The IoRT-in-Hand: Tele-Robotic Echography and Digital Twins on Mobile Devices.

The integration of robotics and mobile networks (5G/6G) through the Internet of Robotic Things (IoRT) is revolutionizing telemedicine, enabling remote physician participation in scenarios where specialists are scarce, where there is a high risk to them, such as in conflicts or natural disasters, or where access to a medical facility is not possible. Nevertheless, touching a human safely with a robotic arm in non-engineered or even out-of-hospital environments presents substantial challenges. This article presents a novel IoRT approach for healthcare in or from remote areas, enabling interaction between a specialist's hand and a robotic hand.

Automated ultrasound system ARTHUR V.2.0 with AI analysis DIANA V.2.0 matches expert rheumatologist in hand joint assessment of rheumatoid arthritis patients.

Objective: To evaluate the agreement and repeatability of an automated robotic ultrasound system (ARTHUR V.2.0) combined with an AI model (DIANA V.

Safe contact-based robot active search using Bayesian optimization and control barrier functions.

In robotics, active exploration and learning in uncertain environments must take into account safety, as the robot may otherwise damage itself or its surroundings. This paper presents a method for safe active search using Bayesian optimization and control barrier functions. As robot paths undertaken during sampling are continuous, we consider an informative continuous expected improvement acquisition function.

Artificial intelligence model for segmentation and severity scoring of osteophytes in hand osteoarthritis on ultrasound images.

Objective: To develop an artificial intelligence (AI) model able to perform both segmentation of hand joint ultrasound images for osteophytes, bone, and synovium and perform osteophyte severity scoring following the EULAR-OMERACT grading system (EOGS) for hand osteoarthritis (OA).

Methods: One hundred sixty patients with pain or reduced function of the hands were included. Ultrasound images of the metacarpophalangeal (MCP), proximal interphalangeal (PIP), distal interphalangeal (DIP), and first carpometacarpal (CMC1) joints were then manually segmented for bone, synovium and osteophytes and scored from 0 to 3 according to the EOGS for OA.

Retinal main vessel calibers and systemic markers for long-term development of proliferative diabetic retinopathy.

Purpose: To evaluate if retinal vascular calibers and systemic risk factors in patients with no or minimal diabetic retinopathy (DR) can predict risk of long-term progression to proliferative diabetic retinopathy (PDR).

Methods: This was a matched case-control study of patients with diabetes having no or minimal DR at baseline with (cases) or without (controls) subsequent development of PDR. We collected six-field, 45-degree retinal images, demographic and clinical data from the Funen Diabetes Database.

Five-Year Incidence of Proliferative Diabetic Retinopathy and Associated Risk Factors in a Nationwide Cohort of 201 945 Danish Patients with Diabetes.

Purpose: To evaluate the proliferative diabetic retinopathy (PDR) progression rates and identify the demographic and clinical characteristics of patients who later developed PDR compared with patients who did not progress to that state.

Design: A national 5-year register-based cohort study including 201 945 patients with diabetes.

Subjects: Patients with diabetes who had attended the Danish national screening program (2013-2018) for diabetic retinopathy (DR).

E-learning and practical performance in musculoskeletal ultrasound: a multicentre randomized study.

Objectives: To examine the effect of pre-course e-learning on residents' practical performance in musculoskeletal ultrasound (MSUS).

Methods: This was a multicentre, randomized controlled study following the Consolidated Standards of Reporting Trials (CONSORT) statement. Residents with no or little MSUS experience were randomized to either an e-learning group or a traditional group.

Ultrasound joint examination by an automated system versus by a rheumatologist: from a patient perspective.

Background: The Arthritis Ultrasound Robot (ARTHUR) is an automated system for ultrasound scanning of the joints of both hands and wrists, with subsequent disease activity scoring using artificial intelligence. The objective was to describe the patient's perspective of being examined by ARTHUR, compared to an ultrasound examination by a rheumatologist. Further, to register any safety issues with the use of ARTHUR.

Monopolar, bipolar, tripolar, and tetrapolar configurations in robot assisted electrical impedance sensing.

Tissue recognition is a critical process during a Robot-assisted minimally invasive surgery (RMIS) and it relies on the involvement of advanced sensing technology.In this paper, the concept of Robot Assisted Electrical Impedance Sensing (RAEIS) is utilized and further developed aiming to sense the electrical bioimpedance of target tissue directly based on the existing robotic instruments and control strategy. Specifically, we present a new sensing configuration called pseudo-tetrapolar method.

A digital online platform for education and certification of diabetic retinopathy health care professionals in the Region of Southern Denmark.

Purpose: The incidence of diabetes continues to increase across the world. As the number of patients rises, so does the need for educated health care professionals. Diabetic retinopathy (DR) remains one of the primary complications in diabetes, and screening has proved to be a cost-effective measure to avoid DR-related blindness.

A disposable force regulation mechanism for throat swab robot.

Robots can protect healthcare workers from being infected by the COVID-19 and play a role in throat swab sampling operation. A critical requirement in this process is to maintain a constant force on the tissue for ensuring a safe and good sampling. In this study, we present the design of a disposable mechanism with two non-linear springs to achieve a 0.

Robot-Assisted Electrical Impedance Scanning system for 2D Electrical Impedance Tomography tissue inspection.

The electrical impedance tomography (EIT) technology is an important medical imaging approach to show the electrical characteristics and the homogeneity of a tissue region noninvasively. Recently, this technology has been introduced to the Robot Assisted Minimally Invasive Surgery (RAMIS) for assisting the detection of surgical margin with relevant clinical benefits. Nevertheless, most EIT technologies are based on a fixed multiple-electrodes probe which limits the sensing flexibility and capability significantly.

An Electrical Bioimpedance Scanning System for Subsurface Tissue Detection in Robot Assisted Minimally Invasive Surgery.

In Robot Assisted Minimally Invasive Surgery, discriminating critical subsurface structures is essential to make the surgical procedure safer and more efficient. In this paper, a novel robot assisted electrical bio-impedance scanning (RAEIS) system is developed and validated using a series of experiments. The proposed system constructs a tri-polar sensing configuration for tissue homogeneity inspection.

Deep learning detects and visualizes bleeding events in electronic health records.

Background: Bleeding is associated with a significantly increased morbidity and mortality. Bleeding events are often described in the unstructured text of electronic health records, which makes them difficult to identify by manual inspection.

Objectives: To develop a deep learning model that detects and visualizes bleeding events in electronic health records.

Applying cascaded convolutional neural network design further enhances automatic scoring of arthritis disease activity on ultrasound images from rheumatoid arthritis patients.

Objectives: We have previously shown that neural network technology can be used for scoring arthritis disease activity in ultrasound images from rheumatoid arthritis (RA) patients, giving scores according to the EULAR-OMERACT grading system. We have now further developed the architecture of this neural network and can here present a new idea applying cascaded convolutional neural network (CNN) design with even better results. We evaluate the generalisability of this method on unseen data, comparing the CNN with an expert rheumatologist.

Neural networks for automatic scoring of arthritis disease activity on ultrasound images.

Background: The development of standardised methods for ultrasound (US) scanning and evaluation of synovitis activity by the OMERACT-EULAR Synovitis Scoring (OESS) system is a major step forward in the use of US in the diagnosis and monitoring of patients with inflammatory arthritis. The variation in interpretation of disease activity on US images can affect diagnosis, treatment and outcomes in clinical trials. We, therefore, set out to investigate if we could utilise neural network architecture for the interpretation of disease activity on Doppler US images, using the OESS scoring system.