Abstract: The article “Predicting patient satisfaction after anterior cruciate ligament reconstruction based on morphology: a machine learning approach”, written by Anna Ghidotti, Daniele Regazzoni, Caterina Rizzi, Vincenzo Condello, was originally published under exclusive license to Springer Science + Business Media, LLC, part of Springer Nature. As a result of the subsequent decision to publish the article under the open access model, the article's

Keywords: Details about financial support for research, including funding sources and grant numbers as provided in academic publications.

Abstract: Anterior cruciate ligament reconstruction (ACL-R) has a high success rate, but a subset of patients report unsatisfactory outcomes, highlighting the need for improved predictive tools. Recent studies have shown that bony morphology plays an important role in both the risk of ACL injury and post-reconstruction outcomes. This study aims to develop a framework for understanding the relationship between morphology and patient satisfaction. We explore the use of machine learning (ML) models to predict patient satisfaction following ACL reconstruction based on preoperative bone morphology. Using 3D models from high-resolution Magnetic Resonance (MR) imaging, 21 morphological parameters of the femur and tibia were analysed, including linear, surface and volumetric measurements. These were correlated with patient satisfaction as assessed by the Knee injury and Osteoarthritis Outcome Score Global (KOOS Global) questionnaire, administered six months after surgery. To streamline the analysis, automated systems based on statistical shape models and deep learning were employed for landmark placement and segmentation. A dataset of 45 patients was used to train and evaluate three ML models: Random Forest, Logistic Regression and Gradient Boosting. Dimensionality reduction identified 10 key morphological features and Gradient Boosting outperformed the other models, achieving accuracy of 0.89, precision of 0.90 and recall of 0.89. The results demonstrate the potential of combining advanced imaging, automated measurement and ML for personalized prediction of patient satisfaction. The predictive model shows that primarily femoral features, along with intercondylar notch volume and tibial measurements, play a critical role in determining patient satisfaction. This approach may guide surgical planning, patient counselling, intervention strategies and rehabilitation. Although promising, further research with larger datasets, additional parameters and advanced ML techniques is needed to improve model robustness and clinical applicability.

Keywords: 3D modeling | Anterior cruciate ligament | Machine learning | Morphology | Prediction

Abstract: Ankle injuries are common due to the joint's role in weight-bearing and dynamic motion. Finite element method (FEM) has become a valuable tool for understanding joint biomechanics and planning clinical interventions. However, many existing models are limited by static simulations, simplified geometry, or generalised approaches that do not focus specifically on the talus-tibial joint. This study presents a subject-specific FEM framework of the ankle, integrating magnetic resonance imaging (MRI) and gait data from a healthy 49-year-old male. The tibia and talus were reconstructed from MRI and cartilages were modeled using a custom method to compensate for limitations in image resolution. Kinematic data was collected using the Xsens motion capture system and applied to a dynamic FEM developed in Abaqus. The model yielded contact pressures between 1.2 and 21.8 MPa and maximum von Mises stresses up to 5.7 MPa. The stress distribution was predominantly lateral, consistent with known biomechanical patterns. This framework allows realistic simulation of ankle mechanics and provides a basis for assessment of pathological conditions and surgical planning.

Keywords: 3D Modeling | Ankle | Finite Element Analysis | Gait Analysis

Abstract: Background: Anterior cruciate ligament (ACL) reconstruction is a widely performed procedure for ACL injury, but there are several factors which may lead to re-rupture or clinical failure. An intercondylar notch (or fossa) that is narrower may increase the likelihood of injury. Traditional two-dimensional assessments are limited, and three-dimensional (3D) volume analysis may offer more detailed insights. This study employs deep learning and statistical shape modeling (SSM) to enhance 3D modeling of the intercondylar notch, aiming to gain a deeper understanding of this complex 3D anatomical region. Methods: A methodology was developed to generate accurate 3D models of the intercondylar fossa within seconds. The variability of the intercondylar notch in ACL-injured samples was analyzed using SSM techniques, focusing on its principal components. Additionally, gender differences in notch volume were examined using t-tests. Results: The best deep learning method for automatic segmentation of the notch was SegResNet, which achieved a Dice similarity coefficient of over 0.88 and a Hausdorff distance of 0.73 mm. The small volume-related relative error (0.06) illustrates the goodness of the result. Three principal components accounted for 72.59% of the variation, including notch volume, shape, width, and height. Females had statistically significant smaller notch compared with males with ACL injury (P < 0.001).

