Validation of a Robotic Testbench for Evaluating Biomechanical Effects of Implant Rotation in Total Knee Arthroplasty on a Cadaveric Specimen.

In this study, we developed and validated a robotic testbench to investigate the biomechanical compatibility of three total knee arthroplasty (TKA) configurations under different loading conditions, including varus-valgus and internal-external loading across defined flexion angles. The testbench captured force-torque data, position, and quaternion information of the knee joint. A cadaver study was conducted, encompassing a native knee joint assessment and successive TKA testing, featuring femoral component rotations at -5°, 0°, and +5° relative to the transepicondylar axis of the femur. The native knee showed enhanced stability in varus-valgus loading, with the +5° external rotation TKA displaying the smallest deviation, indicating biomechanical compatibility. The robotic testbench consistently demonstrated high precision across all loading conditions. The findings demonstrated that the TKA configuration with a +5° external rotation displayed the minimal mean deviation under internal-external loading, indicating superior joint stability. These results contribute meaningful understanding regarding the influence of different TKA configurations on knee joint biomechanics, potentially influencing surgical planning and implant positioning. We are making the collected dataset available for further biomechanical model development and plan to explore the 6 Degrees of Freedom (DOF) robotic platform for additional biomechanical analysis. This study highlights the versatility and usefulness of the robotic testbench as an instrumental tool for expanding our understanding of knee joint biomechanics.

Dentronics: tooth cleaning with a tactile collaborative robot - an in vitro proof of concept.

AIM: The aim of the present study was to compare the performance of a tactile collaborative robot programmed by a dental professional (DP) with that of a DP in the removal of surrogate plaque in vitro. MATERIALS AND METHODS: Six typodont teeth in articulated jaws were covered with surrogate plaque and cleaned by a DP with the help of a manual toothbrush (DP/manual) and an electric toothbrush (DP/electric). The experiment was repeated with the help of a collaborative seven-axis tactile robot programmed by a DP handling a manual toothbrush (robot/manual) and an electric toothbrush (robot/electric). All experiments were repeated five times, resulting in a total of N = 30 teeth in each group. Cleaning results were reported as the percentage of surface area with residual plaque. RESULTS: The cleaning results of the DP and the robot showed no significant differences. However, electric toothbrushing was significantly less effective compared with manual toothbrushing (P < 0.05). CONCLUSION: The present in vitro study indicates that current robot technology may perform the removal of surrogate plaque as efficiently as a DP. In future, this may be helpful to release nursing staff from this time-demanding task that could possibly cause contagion or to support people with reduced motor skills or impaired vision in performing daily oral hygiene.

Interproximal tooth cleaning operated by a tactile robot. An in vitro analysis.

AIM: New technologies such as tactile robots and artificial intelligence are about to find their way into clinical practice in dentistry and may contribute to the improvement of oral health care in the future. In this study we hypothesized that a collaborative, tactile robot programmed by a dental student removes interproximal artificial plaque as effectively as a human operator in an in vitro pilot trial. MATERIAL AND METHODS: Model teeth were fully covered with artificial plaque and set into phantom jaws. First, a robot was programmed by a dental student to perform interproximal cleaning with an interproximal brush. Second, teeth were covered with artificial plaque again and the dental student performed the interproximal cleaning manually. Both experiments were repeated five times. Residual plaque was measured with binary pictures. Surface coverage was reported and comparison of methods was performed with significance defined at a= 0.05. RESULTS: No statistically significant difference was found in the cleaning result between the robot and the human operator. CONCLUSION: The results of this in vitro pilot study indicate that a tactile robot with integrated artificial intelligence programmed by a dental student can perform interproximal cleaning as effectively as the dental student. Practical lmplications: In the future, the use of robot assistants to support oral hygiene, e.g., in patients with reduced motor skills or impaired vision may be further investigated.

Embedded ethics: a proposal for integrating ethics into the development of medical AI.

The emergence of ethical concerns surrounding artificial intelligence (AI) has led to an explosion of high-level ethical principles being published by a wide range of public and private organizations. However, there is a need to consider how AI developers can be practically assisted to anticipate, identify and address ethical issues regarding AI technologies. This is particularly important in the development of AI intended for healthcare settings, where applications will often interact directly with patients in various states of vulnerability. In this paper, we propose that an 'embedded ethics' approach, in which ethicists and developers together address ethical issues via an iterative and continuous process from the outset of development, could be an effective means of integrating robust ethical considerations into the practical development of medical AI.

