Objective: This study proposes a Natural Language Processing pipeline to evaluate prescription appropriateness in Italian referrals, where reasons for prescriptions are recorded only as free text, complicating automated comparisons with guidelines. The pipeline aims to derive, for the first time, a comprehensive summary of the reasons behind these referrals and a quantification of their appropriateness. While demonstrated in a specific case study, the approach is designed to generalize to other types of examinations. Methods: Leveraging embeddings from a transformer-based model, the proposed approach clusters referral texts, maps clusters to labels, and aligns these labels with existing guidelines. We present a case study on a dataset of 496,971 referrals, consisting of all referrals for venous echocolordopplers of the lower limbs between 2019 and 2021 in the Lombardy Region. A sample of 1,000 referrals was manually annotated to validate the results. Results: The pipeline exhibited high performance for referrals' reasons (Prec=92.43%, Rec=83.28%) and excellent results for referrals' appropriateness (Prec=93.58%, Rec=91.52%) on the annotated subset. Analysis of the entire dataset identified clusters matching guideline-defined reasons - both appropriate and inappropriate - as well as clusters not addressed in the guidelines. Overall, 34.32% of referrals were marked as appropriate, 34.07% inappropriate, 14.37% likely inappropriate, and 17.24% could not be mapped to guidelines. Conclusions: The proposed pipeline effectively assessed prescription appropriateness across a large dataset, serving as a valuable tool for health authorities. Findings have informed the Lombardy Region's efforts to strengthen recommendations and reduce the burden of inappropriate referrals.

One of the most crucial and challenging steps in the development of robots intended to restore the mobility of the human body after a loss of functional movement due to neurological injuries is the IK of physiological limbs, which consists of computing joint angles configuration based on the predefined input workspace coordinates. Generally speaking, the complexity of the IK problem depends on the geometry of the manipulator and the nonlinearity of its model, which gives the corresponding relation between the task and the joint spaces. Furthermore, IK solution is essential for the real-time control. Thus, it must be precise in order to enable the robot to perform the task successfully. IK techniques can be classified into three categories, namely, analytical method, numerical method, and intelligent method. The analytical method solves IK by solving a set of closed-form equations that can give the generalized coordinate value that drives the end effector of the manipulator to the predefined target position [1].

Compared to classical methods as Cardan, Fick and Euler angles which are based on homogeneous transformation, dual quaternions [1] offer an advantageous representation of a rigid transformation in 3D-space in many aspects. The dual quaternions have less computer memory cost, computer memory locations, since it needs 8 elements while classical methods require 12 elements, to describe a rotation composed with translation of a rigid body in 3D-space. Thus, the homogeneous transformation methods cost more storage and require a high computational time due to the nonlinearity of the end-effector coordinates. Moreover, these methods impose a well-defined rotation order. While, the dual quaternions method enables the reduction of the number of mathematical operations which leads automatically to minimize the computational cost [2]. Moreover, dual quaternions method permits to avoid discontinuities and singularities that arise from the Euler angle representation based on cylindrical polar coordinates to represent the motion of a rigid body in 3D-space [3].

This paper presents the development and experimental evaluation of a surgical robotic system for total hip arthroplasty (THA). Although existing robotic systems used in joint replacement surgery have achieved some progresses, the robot arm must be situated accurately at the target position during operation, which depends significantly on the experience of the surgeon. In addition, handheld acetabulum reamers typically exhibit uneven strength and grinding file. Moreover, the lack of techniques to real-time measure femoral neck length may lead to poor outcomes. To tackle these challenges, we propose a real-time traceable optical positioning strategy to reduce unnecessary manual adjustments to the robotic arm during surgery, an end-effector system to stabilise grinding, and an optical probe to provide real-time measurement of the femoral neck length and other parameters used to choose the proper prosthesis. The lengths of the lower limbs are measured as the prosthesis is installed. The experimental evaluation results show that, based on its accuracy, execution ability, and robustness, the proposed surgical robotic system is feasible for THA.

Scientists and engineers led by Duke University neuroscientist Miguel A. Nicolelis report that a group of spinal-cord-injury patients who trained to walk using a brain computer interface (BCI) in combination with an Occulus Rift virtual reality device and with a robotic exoskeleton have regained the ability to voluntarily move their leg muscles and to feel touch and pain in their paralyzed limbs. The study, the results of 12 months of training, is the first long-term BCI experiment to show significant recovery from such severe injuries, the researchers say. The researchers reported their results today in the journal Scientific Reports. A non-invasive electroencephelogram-based brain computer interface was central to the therapies. During the virtual reality excercises, patients were told to imagine walking through a virtual scene.