Augmented Reality: Mapping Methods and Tools for Enhancing the Human Role in Healthcare HMI

Background: Augmented Reality (AR) represents an innovative technology to improve data visualization and strengthen the human perception. Among Human–Machine Interaction (HMI), medicine can benefit most from the adoption of these digital technologies. In this perspective, the literature on orthopedic surgery techniques based on AR was evaluated, focusing on identifying the limitations and challenges of AR-based healthcare applications, to support the research and the development of further studies. Methods: Studies published from January 2018 to December 2021 were analyzed after a comprehensive search on PubMed, Google Scholar, Scopus, IEEE Xplore, Science Direct, and Wiley Online Library databases. In order to improve the review reporting, the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were used. Results: Authors selected sixty-two articles meeting the inclusion criteria, which were categorized according to the purpose of the study (intraoperative, training, rehabilitation) and according to the surgical procedure used. Conclusions: AR has the potential to improve orthopedic training and practice by providing an increasingly human-centered clinical approach. Further research can be addressed by this review to cover problems related to hardware limitations, lack of accurate registration and tracking systems, and absence of security protocols

Development of assessment in hip arthroplasty review

AbstractThis thesis describes the development of criteria in hip arthroplasty review. The insertion of a hip replacement brings relief from pain and improved function but the artificial joint does not last indefinitely. Periodic review provides the opportunity to assess the state of the joint in order to identify a failing hip arthroplasty.A literature search was conducted on the subject of failing hip arthroplasty and the findings are summarised. There was a lack of standardisation of methodology but an emphasis on the need for review because of the commonly asymptomatic nature of a failing hip arthroplasty.The review process has traditionally been completed by medical members of the orthopaedic team but there has been a recent change to include non-medical health professionals in this work. A lack of formalised educational programmes has led to innovative ways of achieving the required competency, and one such method is described for the development of a skill in interpretation of x-ray images of hip replacements.Radiographic assessment is an important component of hip arthroplasty review and includes the measurement of osteolytic lesions, a phenomenon caused by the wear particles produced from the articulating surfaces of the artificial joint. A simple, clinical tool was developed to measure these irregularly shaped lesions and the testing of the tool is described.Finally, a clinical study was conducted to explore the association between changes on a patient reported outcome measure and x-ray changes over the same period of time. The patients had all received a hip replacement approximately seven years earlier (mid-term) and so were at a stage when signs of deterioration of the hip joint were likely to appear. This thesis makes a contribution to the scientific base of arthroplasty review. It demonstrates a training model for non-medical health professions to acquire the skills needed to conduct the review. It employs basic research to develop a simple and reliable tool for use in the clinical situation. It shows that, for patients reviewed at mid-term, it is essential to include an x-ray as well as a joint-specific patient reported outcome measure. This information is important for future service planning and development of practitioners, and is of benefit to patients through adding to the evidence about the criteria for arthroplasty review

Risk factors and risk prediction models for early complications following total hip arthroplasty