CyberLimb: a novel robotic prosthesis concept with shared and intuitive control.

BACKGROUND: Existing assistive technologies attempt to mimic biological functions through advanced mechatronic designs. In some occasions, the information processing demands for such systems require substantial information bandwidth and convoluted control strategies, which make it difficult for the end-user to operate. Instead, a practical and intuitive semi-automated system focused on accomplishing daily tasks may be more suitable for end-user adoption. METHODS: We developed an intelligent prosthesis for the Cybathlon Global Edition 2020. The device was designed in collaboration with the prosthesis user (pilot), addressing her needs for the competition and aiming for functionality. Our design consists of a soft robotic-based two finger gripper controlled by a force-sensing resistor (FSR) headband interface, automatic arm angle dependent wrist flexion and extension, and manual forearm supination and pronation for a shared control system. The gripper is incorporated with FSR sensors to relay haptic information to the pilot based on the output of a neural network model that estimates geometries and objects material. RESULTS: As a student team of the Munich Institute of Robotics and Machine Intelligence, we achieved 12th place overall in the Cybathlon competition in which we competed against state-of-the-art prosthetic devices. Our pilot successfully accomplished two challenging tasks in the competition. During training sessions, the pilot was able to accomplish the remaining competition tasks except for one. Based on observation and feedback from training sessions, we adapted our developments to fit the user's preferences. Usability ratings indicated that the pilot perceived the prosthesis to not be fully ergonomic due to the size and weight of the system, but argued that the prosthesis was intuitive to control to perform the tasks from the Cybathlon competition. CONCLUSIONS: The system provides an intuitive interface to conduct common daily tasks from the arm discipline of the Cybathlon competition. Based on the feedback from our pilot, future improvements include the prosthesis' reduction in size and weight in order to enhance its mobility. Close collaboration with our pilot has allowed us to continue with the prosthesis development. Ultimately, we developed a simple-to-use solution, exemplifying a new paradigm for prosthesis design, to help assist arm amputees with daily activities.

Development of an Exoskeleton Platform of the Finger for Objective Patient Monitoring in Rehabilitation.

This paper presents the application of an adaptive exoskeleton for finger rehabilitation. The system consists of a force-controlled exoskeleton of the finger and wireless coupling to a mobile application for the rehabilitation of complex regional pain syndrome (CRPS) patients. The exoskeleton has sensors for motion detection and force control as well as a wireless communication module. The proposed mobile application allows to interactively control the exoskeleton, store collected patient-specific data, and motivate the patient for therapy by means of gamification. The exoskeleton was applied to three CRPS patients over a period of six weeks. We present the design of the exoskeleton, the mobile application with its game content, and the results of the performed preliminary patient study. The exoskeleton system showed good applicability; recorded data can be used for objective therapy evaluation.

Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich. / Mensch-Roboter-Interaktion: vernetzte, lernfähige Maschinen in der Medizin.

The application of robotic and intelligent technologies in healthcare is dramatically increasing. The next generation of lightweight and tactile robots have provided a great opportunity to be used for a wide range of applications from medical examination, diagnosis, therapeutic procedures to rehabilitation and assistive robotics. They can potentially outperform current medical procedures by exploiting the com- plementary strengths of humans and computer-based technologies. In this study, the importance of human- robot interaction is discussed and technological re- quirements and challenges in making human-centered robot platforms for medical applications is addressed.

SPINE20 A global advocacy group promoting evidence-based spine care of value.

