Index and the HAMIS test for evaluating hand mobility by simulating specific grasps with nine different objects. PURPOSE OF THE STUDY: This study aimed to provide an adequate assessment methodology for hand grasp dysfunctions in patients suffering from systemic sclerosis (SSc) through the 16-grasp test. STUDY DESIGN: Case-control study. METHODS: Ninety-seven consecutive SSc patients were recruited at our Scleroderma Unit, where a 16-grasp test was performed by all patients and supervised by an experienced hand therapist. Sixteen different patterns of grasp have been divided into power grasps and precision pinch and two more modalities: static and dynamic prehension evaluation on scale from 0 to 4. We also compared previous evaluations on 19 of patients recruited. RESULTS: The majority of SSc patients (84 females and 13 males; mean age 56.0±12.0 years; mean disease duration 8.0±6.0 years) displayed grasp dysfunctions; in particular 48% and 54% reported slight difficulty in the right and left grasps respectively, 6% medium difficulty in both hands, and only 3% and 1% experienced severe difficulty respectively, while 31.5% had no issues in either hand. Our results showed that the limited cutaneous subset (lcSSc) scored a lower deficit for either grasp compared to diffuse form (dcSSc). No statistically significant differences in total grasp deficit had been noticed when comparing patients having a disease duration < 5 years or longer. In the retrospective study on 19 of these patients, 8 out of 10 lcSSc patients showed no significant changes, while in 2 out of 10, slight improvements were observed in both hands. However, in the dcSSc group, 4 out of 9 worsened bilaterally while the grasp scores for 5 of them remained unchanged. CONCLUSION: Our study reported hand involvement in both lcSSc and dcSSc forms, more significantly in dcSSc patients. This test is intended to be a more objective means of assessing grasp alterations linked to scleroderma hand deformities. Furthermore, thanks to its intuitiveness, the test may be useful for engineers designing personalized ergonomic assistive devices.

The REHAB-LAB model for individualized assistive device co-creation and production.

Assistive devices are designed to enhance individuals with disabilities' functional abilities. The rise of 3D printing technology enabled the production of individualized assistive devices (IADs). A REHAB-LAB is intended for IAD provision involving technical referents and occupational therapists. This study aimed to develop the REHAB-LAB logic model; to explore its fidelity and desirability; and to explore the characteristics of arising initiatives of IAD production. The REHAB-LAB logic model development involved stakeholders throughout the research process. A pragmatic multimethod approach followed two phases 1) logic model development and 2) exploration of its fidelity and desirability. The REHAB-LAB logic model presented the resources (equipment, space, human) required to implement IAD provision in a rehabilitation center, and the expected deliverables (activities and outputs). The REHAB-LAB logic model highlights the interdisciplinarity of IAD provision including occupational therapists, doctors, engineers, managers, and technical referents and places the users at the center of the IAD production. Results confirmed the fidelity and desirability of the REHAB-LAB logic model. The REHAB-LAB logic model can be used as a reference for future healthcare organizations wishing to implement an IAD provision. This research highlighted the interest of IAD provision based on the REHAB-LAB model involving users and transdisciplinary practices.

Classification of Geometric Forms in Mosaics Using Deep Neural Network.

The paper addresses an image processing problem in the field of fine arts. In particular, a deep learning-based technique to classify geometric forms of artworks, such as paintings and mosaics, is presented. We proposed and tested a convolutional neural network (CNN)-based framework that autonomously quantifies the feature map and classifies it. Convolution, pooling and dense layers are three distinct categories of levels that generate attributes from the dataset images by introducing certain specified filters. As a case study, a Roman mosaic is considered, which is digitally reconstructed by close-range photogrammetry based on standard photos. During the digital transformation from a 2D perspective view of the mosaic into an orthophoto, each photo is rectified (i.e., it is an orthogonal projection of the real photo on the plane of the mosaic). Image samples of the geometric forms, e.g., triangles, squares, circles, octagons and leaves, even if they are partially deformed, were extracted from both the original and the rectified photos and originated the dataset for testing the CNN-based approach. The proposed method has proved to be robust enough to analyze the mosaic geometric forms, with an accuracy higher than 97%. Furthermore, the performance of the proposed method was compared with standard deep learning frameworks. Due to the promising results, this method can be applied to many other pattern identification problems related to artworks.