Background: Predicting hamstring graft size preoperatively for anterior cruciate ligament (ACL) reconstruction is important for preempting an insufficient diameter in graft size intraoperatively, possibly leading to graft failure. While there are multiple published methods using magnetic resonance imaging (MRI) picture archiving and communication systems (PACS), most are not feasible and practical. Our study aims to (1) practically predict the ACL hamstring graft size in a numerically continuous manner using the preoperative MRI from any native MRI PACS system, (2) determine the degree of correlation between the predicted and actual graft size, and (3) determine the performance of our prediction method if we define an adequate actual graft size as ≥ 8 mm. Methods: A retrospective review of 112 patients who underwent primary ACL reconstruction with quadrupled hamstring semitendinosus-gracilis grafts at a tertiary institution was conducted between January 2018 and December 2018. Graft diameter can be predicted in a numerically continuous manner as √[2*(AB + CD)], where A and B are the semitendinosus cross-sectional length and breath, respectively, and C and D are the gracilis cross-sectional length and breath, respectively. Results: A moderately positive correlation exists between the predicted and actual graft diameter (r = 0.661 and p < .001). Our method yields a high specificity of 92.6% and a moderate sensitivity of 67.2% if we define an adequate actual graft size as ≥ 8 mm. An area under receiver-operating characteristic curve shows good discrimination (AUC = 0.856). Conclusions: We present a practical method to predict the ACL hamstring graft size with high specificity using preoperative MRI measurements.

Nanomaterials hold significant promise for the future of orthopaedic implants due to their ability to mimic the nanoscale components of the bone, such as collagen fibrils and hydroxyapatite. Nanomaterials can regulate cell behaviour while offering mechanical strength and biocompatibility, making them ideal for bone repair and tissue regeneration. This comprehensive review explores the key existing and potential applications of nanotechnology in orthopaedics, including bone tissue engineering, drug delivery systems, systems combatting implant-related infections, and the surface preparation of implants to enhance osseointegration. These innovations are poised to revolutionise orthopaedic care by improving implant durability, reducing infection risks, and promoting bone regeneration to deliver personalised treatment and create better patient outcomes.

Background: Robotic Total Knee Arthroplasty (rTKA) has become increasingly popular. Intraoperative manual planning of femur and tibia implant positions in all degrees of freedom to achieve surgeon-defined targets and limits of bone cuts, gaps, and alignment is challenging. The final manually defined solution may not be optimal, and surgical duration increases significantly. We aim to demonstrate the effectiveness of our novel algorithm in terms of accuracy and surgical duration. Methods: We developed a novel AI computational algorithm to optimize rTKA implant positioning in three-dimensional space. The initial parameters of 3D implant positioning and surgeon-defined target gaps and bone cuts are set. The algorithm determines permutations achieving ideal 3D implant positioning with ± 0.5 mm accuracy, ranking them by surgeon preference and evidence-based criteria. We compared accuracy in achieving surgeon-defined target gaps, intraoperative soft tissue balancing duration, and total surgical time. Results: A prospective study of 67 consecutive rTKA patients at a tertiary institution (Nov 2021–Dec 2023) was conducted. 25 patients (mean age 70.4 ± 7.34 years) had our algorithm used intraoperatively, while 42 (mean age 70.5 ± 6.90 years) did not. 92% of rTKAs using our algorithm achieved target gaps ± 1.5 mm, vs. 52% of non-algorithm rTKAs (P = 0.003). The average difference between surgeon-defined target gaps and final achieved gaps was 1.1 ± 0.5 mm in the algorithm group vs. 1.8 ± 1.0 mm in the non-algorithm group (P = 0.003). Soft tissue balancing duration was significantly shorter: 1.16 min ± 0.11 with algorithm use vs. 14.5 min ± 8.3 (P < 0.0001). Total surgical duration was also significantly lower: 38.4 min ± 14.9 vs. 73.7 min ± 19.6 (P = 0.0002). Conclusion: Our novel AI algorithm significantly improves accuracy in achieving surgeon-defined target extension and flexion gaps while reducing soft tissue balancing and total surgical duration. This is highly promising for achieving both reproducibility and efficiency in rTKAs.

Purpose: This study aims to analyse the correlation between the incidence rate of hand injuries and various major economic indicators in Singapore. We hypothesise that the number of hand injuries is correlated to activity in the construction and manufacturing industries in Singapore. Methods: Twenty thousand seven hundred sixty-four patients who underwent hand surgeries in a tertiary institution between 2012 to 2018 were reviewed. Two independent, blinded observers extracted the frequency of hand surgeries performed from Electronic Medical Records. Economic indicators pertinent to Singapore’s economic activity were collected and smoothed by simple moving average of the prior 3 months. Results were analysed using IBM SPSS v25.0. Results: Significant independent univariate variables were Purchasing-Manager-Index and Industrial-Production-Index. Multiple linear regression of quarterly reported figures showed that Total-Livestock-Slaughtered, Total-Seafood-Handled, Purchasing-Manger-Index, Industrial-Production-Index, Gas-Tariffs, Construction-Index, Consumer-Price-Index, Total-Air-Cargo-Handled, Total-Container-Throughput, Total-Road-Traffic-Accident-Casualties, Food-&-Beverage-Services-Index were significantly correlated (p < 0.05) with hand injuries, with R² = 62.3%. Conclusion: Quarterly economic indicators from major economic industries can be used to predict the incidence of hand injuries with a 62.3% correlation. These findings may be useful for anticipating healthcare resource allocation to treat hand injuries. Type of study and level of evidence: Economic and decision, Level II.