Keywords: ACL injury | Anatomical variation | Automatic segmentation | Deep learning | Principal component analysis | Statistical shape modelling

Abstract: Background: Hip osteoarthritis (OA) is a degenerative joint disease that affects approximately 25% of individuals over their lifetime, with prevalence expected to rise due to population aging. Gait analysis is recognized as a valuable tool for understanding the biomechanical impact of OA, providing insights into how patients adapt to pain and dysfunction. Objectives: This study aims to investigate gait differences between hip OA patients and healthy controls using markerless systems and machine learning techniques to classify patients based on gait data. Methods: Thirty-six hip OA patients and thirteen healthy controls were analyzed, and machine learning models were trained using gait parameters. Results: The Support Vector Classifier model achieved the highest classification F1-score (96%), demonstrating the potential of combining gait analysis with machine learning to support hip OA diagnosis. Conclusion: The findings of the present study suggest that machine learning applied to gait analysis data could enhance diagnosis and management of OA in clinical practice.

Keywords: Diagnosis | Gait Analysis | Hip Osteoarthritis | Machine Learning | Total Hip Arthroplasty

Abstract: Shoulder pain is a common complaint in clinical practice, often requiring MRI for diagnosis and treatment planning. Accurate segmentation of shoulder bones from MRI scans is crucial for 3D anatomical assessment. Deep learning (DL) models have shown promise in automating segmentation, yet previous studies have primarily focused on the humerus and scapula, with limited investigation of the clavicle. This study compares DL models for automatic segmentation of the humerus, scapula, and clavicle from MRI. We explore multiple MRI sequences, including PDW SPAIR (coronal and sagittal) and PD-TSE, to assess their impact on segmentation accuracy. DL architectures, including 3D U-Net and 3D Attention U-Net, were trained, and their hyperparameters were optimized. Performance was assessed using the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and volume variation. Additionally, morphological measurements were analyzed to quantify segmentation errors. The best segmentation performance was achieved using a combination of coronal and sagittal PDW SPAIR sequences with Attention U-Net, yielding DSC values of 0.878 (clavicle), 0.843 (scapula), and 0.931 (humerus), and minimal HD. Our study highlights the importance of multimodal MRI input and model optimization for shoulder segmentation. The integration of morphological assessments enhances clinical applicability, providing reliable automated segmentation for surgical planning and medical training.

Keywords: Convolutional Neural Network | Deep Learning | Image Segmentation | Magnetic Resonance Imaging | Shoulder

Abstract: The scarcity of medical imaging data poses a significant challenge for the development of automated segmentation models. This study addresses this limitation by focusing on 3D reconstruction of shoulder bones. We evaluated manual and automatic segmentation methods: DL-based using MONAILabel and commercial software (Materialise Mimics), using only 14 CT scans manually labeled for the humerus, scapula, and clavicle. To mitigate data limitations, we applied data augmentation techniques and trained convolutional neural networks optimized for medical imaging, systematically tuning hyperparameters to assess their impact. Segmentation performance was measured using Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). Our DL model achieved DSC scores of 0.96 (humerus), 0.90 (scapula), and 0.82 (clavicle). Fractures were preserved, enabling accurate 3D reconstruction and measurement. The commercial software achieved similar accuracy. This study highlights the feasibility of using DL for accurate shoulder segmentation in data-constrained scenarios and supports the use of open-source tools as viable alternatives in clinical workflows. Future work will focus on expanding the dataset and further refining the CNN models.

Keywords: Computed Tomography | Convolutional Neural Network | Deep Learning | Image Segmentation | Shoulder

Abstract: Surgery for Anterior Cruciate Ligament Reconstruction (ACL-R) is common and generally successful. This study investigates whether specific bony morphologies can predict surgical outcomes and patient life quality post-ACL-R. The study involves eighteen patients who underwent ACL-R, with magnetic resonance images used to reconstruct their distal femur and proximal tibia. 3D models are utilized to measure morphological parameters identified in the literature. Knee health is assessed using the KOOS questionnaire, dividing patients into two groups based on scores (≥60 and <60). Statistical analysis, including correlation matrix, t-tests (or Mann-Whitney tests), and logistic regression, is performed to evaluate the impact of bone morphology on surgical outcomes. The results indicate no significant association between bony morphology and surgical outcomes.