PURPOSE: The Global Burden of Diseases (GBD) Studies have estimated that low back pain is one of the costliest ailments worldwide. Subsequent to GBD publications, leadership of the four largest global spine societies agreed to form SPINE20. This article introduces the concept of SPINE20, the recommendations, and the future of this global advocacy group linked to G20 annual summits. METHODS: The founders of SPINE20 advocacy group coordinated with G20 Saudi Arabia to conduct the SPINE20 summit in 2020. The summit was intended to promote evidence-based recommendations to use the most reliable information from high-level research. Eight areas of importance to mitigate spine disorders were identified through a voting process of the participating societies. Twelve recommendations were discussed and vetted. RESULTS: The areas of immediate concern were "Aging spine," "Future of spine care," "Spinal cord injuries," "Children and adolescent spine," "Spine-related disability," "Spine Educational Standards," "Patient safety," and "Burden on economy." Twelve recommendations were created and endorsed by 31/33 spine societies and 2 journals globally during a vetted process through the SPINE20.org website and during the virtual inaugural meeting November 10-11, 2020 held from the G20 platform. CONCLUSIONS: This is the first time that international spine societies have joined to support actions to mitigate the burden of spine disorders across the globe. SPINE20 seeks to change awareness and treatment of spine pain by supporting local projects that implement value-based practices with healthcare policies that are culturally sensitive based on scientific evidence.

An Adaptive Multi-Modal Control Strategy to Attenuate the Limb Position Effect in Myoelectric Pattern Recognition.

Over the last few decades, pattern recognition algorithms have shown promising results in the field of upper limb prostheses myoelectric control and are now gradually being incorporated in commercial devices. A widely used approach is based on a classifier which assigns a specific input value to a selected hand motion. While this method guarantees good performance and robustness within each class, it still shows limitations in adapting to different conditions encountered in real-world applications, such as changes in limb position or external loads. This paper proposes an adaptive method based on a pattern recognition classifier that takes advantage of an augmented dataset-i.e., representing variations in limb position or external loads-to selectively adapt to underrepresented variations. The proposed method was evaluated using a series of target achievement control tests with ten able-bodied volunteers. Results indicated a higher median completion rate >3.33% for the adapted algorithm compared to a classical pattern recognition classifier used as a baseline model. Subject-specific performance showed the potential for improved control after adaptation and a ≤13% completion rate; and in many instances, the adapted points were able to provide new information within classes. These preliminary results show the potential of the proposed method and encourage further development.

New Method for Surgical Diagnostics - a Robotic Telemedical Approach.

Apart from the tremendous increase in the demand for telemedicine during the COVID-19 pandemic, the use of telemedical technology offers many advantages, such as better coverage of rural areas and improved access to specialists. While current telediagnostic possibilities are often limited to a verbal consultation, the field of surgery has already made use of robotics for one of the most challenging areas of medicine: invasive procedures. Since comprehensive diagnostics are a prerequisite for each surgery, we built upon the knowledge gained in telesurgery and developed a telediagnostic system that allows for an extensive perioperative and emergency examination. It is based on a robotic platform consisting of a remote lead robotic arm at the physician's site and a follower robot at the patient's site. Mirroring all movements directly and using force-feedback, both parties can precisely interact, enabling tasks such as auscultation, percussion, and palpation without the need for extensive training. Our overall setup also includes the possibility to measure and monitor all relevant vital parameters and can be used to perform ear and nasopharyngeal inspections as well as an automatic swab to screen for COVID or other contagious diseases prior to hospital admission. In this paper, we focus on the potential of this technology for the surgical community by demonstrating the ease of adding an ultrasound probe to our modular setup to perform a high-quality emergency ultrasound examination. While the system is not yet ready for everyday use in a hospital and drawbacks such as a high cost persist, our setup paves the way for the future use of telediagnostics in surgery.

Reach and grasp by people with tetraplegia using a neurally controlled robotic arm.

Paralysis following spinal cord injury, brainstem stroke, amyotrophic lateral sclerosis and other disorders can disconnect the brain from the body, eliminating the ability to perform volitional movements. A neural interface system could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with long-standing tetraplegia can use a neural interface system to move and click a computer cursor and to control physical devices. Able-bodied monkeys have used a neural interface system to control a robotic arm, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here we demonstrate the ability of two people with long-standing tetraplegia to use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm and hand over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor 5 years earlier, also used a robotic arm to drink coffee from a bottle. Although robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after injury to the central nervous system, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.

Autonomous swab robot for naso- and oropharyngeal COVID-19 screening.

