There are numerous orthopaedic applications of three-dimensional (3D) printing for the pelvis and acetabulum. The authors reviewed recently published articles and summarized their experience. 3D printed anatomical models are particularly useful in pelvic and acetabular fracture surgery for planning, implant templating and for anatomical assessment of pathologies such as CAM-type femoroacetabular impingement and rare deformities. Custom-made metal 3D printed patient-specific implants and instruments are increasingly being studied for pelvic oncologic resection and reconstruction of resected defects as well as for revision hip arthroplasties with favourable results. This article also discusses cost-effectiveness considerations when preparing pelvic 3D printed models from a hospital 3D printing centre.

Aims: The use of 3D printing has become increasingly popular and has been widely used in orthopaedic surgery. There has been a trend towards an increasing number of publications in this field, but existing literature incorporates limited high-quality studies, and there is a lack of reports on outcomes. The aim of this study was to perform a scoping review with Level I evidence on the application and effectiveness of 3D printing. Methods: A literature search was performed in PubMed, Embase, and Web of Science databases. The keywords used for the search criteria were ((3d print*) OR (rapid prototyp*) OR (additive manufactur*)) AND (orthopaedic). The inclusion criteria were: 1) use of 3D printing in orthopaedics, 2) randomized controlled trials, and 3) studies with participants/patients. Risk of bias was assessed with Cochrane Collaboration Tool and PEDro Score. Pooled analysis was performed. Results: Overall, 21 studies were included in our study with a pooled total of 932 participants. Pooled analysis showed that operating time (p < 0.001), blood loss (p < 0.001), fluoroscopy times (p < 0.001), bone union time (p < 0.001), pain (p = 0.040), accuracy (p < 0.001), and functional scores (p < 0.001) were significantly improved with 3D printing compared to the control group. There were no significant differences in complications. Conclusion: 3D printing is a rapidly developing field in orthopaedics. Our findings show that 3D printing is advantageous in terms of operating time, blood loss, fluoroscopy times, bone union time, pain, accuracy, and function. The use of 3D printing did not increase the risk of complications. Cite this article: Bone Joint Res 2021;10(12):807–819.

Fracture-related infection (FRI) is a devastating complication in orthopedic surgery. A recent study showed that FRI causes more severe infection and further delays healing in osteoporotic bone. Moreover, bacterial biofilm formed on implants cannot be eradicated by systemic antibiotics, warranting novel treatments. Here, we developed a DNase I and Vancomycin hydrogel delivery vehicle to eradicate Methicillin-resistant Staphylococcus aureus (MRSA) infection in vivo. Vancomycin was encapsulated in liposomes, and DNase I and Vancomycin/liposomal-Vancomycin was loaded on thermosensitive hydrogel. In vitro drug release test showed a burst release of DNase I (77.2%) within 72 h and sustained release of Vancomycin (82.6%) up to day 14. The in vivo efficacy was evaluated in a clinically relevant ovariectomy (OVX) induced osteoporotic metaphyseal fracture model with MRSA infection, and a total of 120 Sprague Dawley rats were used. In the OVX with infection group, biofilm development caused a drastic inflammatory response, trabecular bone destruction, and non-union. In the DNase I and Vancomycin co-delivery hydrogel group (OVX-Inf-DVG), bacteria on bone and implant were eradicated. X-ray and micro-CT showed preservation of trabecular bone and bone union. HE staining showed the absence of inflammatory necrosis, and fracture healing was restored. The local elevation of TNF-α and IL-6 and increased number of osteoclasts were prevented in the OVX-Inf-DVG group. Our findings suggest that dual release of DNase I and Vancomycin initially followed by Vancomycin only later up to 14 days effectively eliminates MRSA infection, prevents biofilm development and provides a sterile environment to promote fracture healing in osteoporotic bone with FRI. STATEMENT OF SIGNIFICANCE: The biofilm on implants are difficult to eradicate, causing recurrent infection and non-union in fracture-related infection (FRI). Here we developed a hydrogel therapy with high in vivo efficacy to eliminate MRSA biofilm infection in a clinically-relevant FRI model in osteoporotic bone. By loading DNase I and vancomycin/liposomal-vancomycin on thermosensitive poly-(DL-lactic acidco-glycolic acid) (PLGA)-polyethylene glycol (PEG)-PLGA hydrogel, a dual release of DNase I and Vancomycin was achieved whilst preserving enzyme activity. In this model, the progressive development of infection caused a drastic inflammatory response, osteoclastogenesis, trabecular bone destruction, and non-union of fracture. These pathological changes were successfully prevented by the dual delivery of DNase I and vancomycin. Our findings provide a promising strategy for FRI in osteoporotic bone.

