Approximating scaffold printability utilizing computational methods.

Bioprinting facilitates the generation of complex, three-dimensional (3D), cell-based constructs for various applications. Although multiple bioprinting technologies have been developed, extrusion-based systems have become the dominant technology due to the diversity of materials (bioinks) that can be utilized, either individually or in combination. However, each bioink has unique material properties and extrusion characteristics that affect bioprinting utility, accuracy, and precision. Here, we have extended our previous work to achieve high precision (i.e. repeatability) and printability across samples by optimizing bioink-specific printing parameters. Specifically, we hypothesized that a fuzzy inference system (FIS) could be used as a computational method to address the imprecision in 3D bioprinting test data and uncover the optimal printing parameters for a specific bioink that result in high accuracy and precision. To test this hypothesis, we have implemented a FIS model consisting of four inputs (bioink concentration, printing flow rate, speed, and temperature) and two outputs to quantify the precision (scaffold bioprinted linewidth variance) and printability. We validate our use of the bioprinting precision index with both standard and normalized printability factors. Finally, we utilize optimized printing parameters to bioprint scaffolds containing up to 30 × 106cells ml-1with high printability and precision. In total, our results indicate that computational methods are a cost-efficient measure to improve the precision and robustness of extrusion 3D bioprinting.

Remote fit wrist braces through artificial intelligence.

INTRODUCTION: Physical boundaries to access skilled orthotist or hand therapy care may be hindered by multiple factors, such as geography, or availability. This study evaluated the accuracy of fitting a prefabricated wrist splint using an app on a smart device. We hypothesize that remote brace fitting by artificial intelligence (AI) can accurately determine the brace size the patient needs without in-person fitting. METHODS: Healthy volunteers were recruited to fit wrist braces. Using 2 standardized calibrated images captured by the smart device, each subject's image was loaded into the machine learning software (AI). Later, hand features were extracted, calibrated, and measured the application, calculated the correct splint size, and compared with the splint chosen by our subjects to improve its own accuracy. As a control (control 1), the subjects independently selected the best brace fit from an array of available splints. Subject selection was recorded and compared with the AI fit splint. As the second method of fitting (control 2), we compared the manufacturer recommended brace size (based on measured wrist circumference and provided sizing chart/insert brochure) with the AI fit splint. RESULTS: A total of 54 volunteers were included. Thirty-two splints predicted by the algorithm matched the exact size chosen by each subject yielding 70% accuracy with a standard deviation of 10% ( p < 0.001). The accuracy increased to 90% with 5% standard deviation if the splints were predicted within the next size category. Fit by manufacturer sizing chart was only 33% in agreement with participant selection. CONCLUSION: Remote brace fitting using AI prediction model may be an acceptable alternative to current standards because it can accurately predict wrist splint size. As more subjects were analyzed, the AI algorithm became more accurate predicting proper brace fit. In addition, AI fit braces are more than twice as accurate as relying on the manufacturer sizing chart.

Contactless Remote 3D Splinting during COVID-19: Report of Two Patients.

We used calibrated 2D images uploaded by patients to an online platform to generate a 3D digital model of the limb. This was used to 3D print a splint. This method of 3D printing of splints was used for two patients who were not able to visit the hospital in person due to restrictions placed by the COVID-19 pandemic. Both patients were satisfied with the splint. We feel that this technology could be used to offer additional options to conventional splinting that allows contactless splint fitting. Level of Evidence: Level V (Therapeutic).

Cubitus Varus Corrective Osteotomy and Graft Fashioning Using Computer Simulated Bone Reconstruction and 3D Printed Custom-Made Cutting Guides.

Preoperative planning is of paramount importance in saving time as well as helping achieve a more precise correction of the deformities. Along with preoperative measurements, customized cutting guides can facilitate intraoperative correction of the deformity with higher confidence. In this report, we are presenting the application of preoperative planning and 3D printed customized cutting guides for correcting cubitus varus alignment of the elbow in an 18 year old male with satisfactory intraoperative and postoperative results.

Safety and Efficacy of Casting during COVID-19 Pandemic: A Comparison of the Mechanical Properties of Polymers Used for 3D Printing to Conventional Materials Used for the Generation of Orthopaedic Orthoses.

To reduce the risk of spread of the novel coronavirus (COVID-19), the emerging protocols are advising for less physician-patient contact, shortening the contact time, and keeping a safe distance. It is recommended that unnecessary casting be avoided in the events that alternative methods can be applied such as in stable ankle fractures, and hindfoot/midfoot/forefoot injuries. Fiberglass casts are suboptimal because they require a follow up for cast removal while a conventional plaster cast is amenable to self-removal by submerging in water and cutting the cotton bandages with scissors. At present, only fiberglass casts are widely available to allow waterproof casting. To reduce the contact time during casting, a custom-made 3D printed casts/splints can be ordered remotely which reduces the number of visits and shortens the contact time while it allows for self-removal by the patient. The cast is printed after the limb is 3D scanned in 5-10 seconds using the commercially available 3D scanners. In contrast to the conventional casting, a 3D printed cast/splint is washable which is an advantage during an infectious crisis such as the COVID-19 pandemic.