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Abstract Background This study evaluated the application of a layered cell free poly (L-lactic acid) (PLLA) scaffold to regenerate an infraspinatus tendon defect in a rabbit model. We hypothesized that PLLA scaffold without cultivated cells would lead to regeneration of tissue with mechanical properties similar to reattached infraspinatus without tendon defects. Methods Layered PLLA fabric with a smooth surface on one side and a pile-finished surface on the other side was used. Novel form of layered PLLA scaffold was created by superimposing 2 PLLA fabrics. Defects of the infraspinatus tendon were created in 32 rabbits and the PLLA scaffolds were transplanted, four rabbits were used as normal control. Contralateral infraspinatus tendons were reattached to humeral head without scaffold implantation. Histological and mechanical evaluations were performed at 4, 8, and 16 weeks after operation. Results At 4 weeks postoperatively, cell migration was observed in the interstice of the PLLA fibers. Regenerated tissue was directly connected to the bone composed mainly of type III collagen, at 16 weeks postoperatively. The ultimate failure load increased in a time-dependent manner and no statistical difference was seen between normal infraspinatus tendon and scaffold group at 8 and 16 weeks postoperatively. There were no differences between scaffold group and reattach group at each time of point. The stiffness did not improve significantly in both groups. Conclusions A novel form of layered PLLA scaffold has the potential to induce cell migration into the scaffold and to bridge the tendon defect with mechanical properties similar to reattached infraspinatus tendon model.

Histomorphometry is an important technique in the evaluation of non‐traumatic osteonecrosis of the femoral head (ONFH). Quantification of empty lacunae and pyknotic cells on histological images is the most reliable measure of ONFH pathology, yet it is time and manpower consuming. This study focused on the application of artificial intelligence (AI) technology to tissue image evaluation. The aim of this study is to establish an automated cell counting platform using YOLOv8 as an object detection model on ONFH tissue images and to evaluate and validate its accuracy. From 30 ONFH model rabbits, 270 tissue images were prepared; based on evaluations by three researchers, ground truth labels were created to classify each cell in the image into two classes (osteocytes and empty lacunae) or three classes (osteocytes, pyknotic cells, and empty lacunae). Two and three classes were then annotated on each image. Transfer learning based on annotated data (80% for training and 20% for validation) was performed using YOLOv8n and YOLOv8x with different parameters. To evaluate the detection accuracy of the training model, the mean average precision (mAP (50)) and precision‐recall curve were identified. In addition, the reliability of cell counting by YOLOv8 relative to manual cell counting was evaluated by linear regression analysis using five histological images unused in previous experiments. The mAP (50) for the detection of empty lacunae was 0.868 for the YOLOv8n and 0.883 for the YOLOv8x. The mAP (50) for the three classes was 0.735 for the YOLOv8n model and 0.750 for the YOLOv8x model. The quantification of empty lacunae by automated cell counting obtained in the learning was highly correlated with the manual counting data. The development of an AI‐applied automated cell counting platform will significantly reduce the time and effort of manual cell counting in histological analysis. [ABSTRACT FROM AUTHOR]

This study emphasizes the importance of early detection of osteonecrosis of the femoral head (ONFH) in young patients on long‐term glucocorticoid therapy, including those with acute lymphoblastic leukemia, lupus, and other diagnoses. While X‐ray and magnetic resonance imaging (MRI) are standard imaging methods for staging ONFH, MRI can be costly and time‐consuming. The research focuses on utilizing artificial intelligence (AI) to enhance the evaluation of radiographic images for ONFH detection. The study involved analyzing X‐ray and MRI from 102 control hips and 104 ONFH‐affected hips at Association Research Circulation Osseous (ARCO) Stage II and IIIa. We employed transfer learning with the YOLOv8 model for object detection, using 80% of the data for training and 20% for validation, then assessed detection accuracy through mean average precision (mAP) and a precision‐recall curve. Additionally, AI generated synthetic MRI (sMRI) from X‐ray images using a Generative Adversarial Network (GAN) and evaluated their similarity to original MRI. Results showed that the mAP for ONFH detection was 0.923 for the YOLOv8n model and 0.951 for YOLOv8x. The GAN‐generated sMRI exhibited lower image quality compared with originals but maintained potential for lesion assessment. Intrarater reliability among evaluators was high. The findings indicate that AI techniques, particularly YOLOv8 for object detection and GAN for image generation, can effectively assist in ONFH screening, despite some limitations in the generated MRI quality. [ABSTRACT FROM AUTHOR]

The expression of bone morphogenetic proteins (BMPs) and their cognate receptors (BMPRs) in osteochondromas has not been investigated. We determined the immunohistochemical localization and distribution of BMP-2/4, -6 and -7; BMP receptors BMPR-1A, BMPR-1B and BMPR-2; signal transducing proteins phosphorylated Smad1/5/8; and BMP antagonist noggin in the cartilaginous cap of solitary (SO) and multiple (MO) human osteochondromas and compared these with bovine growth plate and articular cartilage. The distribution and localization patterns for BMP-6, BMP-7, BMPR-1A and BMPR-2 were similar between the cartilaginous cap and the growth plate. BMP-2/4 and BMPR-1B were present throughout the growth plate. However, BMP-2/4 and phosphorylated Smad1/5/8 were mainly detected in proliferating chondrocytes of the cartilaginous cap. Also, BMPR-1B was found in hypertrophic chondrocytes of SO and proliferating chondrocytes of MO. Noggin was observed in resting chondrocytes and, to a lesser extent, in clustered proliferating chondrocytes in SO. On the other hand, noggin in MO was observed in proliferating chondrocytes. Since BMPs can stimulate proliferation and hypertrophic differentiation of chondrocytes, these findings suggest that there is an imbalance of BMP-2/4 and noggin interactions that may lead to abnormal regulation of chondrocyte proliferation and differentiation in the cartilaginous cap of human osteochondromas.