The COVID-19 outbreak has triggered a global health and economic crisis, necessitating widespread testing to control viral spread amidst rising cases and fatalities. The recommended testing method, a combined naso- and oropharyngeal swab, poses risks and demands limited protective gear. In response to the COVID-19 pandemic, we developed and tested the first autonomous swab robot station for Naso- and Oropharyngeal Coronavirus Screening (SR-NOCS). A force-sensitive robot running under a Cartesian impedance controller is employed to drive the swab to the sampling area. This groundbreaking device underwent two clinical studies-one conducted during the initial pandemic lockdown in Europe (early 2021) and the other, more recently, in a public place after the pandemic had subsided earlier in the year 2023. In total, 52 patients suspected of COVID-19 infection were included in these clinical studies. The results revealed a complete positive correlation between autonomous and manual sampling. The test subjects exhibited a high acceptance rate, all expressing a willingness to undergo future tests with SR-NOCS. Based on our findings, such systems could enhance testing capabilities, potentially conducting up to 300 tests per robot per day with consistent precision. The tests can be carried out with minimal supervision, reducing infection risks and effectively safeguarding patients and healthcare workers.

Toward telemedical diagnostics-clinical evaluation of a robotic examination system for emergency patients.

Introduction: The SARS-CoV-2 pandemic has affected global public healthcare for several years. Numerous medical professionals have been infected since the outbreak in 2019, resulting in a shortage of healthcare providers. Since traditional personal protective wear was insufficient to eliminate the virus transmission reliably, new strategies to avoid cross-infection were imperative while enabling high-quality medical care. In the project ProteCT, we investigated the potential of robotic-assisted examination in providing medical examination via a telemedical approach. Material and Methods: We constructed a fully functional examination cabin equipped with cameras, microphones, screens and robotic arms to evaluate usability and perception. Therefore, we conducted a preliminary study with 10 healthy volunteers and 10 physicians to gain first insights and optimize the setup. In a second step, we performed telemedical examinations of actual patients from the local emergency department to compare the robotic approach with the classical method of measuring vital signs, auscultation, palpation and percussion. Results: The preliminary study identified basic requirements, such as the need for force-feedback and telemedical training for physicians. In the main study, acceptance was high and most patients indicated they would use a telemedical system again. Our setup enabled the physician to make the same diagnoses as by classic examination in the emergency department in most cases. Discussion: The potential acceptance of a telemedical system such as ProteCT is high. Robotic telemedical approaches could complement future healthcare beyond the Corona pandemic to reach rural areas or even war zones. Moreover, the daily clinical use of robotic telemedicine could improve patients' safety, the quality of perioperative management and the workflow in any medical facility. Conclusion: The development of telemedical and telerobotic systems is a multidisciplinary and complex challenge. However, acceptance of the proposed system was high among patients and physicians, indicating the potential use of similar systems for future healthcare.

Multicentric development and validation of a multi-scale and multi-task deep learning model for comprehensive lower extremity alignment analysis.

Osteoarthritis of the knee, a widespread cause of knee disability, is commonly treated in orthopedics due to its rising prevalence. Lower extremity misalignment, pivotal in knee injury etiology and management, necessitates comprehensive mechanical alignment evaluation via frequently-requested weight-bearing long leg radiographs (LLR). Despite LLR's routine use, current analysis techniques are error-prone and time-consuming. To address this, we conducted a multicentric study to develop and validate a deep learning (DL) model for fully automated leg alignment assessment on anterior-posterior LLR, targeting enhanced reliability and efficiency. The DL model, developed using 594 patients' LLR and a 60%/10%/30% data split for training, validation, and testing, executed alignment analyses via a multi-step process, employing a detection network and nine specialized networks. It was designed to assess all vital anatomical and mechanical parameters for standard clinical leg deformity analysis and preoperative planning. Accuracy, reliability, and assessment duration were compared with three specialized orthopedic surgeons across two distinct institutional datasets (136 and 143 radiographs). The algorithm exhibited equivalent performance to the surgeons in terms of alignment accuracy (DL: 0.21 ± 0.18°to 1.06 ± 1.3°vs. OS: 0.21 ± 0.16°to 1.72 ± 1.96°), interrater reliability (ICC DL: 0.90 ± 0.05 to 1.0 ± 0.0 vs. ICC OS: 0.90 ± 0.03 to 1.0 ± 0.0), and clinically acceptable accuracy (DL: 53.9%-100% vs OS 30.8%-100%). Further, automated analysis significantly reduced analysis time compared to manual annotation (DL: 22 ± 0.6 s vs. OS; 101.7 ± 7 s, p ≤ 0.01). By demonstrating that our algorithm not only matches the precision of expert surgeons but also significantly outpaces them in both speed and consistency of measurements, our research underscores a pivotal advancement in harnessing AI to enhance clinical efficiency and decision-making in orthopaedics.

