Conference Agenda

Introduction

In orthopaedics, traditional treatments generally involve the use of techniques and medical devices developed on the basis of population samples. However, they are unfortunately unlikely to account for the actual multiple characteristics specific to each individual patient, including all morphological and functional parameters of the anatomical complex under treatment. As surgical instrumentation and implantable devices are generally not sized to the patient's specific anatomy, which can lead to inaccurate implant positioning, impaired postoperative functional performance, and potentially to the surgical failure. The extraordinary advances in medical-imaging techniques and the advent of 3D printing, using basic biocompatible materials, i.e. polymeric/metal filament or powders, makes it possible to manufacture unique, highly complex medical devices. Hence, it is now possible and accessible to design and manufacture patient-specific surgical instrumentation and implant components that are well adapted to the patient's actual morphology. The aim of this study is to provide an in-depth, orthopaedic-focused view of the real potential and applications of 3D printing in the field of personalized surgery.

Advancements and Applications

Innovative solutions for the definition of fully customized medical devices for clinical applications have recently been developed at the Rizzoli Orthopaedic Institute in Bologna, Italy. These can be applied in conservative treatments or surgical procedures, these including minimal and massive skeletal reconstructions. A number of applications have been conducted in preclinical investigations and early clinical cases. Typically, they are based on reliable and accurate anatomical modelling using state-of-the-art medical imaging systems for morphological reconstruction. More specifically, these now include innovative weight-bearing CT based on cone-beam technology and EOS imaging, both with reduced radiation dose compared to standard devices and therefore safer for the patients and in paediatric applications, as well as dual-energy CT and 3.0T MRI supported by machine-learning protocols.

Preclinical applications included in-vitro testing of novel joint prostheses prototypes, investigation of lattice structures for bone growth into implant using cell cultures, sensitivity analysis on anatomical reconstruction from medical-image segmentation, and identification of the most appropriate clinical-oriented 3D printing parameters. The most recent clinical experiences include customized total knee and ankle replacement, personalised femur and tibia osteotomy for correction of varus-valgus deformity of the knee and ankle, massive pelvis reconstruction in oncologic patients, of spine deformity correction, vertebrectomy with subsequent reconstruction, patient-specific tibial intercalary segmental reconstruction, coronoid fracture repair, and many others. In all these applications, starting from CT/MRI-based morphological reconstructions of the patient's anatomy under treatment, the customised procedures involve surgical planning, design and EBM/SLM-based 3D printing, using biocompatible metal alloy powders, of the implantable devices and of the necessary surgical instrumentation. Other experiences have led to 3D printing of bio-models, which have proven to be essential in medical training, for preoperative planning and device preparation. The basic experiences principles can ideally be transferred to other medical branches.

Concluding Remarks

3D printing now seems clearly essential for the customization of orthopaedic treatments. We are confident that this technique will optimize patient-to-doctor communication, final patient performance, and medical training, resulting in cost-effective solutions for health care systems and industries.

Muscle strength assessment is a fundamental outcome measure for evaluating shoulder function in patients with musculoskeletal disorders. Hand-held dynamometers (HHDs) are widely used in clinical practice due to their ease of use, cost-effectiveness, and portability. However, factors such as examiner strength, subject positioning, and HHD placement often compromise their reliability. To address these limitations, recent advancements in orthopedic bioengineering have explored novel measurement devices, including load cells, which provide higher accuracy and improved data consistency.

This study proposed an innovative system that integrates an HHD with a load cell mounted on a rigid, 3D-printed, modular support, designed to reduce operator dependency and enhance measurement precision. By standardizing force measurements and eliminating variability due to manual positioning and tester influence, this configuration aims to improve the reproducibility of muscle strength assessments. Furthermore, the system enables experimental trials in which the load cell and HHD function in a serial configuration, allowing for a direct comparison of their precision and reliability in force measurement.