The regeneration of articular cartilage damaged due to trauma and posttraumatic osteoarthritis is an unmet medical need. Current approaches to regeneration and tissue engineering of articular cartilage include the use of chondrocytes, stem cells, scaffolds and signals, including morphogens and growth factors. Stem cells, as a source of cells for articular cartilage regeneration, are a critical factor for articular cartilage regeneration. This is because articular cartilage tissue has a low cell turnover and does not heal spontaneously. Adult stem cells have been isolated from various tissues, such as bone marrow, adipose, synovial tissue, muscle and periosteum. Signals of the transforming growth factor beta superfamily play critical roles in chondrogenesis. However, adult stem cells derived from various tissues tend to differ in their chondrogenic potential. Pluripotent stem cells have unlimited proliferative capacity compared to adult stem cells. Chondrogenesis from embryonic stem (ES) cells has been studied for more than a decade. However, establishment of ES cells requires embryos and leads to ethical issues for clinical applications. Induced pluripotent stem (iPS) cells are generated by cellular reprogramming of adult cells by transcription factors. Although iPS cells have chondrogenic potential, optimization, generation and differentiation toward articular chondrocytes are currently under intense investigation.

Traditionally, angle measurements have been performed using a goniometer, but the complex motion of shoulder movement has made these measurements intricate. The angle of rotation of the shoulder is particularly difficult to measure from an upright position because of the complicated base and moving axes. In this study, we attempted to estimate the shoulder joint internal/external rotation angle using the combination of pose estimation artificial intelligence (AI) and a machine learning model. Videos of the right shoulder of 10 healthy volunteers (10 males, mean age 37.7 years, mean height 168.3 cm, mean weight 72.7 kg, mean BMI 25.6) were recorded and processed into 10,608 images. Parameters were created using the coordinates measured from the posture estimation AI, and these were used to train the machine learning model. The measured values from the smartphone's angle device were used as the true values to create a machine learning model. When measuring the parameters at each angle, we compared the performance of the machine learning model using both linear regression and Light GBM. When the pose estimation AI was trained using linear regression, a correlation coefficient of 0.971 was achieved, with a mean absolute error (MAE) of 5.778. When trained with Light GBM, the correlation coefficient was 0.999 and the MAE was 0.945. This method enables the estimation of internal and external rotation angles from a direct-facing position. This approach is considered to be valuable for analyzing motor movements during sports and rehabilitation. [ABSTRACT FROM AUTHOR]

Background: We aimed to evaluate the efficacy of abductor pollicis longus (APL) suspension arthroplasty with trapeziectomy combined with first and second metacarpal fixation using suture button device. Methods: Thirteen patients (14 thumbs) who underwent APL suspension arthroplasty combined with suture button device for osteoarthritis of the carpometacarpal joint of the thumb (Eaton grade III: 6 and IV: 8 thumbs) at our institution between 2015 and 2019 and were followed up for more than 24 months were included in the study. Preoperative and postoperative range of motion (ROM) of radial and palmar abduction, grip and key pinch strength, visual analog scale (VAS) scores for pain, and the trapezial space ratio (TSR) on radiographic images before and after surgery were statistically examined. Results: The mean age of the patients was 59.6 ± 8.1 years, and the mean follow-up period was 45.4 ± 12.2 months. While grip and pinch strength did not significantly change between preoperative and final follow-up measurements, the ROM of radial and palmar abduction and the VAS score at the latest follow-up were significantly better than the preoperative values (P <.05). The mean TSR immediately after surgery and at the latest follow-up was 0.28 ± 0.089 and 0.22 ± 0.084, respectively, indicating a mean reduction rate of 23.3 ± 15.2%. Conclusions: This surgical technique showed significant improvement in the ROM of radial and palmar abduction and the VAS score for pain, and the combined fixation of the first and second metacarpals with suture button device suppressed the subsidence of the first metacarpal. [ABSTRACT FROM AUTHOR]

Carpal tunnel syndrome (CTS) is caused by subsynovial connective tissue fibrosis, resulting in median nerve (MN) mobility. The standard evaluation method is the measurement of the MN cross-sectional area using static images, and dynamic images are not widely used. In recent years, remarkable progress has been made in the field of deep learning (DL) in medical image processing. The aim of the present study was to evaluate MN dynamics in CTS hands using the YOLOv5 model, which is one of the object detection models of DL. We included 20 normal hands (control group) and 20 CTS hands (CTS group). We obtained ultrasonographic short-axis images of the carpal tunnel and the MN and recorded MN motion during finger flexion–extension, and evaluated MN displacement and velocity. The YOLOv5 model showed a score of 0.953 for precision and 0.956 for recall. The radial–ulnar displacement of the MN was 3.56 mm in the control group and 2.04 mm in the CTS group, and the velocity of the MN was 4.22 mm/s in the control group and 3.14 mm/s in the CTS group. The scores were significantly reduced in the CTS group. This study demonstrates the potential of DL-based dynamic MN analysis as a powerful diagnostic tool for CTS. [ABSTRACT FROM AUTHOR]