Treatment of end-stage hip osteoarthritis was revolutionized in the 1960s with the newly invented low-friction total hip arthroplasty (THA). Since then, an increasing number of both primary and revision THAs have been performed annually, especially over the past two decades. To achieve better outcomes, orthopedic surgeons should carefully select optimal patients and appropriate methods and devices. Risk prediction models have been developed to inform the surgeon and patient more precisely about the expected outcomes of the surgery. The use of such a tool could engage patients more closely in the decision-making process and guide surgeons in avoiding unnecessary risk. The aims of this doctoral thesis were: 1) to determine the risk factors for revision due to dislocation after primary THA; 2) to determine the risk factors for revision due to periprosthetic joint infection (PJI) after primary THA; 3) to develop risk prediction models for assessing the risk of the most common adverse outcomes after primary THA, based on versatile registry data from Finland; and 4) to develop risk prediction models for early revisions and death, and to evaluate the predictive potential of various machine learning algorithms for complications following primary THA, based on the Nordic Arthroplasty Register Association (NARA) dataset. ,, We found that posterior approach, fracture diagnosis, and American Society of Anesthesiologists class III–IV were associated with an increased risk of revision for dislocation after primary THA. The use of a 36 mm femoral head size decreased the risk of revision for dislocation. For PJI, we identified several modifiable variables increasing and decreasing the risk of revision. Especially patients with a high body mass index may be at even higher risk of developing infection than previously reported. We also successfully developed preoperative risk prediction models for PJI, dislocation, periprosthetic fracture, and death after primary THA. Based on the NARA dataset, we were able to demonstrate that complex risk prediction methods are not required to achieve maximum predictive potential. Hence, simpler models can improve usability. All the developed models can easily be used in clinical practice to serve individual risk estimations for adverse outcomes.--- Pitkälle edenneen lonkan nivelrikon hoito mullistui, kun moderni lonkan tekonivelleikkaus yleistyi 60-luvulla. Lonkan tekonivelen ensi- ja uusintaleikkausten määrät ovat kasvaneet merkittävästi erityisesti kahden viimeisen vuosikymmenen aikana. Uusintaleikkausten välttämiseksi ortopedien tulisi huolellisesti valita ensileikkaukseen sopivat potilaat sekä parhaat mahdolliset leikkausmenetelmät ja komponentit. Viime aikoina onkin kehitetty riskilaskureita, jotta sekä kirurgien että potilaiden ymmärrys odotettavissa olevasta lopputuloksesta paranisi. Riskilaskureiden avulla potilaat voidaan ottaa paremmin mukaan yhteiseen päätöksentekoon. Tässä väitöskirjatutkimuksessa selvitettiin riskitekijöitä lonkan tekonivelleikkauksen jälkeisille uusintaleikkauksille. Erityishuomion kohteena olivat tekonivelen sijoiltaanmenot sekä infektiot. Lisäksi kehitimme riskilaskurimalleja ennustamaan potilaskohtaista riskiä tyypillisimmille komplikaatioille ja kuolemalle lonkan ensitekonivelleikkauksen jälkeen. Tämä väitöskirja perustuu uudistetun Suomen Endoproteesirekisterin ja Pohjoismaisen tekonivelrekisterin tietoihin. Tutkimuksessa havaittiin taka-avauksen, reisiluun kaulan murtumadiagnoosin ja anestesiariskiluokkien III-IV altistavan uusintaleikkaukselle tekonivelen sijoiltaanmenon vuoksi. Käytettäessä 36 mm:n halkaisijan omaavia nuppeja sijoiltaanmenoriski oli matala. Lisäksi tunnistimme useita muuttujia, jotka olivat yhteydessä tekonivelen infektoitumiseen. Erityisesti potilaat, joilla on korkea painoindeksi, saattavat olla alttiimpia tekonivelinfektiolle, kuin mitä aikaisemmin on raportoitu. Kehitimme myös onnistuneesti riskilaskurimallit ennustamaan riskiä tekonivelen uusintaleikkaukselle infektion, sijoiltaanmenon ja periproteettisen murtuman johdosta sekä kuolemalle lonkan ensitekonivelleikkauksen jälkeen. Tärkeä havainto riskilaskurimallien kehityksessä oli myös se, että yksinkertaisilla menetelmillä pystytään ennustamaan riskiä yhtä hyvin kuin monimutkaisilla menetelmillä. Kaikkia kehittämiämme malleja voi käyttää kliinisen päätöksenteon tukena arvioimaan potilaskohtaista riskiä leikkauksen jälkeiselle epäsuotuisalle päätetapahtumalle

Consensus document for the diagnosis of prosthetic joint infections:a joint paper by the EANM, EBJIS, and ESR (with ESCMID endorsement)

BACKGROUND: For the diagnosis of prosthetic joint infection, real evidence-based guidelines to aid clinicians in choosing the most accurate diagnostic strategy are lacking.AIM AND METHODS: To address this need, we performed a multidisciplinary systematic review of relevant nuclear medicine, radiological, orthopaedic, infectious, and microbiological literature to define the diagnostic accuracy of each diagnostic technique and to address and provide evidence-based answers on uniform statements for each topic that was found to be important to develop a commonly agreed upon diagnostic flowchart.RESULTS AND CONCLUSION: The approach used to prepare this set of multidisciplinary guidelines was to define statements of interest and follow the procedure indicated by the Oxford Centre for Evidence-based Medicine (OCEBM).</p

Consensus document for the diagnosis of prosthetic joint infections. a joint paper by the EANM, EBJIS, and ESR (with ESCMID endorsement)

Background: For the diagnosis of prosthetic joint infection, real evidence-based guidelines to aid clinicians in choosing the most accurate diagnostic strategy are lacking. Aim and Methods: To address this need, we performed a multidisciplinary systematic review of relevant nuclear medicine, radiological, orthopaedic, infectious, and microbiological literature to define the diagnostic accuracy of each diagnostic technique and to address and provide evidence-based answers on uniform statements for each topic that was found to be important to develop a commonly agreed upon diagnostic flowchart. Results and Conclusion: The approach used to prepare this set of multidisciplinary guidelines was to define statements of interest and follow the procedure indicated by the Oxford Centre for Evidence-based Medicine (OCEBM)