Keywords: 3D Modeling | ACL-R | Anterior Cruciate Ligament | Bony Morphology | KOOS questionnaire

Abstract: The objective of this study was to develop a U-net capable of generating highly accurate 3D models of knee bones, in particular the femur. As part of the approach, a U-net was designed, trained, and validated. In order to achieve these goals, a novel architecture was proposed, including an architecture that reduces encoder parameters and incorporates transfer learning, in order to enhance the attention U-net. Additionally, an extra depth layer was added to extract more salient information. Moreover, the model includes a classifier unit to reduce false positives, as well as a Tversky focal loss function, which is an innovative loss function. The proposed architecture achieved a Dice coefficient of 98.05. By using these enhanced tools, clinicians can visualize and analyze knee structures more accurately, improve surgical intervention effectiveness, and improve patient care quality overall.

Keywords: attention U-net | deep learning | knee bone segmentation

Abstract: A bi-level analysis of computed tomography (CT) images of malignant pleural mesothelioma (MPM) is presented in this paper, starting with a deep learning-based system for classification, followed by a three-dimensional (3D) reconstruction method. MPM is a highly aggressive cancer caused by asbestos exposure, and accurate diagnosis and determination of the tumor's volume are crucial for effective treatment. The proposed system employs a bi-level approach, utilizing machine learning and deep learning techniques to classify CT lung images and subsequently calculate the tumor's volume. The study addresses challenges related to deep neural networks, such as the requirement for large and diverse datasets, hyperparameter optimization, and potential data bias. To evaluate performance, two convolutional neural network (CNN) architectures, Inception-v3 and ResNet-50, were compared in terms of their features and performance. Based on CT images, the second stage incorporates 3D volume reconstruction. The process is carried out by cropping, registering, filtering, and segmenting images. This study demonstrated the efficacy of the developed system by combining CNN optimizations with 3D image reconstruction. It is intended to improve the accuracy of MPM diagnosis and to assist in the determination of chemotherapy doses, both of which may result in improved outcomes for patients.

Keywords: 3D reconstruction | CNN | CT imaging | malignant pleural mesothelioma

Abstract: Segmentation of anatomical components is a major step in creating accurate and realistic 3D models of the human body, which are used in many clinical applications, including orthopedics. Recently, many deep learning approaches have been proposed to solve the problem of manual segmentation, which is time-consuming and operator-dependent. In the present study, SegResNet has been adapted from other domains, such as brain tumors, for knee joints, in particular, to segment the femoral bone from magnetic resonance images. This algorithm has been compared to the well-known U-Net in terms of evaluation metrics, such as the Dice similarity coefficient and Hausdorff distance. In the training phase, various combinations of hyperparameters, such as epochs and learning rates, have been tested to determine which combination produced the most accurate results. Based on their comparable results, both U-Net and SegResNet performed well in accurately segmenting the femur. Dice similarity coefficients of 0.94 and Hausdorff distances less than or equal to 1 mm indicate that both models are effective at capturing anatomical boundaries in the femur. According to the results of this study, SegResNet is a viable option for automating the creation of 3D femur models. In the future, the performance and applicability of SegResNet in real-world settings will be further validated and tested using a variety of datasets and clinical scenarios.

Keywords: 3D modeling knee | automated segmentation | CNN | computer-aided design | deep learning | machine learning for engineering applications | MONAI framework | SegResNet | U-Net

