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1. Comparison of in‐person and synchronous remote musculoskeletal exam using augmented reality and haptics: A pilot study

   https://doi.org/10.1002/pmrj.12883  

   Borresen, Aleks ; Chakka, Keerthana ; Wu, Richard ; Lin, Chung‐Kuang ; Wolfe, Cody ; Prabhakaran, Balakrishnan ; Annaswamy, Thiru M. ( October 2022 , PM&R)

   Utilization of telemedicine for health care delivery increased rapidly during the coronavirus disease 2019 (COVID‐19) pandemic. However, physical examination during telehealth visits remains limited. A novel telerehabilitation system—The Augmented Reality‐based Telerehabilitation System with Haptics (ARTESH)—shows promise for performing synchronous, remote musculoskeletal examination.

   To assess the potential of ARTESH in remotely examining upper extremity passive range of motion (PROM) and maximum isometric strength (MIS).

   In this cross‐sectional pilot study, we compared the in‐person (reference standard) and remote evaluations (ARTESH) of participants' upper extremity PROM and MIS in 10 shoulder and arm movements. The evaluators were blinded to each other's results.

   Participants underwent in‐person evaluations at a Veterans Affairs hospital's outpatient Physical Medicine and Rehabilitation (PM&R) clinic, and underwent remote examination using ARTESH with the evaluator located at a research lab 30 miles away, connected via a high‐speed network.

   Fifteen participants with upper extremity pain and/or weakness.

   Not applicable.

   Inter‐rater agreement between in‐person and remote evaluations on 10 PROM and MIS movements and presence/absence of pain with movement was calculated.

   The highest inter‐rater agreements were noted in shoulder abduction and protraction PROM (kappa (κ) = 0.44, confidence interval (CI): −0.1 to 1.0), and in elbow flexion, shoulder abduction, and shoulder protraction MIS (κ = 0.63, CI: 0 to 1.0).

   This pilot study suggests that synchronous tele‐physical examination using the ARTESH system with augmented reality and haptics has the potential to provide enhanced value to existing telemedicine platforms. With the additional technological and procedural improvements and with an adequately powered study, the accuracy of ARTESH‐enabled remote tele‐physical examinations can be better evaluated.

    

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2. Volumetric Communication in Medical Training - CPR Emergencies

   https://doi.org/10.21125/edulearn.2023.2014  

   Pietroszek, Krzysztof ; Rebol, Manuel ; Steinmaurer, Alexander ; Gamillscheg, Florian ; Gütl, Christian ; Ranniger, Claudia ; Hood, Colton ; Rutenberg, Adam ; Sikka, Neal ( July 2023 , 15th International Conference on Education and New Learning Technologies)

   Over the last two years, we built, evaluated, and published a volumetric communication system for volumetric-based training and assistantship of medical personnel. Technical aspects of the system have been published at HICCS conference. In this paper, we discuss a follow-up work: the design and evaluation of a mixed reality real-time communication system for remote assistance during CPR emergencies. Our system allows an expert to guide a first responder, remotely, on how to give first aid. RGBD cameras capture a volumetric view of the local scene including the patient, the first responder, and the environment. The volumetric capture is augmented onto the remote expert's view to spatially guide the first responder using visual and verbal instructions. We evaluate the mixed reality communication system in a research study in which participants face a simulated emergency. The first responder moves the patient to the recovery position and performs chest compressions as well as mouth-to-mask ventilation. Our study compares mixed reality against videoconferencing-based assistance using CPR performance measures, cognitive workload surveys, and semi-structured interviews. We find that more visual communication including gestures and objects is used by the remote expert when assisting in mixed reality compared to videoconferencing. Moreover, the performance and the workload of the first responder during simulation do not differ significantly between the two technologies. 

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3. Velocity-Domain Motion Quality Measures for Surgical Performance Evaluation and Feedback

   https://doi.org/10.1115/1.4049310  

   Murali, Barathwaj ; Belvroy, Viony M. ; Pandey, Shivam ; Bismuth, Jean ; Byrne, Michael D. ; O'Malley, Marcia K. ( December 2020 , Journal of Medical Devices)

   null (Ed.)

   Abstract Endovascular navigation proficiency requires a significant amount of manual dexterity from surgeons. Objective performance measures derived from endovascular tool tip kinematics have been shown to correlate with expertise; however, such metrics have not yet been used during training as a basis for real-time performance feedback. This paper evaluates a set of velocity-based performance measures derived from guidewire motion to determine their suitability for online performance evaluation and feedback. We evaluated the endovascular navigation skill of 75 participants using three metrics (spectral arc length, average velocity, and idle time) as they steered tools to anatomical targets using a virtual reality simulator. First, we examined the effect of navigation task and experience level on performance and found that novice performance was significantly different from intermediate and expert performance. Then we computed correlations between measures calculated online and spectral arc length, our "gold standard" metric, calculated offline (at the end of the trial, using data from the entire trial). Our results suggest that average velocity and idle time calculated online are strongly and consistently correlated with spectral arc length computed offline, which was not the case when comparing spectral arc length computed online and offline. Average velocity and idle time, both time-domain based performance measures, are therefore more suitable measures than spectral arc length, a frequency-domain based metric, to use as the basis of online performance feedback. Future work is needed to determine how to best provide real-time performance feedback to endovascular surgery trainees based on these metrics. 

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4. A mixed reality system combining augmented reality, 3D bio-printed physical environments and inertial measurement unit sensors for task planning

   https://doi.org/10.1007/s10055-023-00777-0  

   Kabuye, Ernest ; LeDuc, Philip ; Cagan, Jonathan ( March 2023 , Virtual Reality)

   Successful surgical operations are characterized by preplanning routines to be executed during actual surgical operations. To achieve this, surgeons rely on the experience acquired from the use of cadavers, enabling technologies like virtual reality (VR) and clinical years of practice. However, cadavers, having no dynamism and realism as they lack blood, can exhibit limited tissue degradation and shrinkage, while current VR systems do not provide amplified haptic feedback. This can impact surgical training increasing the likelihood of medical errors. This work proposes a novel Mixed Reality Combination System (MRCS) that pairs Augmented Reality (AR) technology and an inertial measurement unit (IMU) sensor with 3D printed, collagen-based specimens that can enhance task performance like planning and execution. To achieve this, the MRCS charts out a path prior to a user task execution based on a visual, physical, and dynamic environment on the state of a target object by utilizing surgeon-created virtual imagery that, when projected onto a 3D printed biospecimen as AR, reacts visually to user input on its actual physical state. This allows a real-time user reaction of the MRCS by displaying new multi-sensory virtual states of an object prior to performing on the actual physical state of that same object enabling effective task planning. Tracked user actions using an integrated 9-Degree of Freedom IMU demonstrate task execution This demonstrates that a user, with limited knowledge of specific anatomy, can, under guidance, execute a preplanned task. In addition, to surgical planning, this system can be generally applied in areas such as construction, maintenance, and education.

    

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5. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   null (Ed.)

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert data scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge. 

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