The system was validated through static weight tests ranging from 9.81 to 98.10 N, based on previous experiments conducted with healthy volunteers and literature studies involving patients with musculoskeletal shoulder disorders. Validation tests using static weights demonstrated good consistency between the two devices, though discrepancies became more pronounced as the weight increased. A feasibility assessment was also conducted with healthy volunteers, who underwent standardized strength evaluations under controlled conditions to assess the system's reliability and validity. Results confirmed the system's capability to measure shoulder muscle strength and facilitate comparisons across different measurement techniques. Specifically, the Bland-Altman analysis indicated a small systematic bias (mean difference: 1.6 N) and narrow Limits of Agreement (LOA: -3.8 N to 7.0 N). The Mean Absolute Error (MAE: 2.9 N) and Mean Absolute Percentage Error (MAPE: 9.4%) further demonstrated acceptable error levels in both dominant and non-dominant arms.

The integration of advanced bioengineering solutions into muscle strength assessment represents a significant step toward enhancing the accuracy, reliability, and standardization of shoulder function evaluation. By minimizing operator dependency and optimizing measurement protocols, this approach offers a robust framework for clinical assessments, rehabilitation monitoring, and postoperative outcome evaluations. Additionally, it offers a more objective and data-driven approach for evaluating musculoskeletal impairments, facilitating early diagnosis and personalized treatment planning. Future large-scale studies are needed to validate the system across diverse populations and clinical environments, ensuring its effectiveness in real-world applications. The proposed system demonstrates strong potential for clinical adoption, particularly in the continuous monitoring and diagnosis of musculoskeletal shoulder disorders, where precise and reproducible strength measurement is critical for guiding therapeutic interventions and optimizing patient outcomes.

Acknowledgement: Funded by the European Union - Next Generation EU - NRRP M6C2 - Investment 2.1 Enhancement and strengthening of biomedical research in the NHS (Project no. PNRR-MAD-2022-12376080 - CUP: F83C22002450001).

Knee osteoarthritis (KOA) is a common degenerative musculoskeletal condition among the elderly, often resulting in chronic pain and reduced mobility. Numerous studies have shown that KOA significantly alters knee biomechanics, leading to changes in movement patterns. Therefore, analyzing lower limb biomechanics is critical for identifying parameters that describe functional limitations and quantifying the effectiveness of therapeutic interventions. To date, accurately characterizing the complex kinematics of the knee joint in patients with KOA remains a challenge. Optoelectronic motion capture systems, currently considered the gold standard, can provide quantitative assessments of 3D kinematics. In particular, gait analysis plays a crucial role in providing a detailed understanding of changes in movement and joint mechanics in subjects with KOA.

Over the past decades, a wide range of biomechanical models have been developed to estimate joint kinematics. Several comparative studies have been conducted on healthy populations to assess the performance of different gait models and marker configurations. These investigations have yielded valuable insights into the reliability and repeatability of kinematic outputs. However, limited attention has been given to evaluating such models in patients with KOA, who often present with knee deformities and soft tissue swelling, which can affect marker positioning and joint center estimation. Consequently, there remains a need to investigate the agreement between different models, specifically in this population, where such anatomical variations can increase uncertainty in kinematic results and impact clinical interpretation.

This study aim to investigate the inter-model agreement between two widely implemented gait models: the anatomically-based Istituto Ortopedico Rizzoli (IOR) model and the cluster-based Calibrated Anatomical System Technique (CAST) model, using both six degrees of freedom (6DoF) and inverse kinematics (IK) computational approaches.

A total of 16 patients was enrolled in this study. Each patient completed six walking trials on a 3-meter walkway at their own pace, with both the IOR and CAST marker sets applied simultaneously. Agreement between models was then quantitatively assessed using intraclass correlation coefficients (ICCs) and mean absolute differences (MD).