Loosening is the leading cause of total knee arthroplasty (TKA) revision. This is a heavy burden toward the healthcare system owing to the difficulty in diagnosis and complications occurring from the delay management. Based on automatic analytical model building, machine learning, may potentially help to automatically recognize the risk of loosening based on radiographs alone. The aim of this study was to build an image-based machine-learning model for detecting TKA loosening. Image-based machine-learning model was developed based on ImageNet, Xception model and a TKA patient X-ray image dataset. Based on a dataset with TKA patient clinical parameters, another system was then created for developing the clinical-information-based machine learning model with random forest classifier. In addition, the Xception Model was pre-trained on the ImageNet database with python and TensorFlow deep learning library for the prediction of loosening. Class activation maps were also used to interpret the prediction decision made by model. Two senior orthopaedic specialists were invited to assess loosening from X-ray images for 3 attempts in setting up comparison benchmark. In the image-based machine learning loosening model, the precision rate and recall rate were 0.92 and 0.96, respectively. While for the accuracy rate, 96.3% for visualization classification was observed. However, the addition of clinical-information-based model, with precision rate of 0.71 and recall rate of 0.20, did not further showed improvement on the accuracy. Moreover, as class activation maps showed corresponding signals over bone-implant interface that is loosened radiographically, this confirms that the current model utilized a similar image recognition pattern as that of inspection by clinical specialists. The image-based machine learning model developed demonstrated high accuracy and predictability of knee arthroplasty loosening. And the class activation heatmap matched well with the radiographic features used clinically to detect loosening, which highlighting its potential role in assisting clinicians in their daily practice. However, addition of clinical-information-based machine-learning model did not offer further improvement in detection. As far as we know, this is the first report of pure image-based machine learning model with high detection accuracy. Importantly, this is also the first model to show relevant class activation heatmap corresponding to loosening location. The finding in this study indicated image-based machine learning model can detect knee arthroplasty loosening with high accuracy and predictability, which the class activation heatmap can potentially assist surgeons to identify the sites of loosening.

To analyze the effectiveness of exercise interventions on falls and fall-related fracture prevention among community-dwelling elderlies. Literature search was conducted in Pubmed and Embase. Keywords used for literature search were “fracture” AND “fall” AND “exercise”. Randomized controlled trials involving community-dwelling elderlies older than 60 years old with physical exercises as intervention were included. A systematic review and meta-analysis was performed. The primary outcomes were falls and fractures. Twelve studies were included and 4784 participants were involved with a mean age of 75.4. The most common exercise interventions were strength and balance exercises. The results of meta-analysis of 11 studies showed that exercise intervention had beneficial effect on fall prevention (RR = 0.71, 95% CI, 0.62–0.82; I2 = 24%, p < 0.0001). The effect was better when exercise intervention applied to women participants (RR = 0.64, 95% CI, 0.49–0.83; I2 = 28%, p = 0.00009) compared to men and women participants (RR = 0.75, 95% CI, 0.64–0.89; I2 = 24%, p = 0.001). The results of meta-analysis of seven studies showed that physical exercise had significant effect on fracture prevention (RR = 0.54, 95% CI, 0.35–0.83; I2 = 25%, p = 0.005). However, the effect was significant when exercise intervention applied to women participants only (RR = 0.37, 95% CI, 0.20–0.67; I2 = 0%, p = 0.001) but not significant when exercise intervention applied to both genders (RR = 0.80, 95% CI, 0.58–1.09; I2 = 0%, p = 0.15). Exercise interventions, especially the combination of strength and balance training, were effective in preventing falls. Resistance exercises and jumping exercises were effective for fracture prevention among community-dwelling older population. The effectiveness of exercise interventions on fracture prevention have more significant effect on women. Further studies are needed to test the effectiveness of exercise interventions in men. The use of effective exercises or biophysical interventions including vibration therapy can be incorporated into Fracture Liaison Services to prevent future fall and fracture.

Scoliosis, characterized by spine deformity, is most common in adolescent idiopathic scoliosis (AIS). Manual Cobb angle measurement limitations underscore the need for automated tools. This study employed a vertebral landmark extraction method and Feedforward Neural Network (FNN) to predict scoliosis progression in 79 AIS patients. The novel intervertebral angles matrix format showcased results. The mean absolute error for the intervertebral angle progression was 1.5 degrees, while the Pearson correlation of the predicted Cobb angles was 0.86. The accuracy in classifying Cobb angles (45°) was 0.85, with 0.65 sensitivity and 0.91 specificity. The FNN demonstrated superior accuracy, sensitivity, and specificity, aiding in tailored treatments for potential scoliosis progression. Addressing FNNs’ over-fitting issue through strategies like “dropout” or regularization could further enhance their performance. This study presents a promising step towards automated scoliosis diagnosis and prognosis.