Digital twins, artificial intelligence, and machine learning technology to identify a real personalized motion axis of the tibiotalar joint for robotics in total ankle arthroplasty

A digital twin model for evidence-based clinical decision support in multiple myeloma treatment

The treatment landscape for multiple myeloma (MM) has experienced substantial progress over the last decade. Despite the efficacy of new substances, patient responses tend to still be highly unpredictable. With increasing cognitive burden that is introduced through a complex and evolving treatment landscape, data-driven assistance tools are becoming more and more popular. Model-based approaches, such as digital twins (DT), enable simulation of probable responses to a set of input parameters based on retrospective observations. In the context of treatment decision-support, those mechanisms serve the goal to predict therapeutic outcomes to distinguish a favorable option from a potential failure. In the present work, we propose a similarity-based multiple myeloma digital twin (MMDT) that emphasizes explainability and interpretability in treatment outcome evaluation. We've conducted a requirement specification process using scientific literature from the medical and methodological domains to derive an architectural blueprint for the design and implementation of the MMDT. In a subsequent stage, we've implemented a four-layer concept where for each layer, we describe the utilized implementation procedure and interfaces to the surrounding DT environment. We further specify our solutions regarding the adoption of multi-line treatment strategies, the integration of external evidence and knowledge, as well as mechanisms to enable transparency in the data processing logic. Furthermore, we define an initial evaluation scenario in the context of patient characterization and treatment outcome simulation as an exemplary use case for our MMDT. Our derived MMDT instance is defined by 475 unique entities connected through 438 edges to form a MM knowledge graph. Using the MMRF CoMMpass real-world evidence database and a sample MM case, we processed a complete outcome assessment. The output shows a valid selection of potential treatment strategies for the integrated medical case and highlights the potential of the MMDT to be used for such applications. DT models face significant challenges in development, including availability of clinical data to algorithmically derive clinical decision support, as well as trustworthiness of the evaluated treatment options. We propose a collaborative approach that mitigates the regulatory and ethical concerns that are broadly discussed when automated decision-making tools are to be included into clinical routine.

A practical guide to the implementation of AI in orthopaedic research - part 1: opportunities in clinical application and overcoming existing challenges

Artificial intelligence (AI) has the potential to transform medical research by improving disease diagnosis, clinical decision-making, and outcome prediction. Despite the rapid adoption of AI and machine learning (ML) in other domains and industry, deployment in medical research and clinical practice poses several challenges due to the inherent characteristics and barriers of the healthcare sector. Therefore, researchers aiming to perform AI-intensive studies require a fundamental understanding of the key concepts, biases, and clinical safety concerns associated with the use of AI. Through the analysis of large, multimodal datasets, AI has the potential to revolutionize orthopaedic research, with new insights regarding the optimal diagnosis and management of patients affected musculoskeletal injury and disease. The article is the first in a series introducing fundamental concepts and best practices to guide healthcare professionals and researcher interested in performing AI-intensive orthopaedic research studies. The vast potential of AI in orthopaedics is illustrated through examples involving disease- or injury-specific outcome prediction, medical image analysis, clinical decision support systems and digital twin technology. Furthermore, it is essential to address the role of human involvement in training unbiased, generalizable AI models, their explainability in high-risk clinical settings and the implementation of expert oversight and clinical safety measures for failure. In conclusion, the opportunities and challenges of AI in medicine are presented to ensure the safe and ethical deployment of AI models for orthopaedic research and clinical application. Level of evidence IV.

Three-Dimensional Virtual Reconstructions of Shoulder Movements Using Computed Tomography Images: Model Development

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Evaluating the Quality and Usability of Artificial Intelligence-Generated Responses to Common Patient Questions in Foot and Ankle Surgery

Background

Artificial intelligence (AI) platforms, such as ChatGPT, have become increasingly popular outlets for the consumption and distribution of health care-related advice. Because of a lack of regulation and oversight, the reliability of health care-related responses has become a topic of controversy in the medical community. To date, no study has explored the quality of AI-derived information as it relates to common foot and ankle pathologies. This study aims to assess the quality and educational benefit of ChatGPT responses to common foot and ankle-related questions.

Methods

ChatGPT was asked a series of 5 questions, including "What is the optimal treatment for ankle arthritis?" "How should I decide on ankle arthroplasty versus ankle arthrodesis?" "Do I need surgery for Jones fracture?" "How can I prevent Charcot arthropathy?" and "Do I need to see a doctor for my ankle sprain?" Five responses (1 per each question) were included after applying the exclusion criteria. The content was graded using DISCERN (a well-validated informational analysis tool) and AIRM (a self-designed tool for exercise evaluation).

Results

Health care professionals graded the ChatGPT-generated responses as bottom tier 4.5% of the time, middle tier 27.3% of the time, and top tier 68.2% of the time.