Robust Independent Component Analysis based EMG decomposition - a comparison study.

High density surface Electromyography (HD-sEMG) provides a high fidelity measurement of the myoelectric activity that can be leveraged by EMG decomposition methods to estimate the motor neuron discharges. Independent Component Analysis (ICA) methods are used as basis for many EMG decomposition algorithms, for the estimation of motor unit action potential signals. Accurate source separation is a non-trivial task in EMG decomposition. While FastICA is widely used for this purpose, other methods with attractive characteristics, such as RobustICA, remain relatively unexplored. The purpose of the current work is to compare three different ICA-based EMG decomposition methods (FastICA, RobustICA and RobustICALCH) in terms of decomposition accuracy and computation time. The evaluation was performed on simulated data using a decomposition algorithm inspired by previous studies. Our results demonstrate that RobustICA outperforms the other methods in terms of number of correctly identified motor units, high decomposition accuracy, and low computation time, across different muscle contraction levels.

Machine learning-driven self-discovery of the robot body morphology.

The morphology of a robot is typically assumed to be known, and data from external measuring devices are used mainly for its kinematic calibration. In contrast, we take an agent-centric perspective and ponder the vaguely explored question of whether a robot could learn elements of its morphology by itself, relying on minimal prior knowledge and depending only on unorganized proprioceptive signals. To answer this question, we propose a mutual information-based representation of the relationships between the proprioceptive signals of a robot, which we call proprioceptive information graphs (π-graphs). Leveraging the fact that the information structure of the sensorimotor apparatus is dependent on the embodiment of the robot, we use the π-graph to look for pairwise signal relationships that reflect the underlying kinematic first-order principles applicable to the robot's structure. In our discussion, we show that analysis of the π-graph leads to the inference of two fundamental elements of the robot morphology: its mechanical topology and corresponding kinematic description, that is, the location and orientation of the robot's joints. Results from a robot manipulator, a hexapod, and a humanoid robot show that the correct topology and kinematic description can be effectively inferred from their π-graph either offline or online, regardless of the number of links and body configuration.

Estimating Joint Kinematics and Muscles Forces During Robotic Rehabilitation to Detect and Counteract Reduced Ankle Mobility.

The paper presents a solution to detect active ankle joint movement while a patient undergoes therapy with a robotic lower limb rehabilitation device that neither restricts nor actively supports ankle dorsi- or plantarflexion. The presented method requires the addition of only two accelerometer sensors to the system as well as a musculoskeletal model of the lower limb. Using forward kinematics and inverse dynamics, it enables knee and ankle joint kinematic tracking in the sagittal plane and muscle force estimation. This is an extension of a previous work in which only hip joint tracking was possible and, thus, muscle force estimation was limited. The correlation results of the current validation study with 12 healthy subjects show high correlation (R=0.88±0.09) between the kinematics estimated with the proposed method and those calculated from a gold standard motion capture setup for all three joints (hip, knee, and ankle). The correlation results of the estimated m. tibialis anterior muscle force against electromyography measurements (R = 0.62±0.27) are promising and a first application to a patient data set shows potential for future clinical application.

From Human Hand to Grasp Surface Detection, Tracking & Analysis.

Hands are paramount for dexterous interactions that humans exhibit in daily life. Understanding the intricacies of human hand-object interactions is therefore necessary. Unfortunately, the limitations of state-of-the-art technologies make capturing the full hand-object complexity unfeasible, giving rise to the need for new technological means to achieve this aim. In this work, we propose an end-to-end framework in which individualized hand models are derived and used to capture quantitative personalized hand-object interaction information, precisely, hand shape, kinematics, and contact surfaces. The results of this study serve as a proof of concept that such a framework can significantly deepen personalized hand-object interaction analyses, providing, in perspective, insights for medical diagnoses and rehabilitation, among others.Clinical relevance- Our work showcases the need to incorporate bespoke human hand models in individualized hand function assessment technologies, as hand-object interaction information is subject-dependent.