Augmented reality for computer assisted orthopaedic surgery

In recent years, computer-assistance and robotics have established their presence in operating theatres and found success in orthopaedic procedures. Benefits of computer assisted orthopaedic surgery (CAOS) have been thoroughly explored in research, finding improvements in clinical outcomes, through increased control and precision over surgical actions. However, human-computer interaction in CAOS remains an evolving field, through emerging display technologies including augmented reality (AR) – a fused view of the real environment with virtual, computer-generated holograms. Interactions between clinicians and patient-specific data generated during CAOS are limited to basic 2D interactions on touchscreen monitors, potentially creating clutter and cognitive challenges in surgery. Work described in this thesis sought to explore the benefits of AR in CAOS through: an integration between commercially available AR and CAOS systems, creating a novel AR-centric surgical workflow to support various tasks of computer-assisted knee arthroplasty, and three pre–clinical studies exploring the impact of the new AR workflow on both existing and newly proposed quantitative and qualitative performance metrics. Early research focused on cloning the (2D) user-interface of an existing CAOS system onto a virtual AR screen and investigating any resulting impacts on usability and performance. An infrared-based registration system is also presented, describing a protocol for calibrating commercial AR headsets with optical trackers, calculating a spatial transformation between surgical and holographic coordinate frames. The main contribution of this thesis is a novel AR workflow designed to support computer-assisted patellofemoral arthroplasty. The reported workflow provided 3D in-situ holographic guidance for CAOS tasks including patient registration, pre-operative planning, and assisted-cutting. Pre-clinical experimental validation on a commercial system (NAVIO®, Smith & Nephew) for these contributions demonstrates encouraging early-stage results showing successful deployment of AR to CAOS systems, and promising indications that AR can enhance the clinician’s interactions in the future. The thesis concludes with a summary of achievements, corresponding limitations and future research opportunities.Open Acces

Combining deep learning and machine learning for the automatic identification of hip prosthesis failure: Development, validation and explainability analysis

Aim: Revision hip arthroplasty has a less favorable outcome than primary total hip arthroplasty and an understanding of the timing of total hip arthroplasty failure may be helpful. The aim of this study is to develop a combined deep learning (DL) and machine learning (ML) approach to automatically detect hip prosthetic failure from conventional plain radiographs. Methods: Two cohorts of patients (of 280 and 352 patients) were included in the study, for model development and validation, respectively. The analysis was based on one antero-posterior and one lateral radiographic view obtained from each patient during routine post-surgery follow-up. After pre-processing, three images were obtained: the original image, the acetabulum image and the stem image. These images were analyzed through convolutional neural networks aiming to predict prosthesis failure. Deep features of the three images were extracted for each model and two feature-based pipelines were developed: one utilizing only the features of the original image (original image pipeline) and the other concatenating the features of the three images (3-image pipeline). The obtained features were either used directly or reduced through principal component analysis. Both support vector machine (SVM) and random forest (RF) classifiers were considered for each pipeline. Results: The SVM applied to the 3-image pipeline provided the best performance, with an accuracy of 0.958 +/- 0.006 in the internal validation and an F1-score of 0.874 in the external validation set. The explainability analysis, besides identifying the features of the complete original images as the major contributor, highlighted the role of the acetabulum and stem images on the prediction. Conclusions: This study demonstrated the potentialities of the developed DL-ML procedure based on plain radiographs in the detection of the failure of the hip prosthesis

Predicting Corrosion Damage in the Human Body Using Artificial Intelligence: In Vitro Progress and Future Applications Applications

Artificial intelligence (AI) is used in the clinic to improve patient care. While the successes illustrate the impact AI can have, few studies have led to improved clinical outcomes. A gap in translational studies, beginning at the basic science level, exists. In this review, we focus on how AI models implemented in non-orthopedic fields of corrosion science may apply to the study of orthopedic alloys. We first define and introduce fundamental AI concepts and models, as well as physiologically relevant corrosion damage modes. We then systematically review the corrosion/AI literature. Finally, we identify several AI models that may be Preprint implemented to study fretting, crevice, and pitting corrosion of titanium and cobalt chrome alloys

Digitalization in orthopaedics: a narrative review

Advances in technology and digital tools like the Internet of Things (IoT), artificial intelligence (AI), and sensors are shaping the field of orthopaedic surgery on all levels, from patient care to research and facilitation of logistic processes. Especially the COVID-19 pandemic, with the associated contact restrictions was an accelerator for the development and introduction of telemedical applications and digital alternatives to classical in-person patient care. Digital applications already used in orthopaedic surgery include telemedical support, online video consultations, monitoring of patients using wearables, smart devices, surgical navigation, robotic-assisted surgery, and applications of artificial intelligence in forms of medical image processing, three-dimensional (3D)-modelling, and simulations. In addition to that immersive technologies like virtual, augmented, and mixed reality are increasingly used in training but also rehabilitative and surgical settings. Digital advances can therefore increase the accessibility, efficiency and capabilities of orthopaedic services and facilitate more data-driven, personalized patient care, strengthening the self-responsibility of patients and supporting interdisciplinary healthcare providers to offer for the optimal care for their patients