Abstract: Introduction: Clinical gait analysis can be used to evaluate the recovery process of patients undergoing total hip arthroplasty (THA). The postoperative walking patterns of these patients can be significantly influenced by the choice of surgical approach, as each procedure alters distinct anatomical structures. The aim of this study is twofold. The first objective is to develop a gait model to describe the change in ambulation one week after THA. The secondary goal is to describe the differences associated with the surgical approach. Materials and methods: Thirty-six patients undergoing THA with lateral (n = 9), anterior (n = 15), and posterior (n = 12) approaches were included in the study. Walking before and 7 days after surgery was recorded using a markerless motion capture system. Exploratory Factor Analysis (EFA), a data reduction technique, condensed 21 spatiotemporal gait parameters to a smaller set of dominant variables. The EFA-derived gait domains were utilized to study post-surgical gait variations and to compare the post-surgical gait among the three groups. Results: Four distinct gait domains were identified. The most pronounced variation one week after surgery is in the Rhythm (gait cycle time: +32.9%), followed by Postural control (step width: +27.0%), Phases (stance time: +11.0%), and Pace (stride length: − 9.3%). In postsurgical walking, Phases is statistically significantly different in patients operated with the posterior approach compared to lateral (p-value = 0.017) and anterior (p-value = 0.002) approaches. Furthermore, stance time in the posterior approach group is significantly lower than in healthy individuals (p-value < 0.001). Conclusions: This study identified a four-component gait model specific to THA patients. The results showed that patients after THA have longer stride time but shorter stride length, wider base of support, and longer stance time, although the posterior group had a statistically significant shorter stance time than the others. The findings of this research have the potential to simplify the reporting of gait outcomes, reduce redundancy, and inform targeted interventions in regards to specific gait domains.

Keywords: Factor analysis | Gait analysis | Motion capture | Recovery of function | Surgical approach | Total hip replacement

Abstract: Injuries to the anterior cruciate ligament (ACL) are common among active populations. The shape and anatomical variations of the bones are directly related to the function of the ACL, and therefore are considered to be risk factors for injury. This research aims to delineate the key patterns of shape variation in femurs affected by ACL injuries through the application of Statistical Shape Modelling (SSM). SSM is applied to 50 distal femur models and their modes of variations are described. Ten morphologically significant parameters of the distal femur bone are selected and measured on a 3D model of the mean femur, as well as the -3SD and +3SD shapes of the first three principal components (PCs). The first principal component reflects the variation in size of the femur. The second principal component is mainly related to the width of the notch. A variation in the height of the medial posterior condyle is observed in the third principal component. Nevertheless, it is not as significant as with the first two PCs. The study presents a statistical shape model for characterizing the fundamental modes of geometric variation in femoral bones following an ACL injury. Knowing the bony morphological features associated with ACL injuries is crucial to tailoring treatment plans to each individual.

Keywords: ACL Injury | Anatomical Variation | Morphological parameters | Principal Component Analysis | Statistical Shape Modelling

Abstract: Meniscal tears may adversely impair knee function and biomechanics, with effects varying based on tear type and location. There are several methods for studying meniscal injuries, including human cadavers, and computational models, such as Finite Element Analysis (FEA). Previous studies have used the FEA technique to examine the effects of artificially replicating meniscal tears. The aim of this study is to investigate the effect of meniscal injury on knee biomechanics, by means of FEA, starting with real meniscal tears. Based on Magnetic Resonance (MR) images of 12 patients, 3D models of the distal femur, proximal tibia, cartilage, and menisci were obtained for each patient through the segmentation process. A preprocessing phase was then necessary to prepare the models for FEA. Finally, simulations were conducted to study the effects of lesions on stress, contact pressure, and meniscal extrusion. The simulation results showed that meniscal lesions, especially radial tears, increase the pressure between menisci and tibial cartilage, increase the maximum shear stress on menisci, and lead to meniscal extrusion. The integration of FEA with clinical data may provide surgeons with detailed insight, enabling them to decide whether surgical intervention is necessary.

Keywords: ACL Injury | Finite Element Analysis | Knee Biomechanics | Meniscal Tears

Abstract: Total hip arthroplasty (THA) is a surgical procedure advised to treat end-stage osteoarthritis. There are several surgical approaches involving different biomechanical effects, potentially affecting the outcome. A possible consequence of THA is the Trendelenburg gait, which consists of a pelvis drop and trunk lean during walking due to a unilateral weakness of the hip abductors. Gait analysis can be useful in assessing the disorder. The purpose of the present research is twofold: i) to assess the Trendelenburg disease in patients undergoing THA through gait analysis; ii) to investigate the relationship between the disorder and surgical approaches. Patients' gaits were recorded 7 days after THA surgery, using two Microsoft Kinect V2 sensors and virtual skeletons were reconstructed by iPi Soft software. A customized tool was developed to automatically identify walking phases and recognize characteristics compatible with the Trendelenburg gait. In addition to pelvic drop and trunk lean in the frontal plane, kinematic measurements are proposed for a more complete assessment of the Trendelenburg gait. These variables are found to be effective in highlighting the differences between surgical approaches.