Results indicated high consistency across all models in measuring sagittal plane knee dynamics, with ICC values consistently exceeding 0.75 and mean absolute differences below 2.4°, demonstrating reliability during all phases of gait (heel strike, toe-off, loading response, terminal stance, and maximum swing). However, considerable variability emerged in frontal and transverse plane movements. Notably, frontal plane analyses revealed mean absolute differences as large as 12.7° during the maximum swing phase between 6DoF configurations of CAST and IOR models. Comparatively smaller discrepancies (≤5.8°) were noted between IK configurations.

The variations observed between IOR and CAST models, as well as between the 6DoF and IK computational methods in the frontal and transverse planes, highlighted the importance of refinement and validation of gait analysis methodologies. Careful model selection is crucial in clinical and research settings, particularly for populations with complex and variable joint dynamics like individuals with KOA.

Acknowledgment: Funded by the European Union - Next Generation EU-NRRP M6C2-Investment 2.1 Enhancement and strengthening of biomedical research in the NHS (Project no. PNRR-MCNT2-2023-12378237-CUP: F87G24000130006)

INTRODUCTION: Telerehabilitation represents an innovative solution for remotely monitoring and managing physical rehabilitation, enabling the execution and supervision of exercises in home or non-clinical settings. This approach has the potential to enhance the overall quality of rehabilitation care, promoting an inclusive strategy that addresses challenges related to accessibility and the sustainability of healthcare services. Among the various technologies explored, thermal cameras, when integrated with computer vision and artificial intelligence (AI) algorithms, offer significant advantages. They enable non-invasive monitoring and the detection of thermal variations associated with muscle movement and physical activity while also addressing privacy concerns, an essential aspect for home-based applications.
This study aims to evaluate the performance of an integrated system based on AI algorithms and thermal cameras for the automatic recognition of upper limb rehabilitation movements.

METHODS: This work was funded by the European Union - Next Generation EU - PNRR M6C2 - Investment 2.1 Enhancement and Strengthening of Biomedical Research in the National Health Service (Project No. PNRR-MAD-2022-12376080 - CUP: F83C22002450001). A total of 20 healthy volunteers were recruited. Data were acquired using a thermal camera (SEEK THERMAL Compact Pro) connected to an Android smartphone, positioned at a fixed distance from the subjects. The experimental protocol included ten shoulder rehabilitation exercises, each repeated an average of six times in three different scenarios: (1) Flexion/Extension, (2) Abduction/Adduction, (3) Scapular plane elevation, (4) External rotation, (5) External rotation with shoulder at 90° abduction, (6) Military press in standing position, (7) Wall slide in standing position, (8) Towel slide, (9) Forward bow, and (10) Pendulum. After a preprocessing phase, the recorded videos were processed through a pipeline that included frame extraction and the identification of 17 anatomical key points. These data were subsequently used to train (70% of the dataset) and validate (30% of the dataset) different AI models, including both machine learning (ML) and deep learning (DL) ones, for the automatic classification of rehabilitation exercise types.

RESULTS: The proposed computer vision approach demonstrated excellent performance on the test data. The findings show the superior performance of the DL model, achieving 95% accuracy in classifying the different exercise classes and an average Area Under the ROC curve of 0.97. The model was able to autonomously extract both spatial and temporal features from the input data, enabling it to capture more complex patterns and relationships within the data. The classification accuracies for all exercises ranged from 74% to 100%, underscoring the robustness of the model even when subjects performed the exercises in various scenarios.

CONCLUSIONS: The results demonstrate the effectiveness of the model in recognizing shoulder rehabilitation exercises. This study highlights the potential of thermography-based telerehabilitation as an innovative tool for remote monitoring. Such an approach can support both physiotherapists and patients in maintaining rehabilitation programs at home, reducing the need for in-person visits and enabling continuous supervision. Further studies should focus on evaluating the performance of such approach on data collected from patients with musculoskeletal disorders, and incorporating objective assessment of the executed exercises.