Conclusion

Although ChatGPT and other related AI platforms have become a popular means for medical information distribution, the educational value of the AI-generated responses related to foot and ankle pathologies was variable. With 4.5% of responses receiving a bottom-tier rating, 27.3% of responses receiving a middle-tier rating, and 68.2% of responses receiving a top-tier rating, health care professionals should be aware of the high viewership of variable-quality content easily accessible on ChatGPT.

Level of Evidence

Level III, cross sectional study.

Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems

Digital twins are computer programs that use real-world data to create simulations that predict the performance of processes, products, and systems. Digital twins may integrate artificial intelligence to improve their outputs. Models for dealing with uncertainties and noise are used to improve the accuracy of digital twins. Most currently used systems aim to reduce noise to improve their outputs. Nevertheless, biological systems are characterized by inherent variability, which is necessary for their proper function. The constrained-disorder principle defines living systems as having a disorder as part of their existence and proper operation while kept within dynamic boundaries. In the present paper, we review the role of noise in complex systems and its use in bioengineering. We describe the use of digital twins for medical applications and current methods for dealing with noise and uncertainties in modeling. The paper presents methods to improve the accuracy and effectiveness of digital twin systems by continuously implementing variability signatures while simultaneously reducing unwanted noise in their inputs and outputs. Accounting for the noisy internal and external environments of complex biological systems is necessary for the future design of improved, more accurate digital twins.

The Digital Twin: A Potential Solution for the Personalized Diagnosis and Treatment of Musculoskeletal System Diseases

Due to the high prevalence and rates of disability associated with musculoskeletal system diseases, more thorough research into diagnosis, pathogenesis, and treatments is required. One of the key contributors to the emergence of diseases of the musculoskeletal system is thought to be changes in the biomechanics of the human musculoskeletal system. However, there are some defects concerning personal analysis or dynamic responses in current biomechanical research methodologies. Digital twin (DT) was initially an engineering concept that reflected the mirror image of a physical entity. With the application of medical image analysis and artificial intelligence (AI), it entered our lives and showed its potential to be further applied in the medical field. Consequently, we believe that DT can take a step towards personalized healthcare by guiding the design of industrial personalized healthcare systems. In this perspective article, we discuss the limitations of traditional biomechanical methods and the initial exploration of DT in musculoskeletal system diseases. We provide a new opinion that DT could be an effective solution for musculoskeletal system diseases in the future, which will help us analyze the real-time biomechanical properties of the musculoskeletal system and achieve personalized medicine.

Human-Centric Digital Twins in Industry: A Comprehensive Review of Enabling Technologies and Implementation Strategies

The last decade saw the emergence of highly autonomous, flexible, re-configurable Cyber-Physical Systems. Research in this domain has been enhanced by the use of high-fidelity simulations, including Digital Twins, which are virtual representations connected to real assets. Digital Twins have been used for process supervision, prediction, or interaction with physical assets. Interaction with Digital Twins is enhanced by Virtual Reality and Augmented Reality, and Industry 5.0-focused research is evolving with the involvement of the human aspect in Digital Twins. This paper aims to review recent research on Human-Centric Digital Twins (HCDTs) and their enabling technologies. A systematic literature review is performed using the VOSviewer keyword mapping technique. Current technologies such as motion sensors, biological sensors, computational intelligence, simulation, and visualization tools are studied for the development of HCDTs in promising application areas. Domain-specific frameworks and guidelines are formed for different HCDT applications that highlight the workflow and desired outcomes, such as the training of AI models, the optimization of ergonomics, the security policy, task allocation, etc. A guideline and comparative analysis for the effective development of HCDTs are created based on the criteria of Machine Learning requirements, sensors, interfaces, and Human Digital Twin inputs.

Advancements in Artificial Intelligence for Foot and Ankle Surgery: A Systematic Review

Background

There has been a rapid increase in research applying artificial intelligence (AI) to various subspecialties of orthopaedic surgery, including foot and ankle surgery. The purpose of this systematic review is to (1) characterize the topics and objectives of studies using AI in foot and ankle surgery, (2) evaluate the performance of their models, and (3) evaluate their validity (internal or external validation).

Methods

A systematic literature review was conducted using PubMed/MEDLINE and Embase databases in December 2022. All studies that used AI or its subsets machine learning (ML) and deep learning (DL) in the setting of foot and ankle surgery relevant to orthopaedic surgeons were included. Studies were evaluated for their demographics, subject area, outcomes of interest, model(s) tested, model(s)' performance, and validity (internal or external).