Keywords: Gait Analysis | Motion Capture | Recovery of Function | Total Hip Arthroplasty | Trendelenburg Gait

Abstract: Background: The COVID-19 outbreak led to the diffusion of several telemedicine solutions. The choice of the correct platform is crucial for ensuring the release of effective assistance. However, there is a lack of an objective method for the assessment of technical features. Objective: This study proposes a methodology for the evaluation of functional requirements of telemedicine platforms. This approach also permits the comparison of solutions in the Italian market by means of defined parameters, thus directing the choice of health care professionals. Methods: The study is divided into three phases. First, a mapping of the telemedicine platforms operating in Italy is performed. Then, the available platforms are selected based on the offered telemedicine activity. Finally, a method for evaluating the investigated platforms is defined. Results: Thirty-three (n = 33) technological systems were identified through an accurate investigation on the web and interviews with IT companies. Fifteen parameters were defined and organized into three categories: (1) usability of the telemedicine platform, (2) security, and (3) technological and organizational aspects. A score between 1 and 4 was assigned to each parameter, proportionally to the completeness of the platform. In particular, 62.96% of platforms reached an average score between 3.01 and 4 points; 33.33% of them had scores between 2.01 and 3, while the remaining 3.70% of solutions obtained a result between 1.01 and 2. Conclusions: The study provides an evaluation approach that is easily usable by health professionals to select the most suitable platform. The number of solutions and quality of information could be updated to obtain a complete tool.

Keywords: technological assessment | telemedicine platforms | telemedicine solutions

Abstract: This paper introduces an assistive system for diagnosing malignant pleural mesothelioma (MPM), a highly aggressive cancer caused by asbestos exposure. The system assists healthcare providers in accurately determining the tumor's volume and the appropriate dose of chemotherapy to administer. It employs a bi-level process that uses machine learning and deep learning techniques to classify CT images of lungs and then calculate the tumor's volume. The study addresses the challenges associated with deep nets, such as the need for large and diverse datasets, hyperparameter optimization, and potential data bias. Two CNN architectures, Inception-v3 and ResNet-50, were compared in terms of their features and performance, and three hyperparameters were optimized for each model to generate a broad range of training scenarios. To estimate the amount of cancer cells to target, CT images were used to calculate the tumor's volume. This process involved image pre-processing, cropping volume, registering and filtering images, filling holes, segmentation, and 3D reconstruction. The results demonstrate that the developed system utilizing CNN optimizations and reconstruction of 3D images from CT images can benefit the treatment of MPM patients. The system has the potential to improve the accuracy of MPM diagnosis and the effectiveness of chemotherapy, ultimately improving patient outcomes.

Keywords: 3D volume reconstruction | CNN | CT imaging | Malignant Pleural Mesothelioma

Abstract: Nowadays, 10–34% of patients undergone Total Knee Arthroplasty (TKA) continues to have pain and reduced mobility. New technologies in TKA have been developed to reproduce the native anatomy, respecting the natural joint line, by means of customized implants or following the kinematic alignment. In this context, the present research aims at defining a method to create customized implants. In particular, three different situations have been studied. The first condition is the healthy knee, that is used as reference for further analysis. For the second situation, an off-the-shelf prosthesis has been virtually implanted, following the kinematic joint line. In the third solution, a custom-made knee implant has been created. In all the three cases, FEA has been performed to study how load transmission and stability change after TKA. To reach the goal, high resolution Magnetic Resonance (MR) images of a healthy knee have been employed. Three-dimensional models of the knee have been reconstructed through a segmentation process, starting from DICOM images. Hence, the three situations have been studied. Distribution of pressure and stress are comparable in the two solutions, since they both maintain the natural joint line. Improving the kinematic function is crucial to increase patient satisfaction. According to the patient's anatomy, the surgeon can choose between the standard and the personalized prosthesis. The studied customized approach allows to overcome the limits of conventional TKA since it permits to create geometries, which accommodate a variety of anatomical variations.

