Knee Osteoarthritis (OA) is a highly prevalent condition affecting knee joint that causes loss of physical function and pain. Clinical treatments are mainly focused on pain relief and limitation of disabilities; therefore, it is crucial to find new paradigms assessing cartilage conditions for detecting and monitoring the progression of OA. The goal of this paper is to highlight the predictive power of several features, such as cartilage density, volume and surface. These features were extracted from the 3D reconstruction of knee joint of forty-seven different patients, subdivided into two categories: degenerative and non-degenerative. The most influent parameters for the degeneration of the knee cartilage were determined using two machine learning classification algorithms (logistic regression and support vector machine); later, box plots, which depicted differences between the classes by gender, were presented to analyze several of the key features' trend. This work is part of a strategy that aims to find a new solution to assess cartilage condition based on new-investigated features.

Articular cartilage morphology and composition are essential factors in joint biomechanics, and their alteration is a crucial aspect of osteoarthritis (OA), a prevalent disease that causes pain and functional loss. This research focuses on developing patient-specific synthetic cartilage using innovative Digital Anatomy polymers. The objectives include investigating the morphology, characterizing the mechanical properties, and replicating the architecture of natural cartilage. This approach offers potential alternatives to traditional manufacturing methods and reduces the need for expensive in vivo experiments. Finite Element Analysis (FEA) validates a novel patient-specific measurement setup. It provides insights into the role of morphology in the distribution of stress and strain within cartilage. CAD design is also utilized to create standardized fiber-reinforced samples that mimic the layered micro-architecture of natural cartilage, allowing for the study of their contribution to the overall mechanical properties. The results demonstrate that 3D-printed polymers can effectively replicate the elastic properties of cartilage. The proposed patient-specific simulator produces reliable results, which have been validated through FEM analysis. While the recreated microstructure closely resembles biological cartilage samples, the elastic properties are slightly underestimated. In conclusion, designing an in silico knee joint is a feasible approach that offers numerous advantages for further development. The Young’s modulus values of our synthetic cartilage modules range from $2.43$ MPa to $7.24$ MPa, within the range reported in the literature. Moreover, Young´s modulus at the micro level shows the differences between surface $1.74$ MPa and internal substrate $1.83$ MPa depending on the fiber orientation. Finally, our model proves to be mechanically and morphologically accurate at both the macro and micro levels.

Experimental characterisation of ex vivo specimens is limited by specimen availability and high costs, whereas 3D printing provides a cost-effective alternative for producing multiple replicas. This study aimed to develop a methodology for evaluating the individual and combined effects of material composition and geometry on the biomechanical performance of 3D-printed vertebrae. CT scans of healthy human vertebrae and with lytic metastases were segmented to fabricate synthetic models through Digital Anatomy Printing. Three types of 3D-printed models were produced: Healthy vertebrae, Metastatic vertebrae, and Healed vertebrae (metastatic geometry filled with healthy material). All models were tested under axial compression to measure the strength, stiffness, and strain. Repeatability across replicas was assessed as well as comparison of mechanical properties among the different vertebral types. Results showed excellent repeatability, with coefficients of variation below 5% for strength and stiffness-related parameters. The Metastatic models exhibited significant reductions in strength compared to Healthy ones, while stiffness remained similar, consistent with ex vivo data trends. Healed models highlighted the role of material composition in driving mechanical behaviour, independently of geometry. This work provides the first quantitative assessment of 3D-printed vertebrae with metastatic lesions, supporting their future potential as standardised alternatives to cadaveric testing.

Objective: This study introduces a novel method for the automated detection and quantification of meibomian gland morphology using gray value distribution profiles. The approach addresses limitations in traditional manual and deep learning-based meibography analysis, which are often time-consuming and prone to variability. Methods: This study enrolled 100 volunteers (mean age 40 ± 16 years, range 18–85) who suffered from dry eye and responded to the Ocular Surface Disease Index questionnaire for scoring ocular discomfort symptoms and infrared meibography for capturing imaging of meibomian glands. By leveraging pixel brightness variations, the algorithm provides real-time detection and classification of long, medium, and short meibomian glands, offering a quantitative assessment of gland atrophy. Results: A novel parameter, namely “atrophy index”, a quantitative measure of gland degeneration, is introduced. Atrophy index is the first instrumental measurement to assess single- and multiple-gland morphology. Conclusions: This tool provides a robust, scalable metric for integrating quantitative meibography into clinical practice, making it suitable for real-time screening and advancing the management of dry eyes owing to meibomian gland dysfunction.

Background and Clinical Significance: Sarcopenia, characterized by muscle loss and fat infiltration, poses a significant health burden for aging populations. Quantitative Color 2D and 3D radiodensitometry provides a powerful tool to monitor muscle quality and quantity through CT imaging. This study assessed the impact of a ten-year-long home-bed gym exercise intervention on muscle quality in an elderly subject using CT-derived radiodensitometric analysis. The study involved two comparative analyses: Study A, which compared knee-to-ankle CT scans of the subject between 2013 and 2023; and Study B, which compared the subject’s 2023 thigh CT scan with a cohort of 2500 elderly Icelandic individuals from the AGES-Reykjavik study. Case Presentation: A 70-year-old male began a home-based Full-Body In-Bed Gym exercise program in 2013. Quantitative muscle volume and radiodensity measurements were performed using CT at baseline and after ten years. Results: Study A shows significant improvements in muscle volume observed in the knee-to-ankle region, while a slower decline in radiodensity was noted, indicating substantial preservation of muscle quality despite the expected decay of ten-year aging. For instance, muscle volume increased by 15% in the left Soleus muscle and by 6% in the right Soleus muscle, while the average radiodensity decreased by 12–17 HU. The subject’s thigh muscle quality at 80-years-old is above the AGES-Reykjavik’s cohort average, with reduced fat infiltration. Conclusions: Long-term home Full-Body In-Bed Gym, a low-impact exercise, can mitigate aging sarcopenia, as evidenced by improved tissue radiodensity and muscle mass substantial preservation. This suggests potential applications in personalized healthcare strategies to enhance muscle preservation among aging populations.