Results

A total of 31 studies met inclusion criteria: 14 studies investigated AI for image interpretation, 13 studies investigated AI for clinical predictions, and 4 studies were grouped as "other." Studies commonly explored AI for ankle fractures, calcaneus fractures, hallux valgus, Achilles tendon pathologies, plantar fasciitis, and sports injuries. For studies reporting the area under the receiver operating characteristic curve (AUC), AUCs ranged from 0.64 (poor) to 0.99 (excellent). Two studies (6.45%) reported external validation.

Conclusion

Applications of AI in the field of foot and ankle surgery are expanding, particularly for image interpretation and clinical predictions. Current model performances range from poor to excellent, and most studies lack external validation, demonstrating a need for further research prior to deploying AI-based clinical applications.

Level of Evidence

Level III, retrospective cohort study.

Bone conductivity and spine fluoroscopy, Hand-Eye-Ear dialogue, during pedicle screw positioning: a new human cognitive system for precision and radiation-decrease; better than artificial intelligence and machine learning system?

PURPOSE

There is an increasing need for pedicle screw positioning while decreasing radiation exposure. This study compares intra-operative radiation dose using posterior internal fixation using impedancemetry-guided pedicle positioning by the Pediguard system versus standard free-hand sighting when surgery was performed with a trainee or expert surgeon.

MATERIAL AND METHODS

Using the electrical properties of bone, the Pediguard detects iatrogenic penetration of the pedicle wall and gives auditory feedback to the surgeon. A single centre, two surgeons (one experienced and the other novice) conducted a continuous prospective randomized study for one year. Twenty patients were randomized into one group (free-hand control group) receiving pedicle instrumentation without the use of the Pediguard and the second group receiving pedicle instrumentation with the use of the Pediguard. The total screw placement times and fluoroscopic times for each screw was recorded and pedicle screw position was analyzed on post-operative CT scan.

RESULTS

Among the 104 screwed pedicles, 22 unrecognized perforations were detected by CT scan, while no perforation signal was observed intra-operatively. Only one perforation was greater than 2 mm. The overall screwing time was 4.33 ± 1.2 minutes per screw for experienced surgeon and 5.84 ± 2.5 minutes per screw for the novice. Pediguard did not increased significantly the time (0.3 mn per screw) for the experienced surgeon, but the time with Pediguard was longer (2 mn more per screw) for the novice surgeon, particularly at the thoracic level. The overall fluoroscopic average time per screw for the experienced surgeon is 5.8 ± 2.3 s and 10.4 ± 4.5 s for the novice surgeon. For the novice surgeon, radiation time reduced from 12 (without Pediguard) to 6 s (with Pediguard). There was no significant difference for the experienced surgeon in terms of improvement in radiation time with the use of Pediguard.

CONCLUSION

The overall time was longer for the novice surgeon with the Pediguard system, but allowed to decrease by 50% the fluoroscopy time.

Efficacy of tooth brushing via a three-dimensional motion tracking system for dental plaque control in school children: a randomized controlled clinical trial

BACKGROUND

School children are in a developmental period in which permanent teeth replace primary dentition. It is also a period with a high incidence of gingivitis and caries, which can be improved with adequate tooth brushing. Advances in information technology have led to the development of smart health devices that assist in tooth brushing. We compared the effectiveness of computer-assisted toothbrushing using a toothbrushing instruction (TBI) method called the smart toothbrush and smart mirror (STM) system with that of conventional TBI (verbal instructions) for plaque control in school children.

METHODS

This randomized controlled clinical trial analyzed and compared the reduction of the modified Quigley-Hein plaque index between the two methods in 42 school children. The participants were randomly assigned to the STM system group (n = 21) or conventional-TBI group (n = 21). The plaque indices were evaluated at baseline, immediately after TBI (day 0), and 1 week and 1 month after TBI.

RESULTS

The STM system and conventional TBI led to an average reduction of 40.50% and 40.57%, respectively, in whole mouth plaque. Reductions in the plaque indices within each tested time period were observed in both groups (P < 0.001), and the mean plaque reduction did not differ between the two groups (P = 0.44).

CONCLUSIONS

The present study tested a computer assisted system for TBI, more studies are needed to confirm its usefulness in different objectives. Clinical relevance The computer-assisted STM system may be an alternative of TBI for children. Trial registration ClinicalTrials.gov (NCT04627324) Registered 13/11/2020-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT04627324 .