Keywords: 3D modeling | Customized knee implants | FEA | Kinematic alignment

Abstract: Total knee arthroplasty (TKA) is the surgical procedure of choice for end-stage osteoarthritis. Even if it is widely performed, there is still dissatisfaction with the surgical procedure in about 20-30% of patients. Factors such as malalignment of the implant and the size of the components have been reported as the most common reasons for post-operative complaints. Custom-made prostheses have been introduced to replicate each individual joint geometry and kinematics. In this study, a novel approach is proposed to automate custom-made knee implant design. The aim is to create a pipeline, where customized implants can be designed automatically for a large number of patients, reducing the time for design. Firstly, a manual procedure for designing the implant is defined. The lower part of the femur is extruded with a thickness that matches with the natural anatomy of the patient and cut planes are used to design the customized femoral implant. Then, an automatic procedure is developed. For the purpose, a statistical shape model of femur, based on 33 patients, is created and employed. The procedure is tested with 5 study cases showing the potential of the methodology. Results will be furtherly validated on a larger scale.

Keywords: 3D Modelling | Customization | Human Modelling | Personalized | Statistical Shape Model | Total Knee Arthroplasty

Abstract: Segmentation of anatomical components is a critical step in creating accurate and realistic 3D models of the human body, which are employed in a wide range of clinical applications, particularly in orthopedics. Recently, many deep learning approaches have been proposed to solve the problem of manual segmentation. Among the available software for automatic segmentation, MONAI Label is a free open-source tool, which allows for the creation of annotated datasets and the development of AI-based annotation models for clinical assessment. In this context, the present study is designed to compare the performance of two well-known neural networks in segmenting knee bones. In spite of the fact that several studies have investigated the use of deep learning techniques for knee reconstruction, there is no consensus regarding the most effective method. In the present study, validation metrics are selected in order to assess the accuracy of the automated segmentation models in comparison with the ground truth data. Magnetic resonance images of 31 patients have been employed for the study. As result, U-Net shows better performance than the SegResNet in the automatic femur segmentation task.

Keywords: 3D Modelling Knee | Automated Segmentation | Deep Learning | SegResNet | U-Net

Abstract: Anterior Cruciate Ligament (ACL) is one of the most injured ligaments. Various researches have studied the relationship between ACL rupture and the bony morphology attempting to understand why ACL rupture occurs. The most investigated predisposing factors in the scientific literature are mainly related to the femoral intercondylar notch and the tibial plateau. The aim of the present research is to create a semi-automatic tool for assessing the risk of developing an ACL injury. A package, named ACL Morsky, has been created in 3D Slicer to follow the entire workflow. The process starts with the load of DICOM data. Three-dimensional models are reconstructed by means of segmentation. Then, five parameters, considered risk factors for the ligament rupture, are measured both in 2D images and on 3D models. A specific algorithm compares the measured values with the range in literature to evaluate the probability of developing an ACL injury. The physician is provided with a report for the patient and his/her risk factors. Knowing in advance the risk of rupture can help in reducing it with preventive plans.

Keywords: 3D Slicer | Knee Injury | Knee Risk Assessment | Ligament Risk Factors | Morphological Parameters

Abstract: Since every structure in the human body can vary, customization is important to choose the most appropriate medical option according to the patient. Total knee arthroplasty (TKA) is a surgical procedure for the knee replacement that has a high rate of patient's dissatisfaction. Indeed, conventional prostheses are based on anthropometric data that accommodate common knees. However, mismatch can occur due to anatomical variations among the individuals. Thanks to the advances in imaging techniques and 3D modeling, it is possible to create customized knee implants starting from medical images. In this context, the present research proposes a methodology to design a customized knee implant taking into account clinical (e.g., prosthesis alignment and surgical cuts) and technical parameters (e.g., materials) that have a direct impact on TKA performance and patient's satisfaction. Changing these parameters, different scenarios have been modeled and simulated to understand the most suitable combination. Finite element analysis (FEA) has been employed to simulate and compare the proposed customized models, changing the different clinical and technical parameters. Stress induced by different combinations of the parameters has been evaluated to choose the optimal solution among the eight proposed scenarios. The optimum is reached with a physiological alignment, with six femoral facets and the ultra-high molecular weight polyethylene (UHMWPE) tibial insert. The implant design maintains the natural joint line and allows preserving more bone. The material is the parameter that mostly influences the stress distribution.

Keywords: computer aided design | computer aided engineering | customized knee implant | femoral component optimization | finite element analysis | knowledge engineering | patient-specific knee prosthesis | total knee arthroplasty | virtual prototyping

Abstract: Background: Tele-rehabilitation is gaining importance due to the increasing need for objectiveness in the evaluation of patients with impaired motor functions. Low-cost marker-less motion capture systems are becoming key enabling technologies as support in the treatment of musculoskeletal diseases. Objectives: The goal of this work is to investigate the use of the Microsoft Azure Kinect device to develop a tele-rehabilitation platform for shoulder motor function recovery. The platform comprehends a set of serious games, which are fundamental to increase the patients' engagement in shoulder rehabilitation. Methods: Starting from a set of functionalities identified together with the medical personnel of an Italian hospital, the Azure Kinect device has been used as motion capture system to interact with the serious games. Mobile applications for patients and physicians have been developed to manage the rehabilitation process. Results: The solution has been tested by the involved medical personnel. It has been considered interesting and promising. Further improvements in the design of the virtual environment of the serious games are required. Conclusion: The presented platform is a starting point to develop a complete IT solution for the daily shoulder rehabilitation.

Keywords: Rehabilitation research | Shoulder rehabilitation | Telerehabilitation | Virtual reality exposure therapy

Abstract: Total Knee Arthroplasty (TKA) is a widely performed surgical procedure, which is advised to treat knee osteoarthritis. However, the literature reported that 25% of the patients are unsatisfied by the functional outcomes after the intervention. The main causes seem to be the prosthesis malalignment and the anatomical mismatch between the prosthetic components and the different people anatomy. Even if there exist several commercial solutions, whose aim has always been to provide a reliable prosthesis with high survivorship, the most recent scientific literature is focusing its aim to improving the patients' clinical outcome, kinematics and satisfaction. For this reason, three approaches have been experimented and then discussed with an orthopedic surgeon. Open-source software applications for 3D modelling have been exploited, such as 3D Slicer and Meshmixer. Starting from medical images, through the segmentation process, the 3D model of the knee has been reconstructed. For the first approach, standard off-the-shelf prosthesis have been used for the virtual planning of the intervention. To overcome the limits of this method, two more customized approaches have been experimented. The first one is based on the patient-specific resurfacing prosthesis that fits the patient's anatomy, preserving the femur and maintaining the natural joint line. The third approach allows to create a customized prosthesis, that is a compromise between the two previous methods. Among the three previous procedures, the most suitable one can be chosen according to the patient's anatomy, knee size and articular cartilage damage.

Keywords: 3D modelling | Customized knee prosthesis | Total knee arthroplasty planning

Abstract: Shoulder disorders are very common in the middle-aged population, due to several causes. The traditional diagnosis relies on the knowledge and the experience of the physician but a clinical misinterpretation in this early phase can have serious consequences for the patient's health. The aim of this study is to investigate morphologicalshoulder parameters, as indicators of healthy or pathological conditions. In this way, it is possible to generate a quantitative report, based on measurements. It can be exploited as a medical support tool for physicians to either confirm the diagnosis, or to raise reasonable doubts, as far as the results differed. However, not all the shoulder disorders can be identified through this approach. Magnetic Resonance and Computed Tomography images of pathological shoulders have been employed for the study. The predefined morphological parameters have been measured on 2D medical images as well as from 3D reconstructed virtual model. Critical Shoulder Angle has been identified as the most significant parameter. It is well known that it is affected by Glenoid Inclination, Lateral Acromial Extension and Acromial Height. However, the contribution of each factor is not clear. Hence, a statistical analysis has been performed to understand how its sub-parameters influence it.

Keywords: Critical Shoulder Angle | Morphological Analysis | Patient Assessment | Shoulder Disorders