Virtual and Real Bidirectional Driving System for the Synchronization of Manipulations in Robotic Joint Surgeries

Surgical robots are increasingly important in orthopedic surgeries to assist or replace surgeons in completing operations. During joint surgeries, the patient’s joint needs to be adjusted several times by the surgeon. Therefore, the virtual model, built on the preoperative medical images, cannot match the actual variation of the patient’s joint during the surgery. Conventional virtual reality techniques cannot fully satisfy the requirements of the joint surgeries. This paper proposes a real and virtual bidirectional driving method to synchronize the manipulations in both the real operation site and the virtual scene. The dynamic digital twin of the patient’s joint is obtained by decoupling the joint and dynamically updating its pose via the intraoperative measurements. During surgery, the surgeon can intuitively monitor the real-time position of the patient and the surgical tool through the system and can also manipulate the surgical robot in the virtual scene. In addition, the system can provide visual guidance to the surgeon when the patient’s joint is adjusted. A prototype system is developed for orthopedic surgeries. Proof-of-concept joint surgery demo is carried out to verify the effectiveness of the proposed method. Experimental results show that the proposed system can synchronize the manipulations in both the real operation site and the virtual scene, thus realizing the bidirectional driving.

Subtalar axis determined by combining digital twins and artificial intelligence: influence of the orientation of this axis for hindfoot compensation of varus and valgus knees

PURPOSE

Previous studies evaluating hindfoot and knee alignment have suggested compensation between the knee and the hindfoot deformities. However, these studies did not investigate the influence of the orientation of the subtalar axis on the results.

MATERIAL AND METHODS

Using computed tomography data of patients without osteoarthritis, digital twins, and artificial intelligence, we identified the orientation of the axis of the subtalar joint. Compensation was evaluated in the subtalar joint according to angular knee deformity and subtalar axis direction.

RESULTS

With the inclination angle defined as the angle between the axis and the XY plane (horizontal) and the deviation angle defined as the angle between the projection of axis on the XZ plane, the inclination angle of the subtalar helical axis showed an average angle of 35.3° (range 5° to 48°). The mean deviation angle for the helical axis was 6.4° (range - 4° to + 12°). Our findings indicated that an increase of the inclination angle of the subtalar axis tends to limit adjustment in the hindfoot alignment toward re-balance of the whole lower limb toward a neutral weight-bearing axis when malalignment of the knee occurs.

CONCLUSION

Malalignment of the knee and different compensations in the hindfoot contribute to various combined deformities in the population: associated valgus or varus deformities and inverse associations of varus/valgus deformities.

Machine learning and artificial intelligence in research and healthcare✰,✰✰

Artificial intelligence (AI) is a broad term referring to the application of computational algorithms that can analyze large data sets to classify, predict, or gain useful conclusions. Under the umbrella of AI is machine learning (ML). ML is the process of building or learning statistical models using previously observed real world data to predict outcomes, or categorize observations based on 'training' provided by humans. These predictions are then applied to future data, all the while folding in the new data into its perpetually improving and calibrated statistical model. The future of AI and ML in healthcare research is exciting and expansive. AI and ML are becoming cornerstones in the medical and healthcare-research domains and are integral in our continued processing and capitalization of robust patient EMR data. Considerations for the use and application of ML in healthcare settings include assessing the quality of data inputs and decision-making that serve as the foundations of the ML model, ensuring the end-product is interpretable, transparent, and ethical concerns are considered throughout the development process. The current and future applications of ML include improving the quality and quantity of data collected from EMRs to improve registry data, utilizing these robust datasets to improve and standardized research protocols and outcomes, clinical decision-making applications, natural language processing and improving the fundamentals of value-based care, to name only a few.

[Digital twins in surgery: achievements and limitations]

OBJECTIVE

To review the possible options for digital twin technology in surgery, as well as to build a patent and publication landscape for identifying technological and academic leaders of the frontier.

MATERIAL AND METHODS

Scientometric and patent analysis was performed.

RESULTS

Possible options for digital twin technology in surgical practice were reviewed. Development of scientific and technological trend «digital twins in surgery» in the world was assessed (2002 - idea, concept, definition; 2007-2014 - large-scale studies in academic sector; 2014 - active participation of regulators in translation of pilot digital models of patient organs into practical healthcare; 2014-2017 - large-scale studies in business sector; 2018-2021 - development of a market of medical services based on digital twin technologies in surgery). According to scientometric and patent analysis of digital twins in surgery, there is no a single Russian-language article on to this issue in journals indexed in WOS at the end of 2021. Our country ranks the 23^rd in the world regarding its share in the total number of patent applications for inventions.

CONCLUSION

Over a 20-year period, large-scale scientific projects have been carried out in the world to develop digital twin algorithms for surgery. Regulators were involved in the process of broadcasting their results into practical health care. Network interaction of all authors and beneficiaries of technological frontier occurred (research centers, hospitals, companies, manufacturers of medical equipment and information services). Technological ecosystems developed (startups, gazelles, investment seed capital). Technological leaders and key players in new market niches have been identified. Development of this field is insufficient in the Russian Federation. There are no qualified customers and companies in the real sector of economy that could become the beneficiaries of the frontier.