Second Metatarsal Length and Transfer Ulcers After First Metatarsal Amputation in Diabetic Foot Infections.

BACKGROUND: Plantar transfer ulcers (TUs) underneath the second metatarsal head are frequent after first metatarsal ray amputations due to diabetic foot infections. Whether the second metatarsal length (2ML) is associated with TU occurrence in these patients is unclear. This study evaluated whether 2ML is associated with TU occurrence after first-ray amputations and whether ulcer-free survival is shorter in patients with "excess" 2ML. METHODS: Forty-two patients with a mean age of 67 (range 33-93) years, diabetes, and first metatarsal ray amputation (first amputation at the affected foot) were included. Two independent readers measured the 2ML using the Coughlin method. A protrusion of more than 4.0 mm of the second metatarsal was defined as "excess" 2ML. The effect of 2ML on ulcer occurrence was analyzed using a multivariate Cox regression model. A Kaplan-Meier curve for TU-free survival was constructed comparing the 2 groups of "normal" (n = 21) and "excess" 2ML (n = 21). RESULTS: Interrater reliability was excellent. TUs underneath the second metatarsal occurred in 15 (36%) patients. In agreement with our hypothesis, 2ML was nonsignificantly different in patients with TUs, recording a mean of 5.3 (SD 2.5) mm, compared to patients without 4.0 (SD 2.3) mm (hazard ratio [HR] 1.12, 95% CI 0.89-1.41), whereas insulin dependence was associated with ulcer occurrence (HR 0.33, 95% CI 0.11-0.99). CONCLUSION: In our relatively small study population with a cutoff level of 4 mm for excess 2ML, ulcer-free survival was similar in patients with "normal" and "excess" 2ML. LEVEL OF EVIDENCE: Level III, retrospective comparative study.

Reducing residual forces in spinal fusion using a custom-built rod bending machine.

BACKGROUND AND OBJECTIVE: As part of spinal fusion surgery, shaping the rod implant to align with the anatomy is a tedious, error-prone, and time-consuming manual process. Inadequately contoured rod implants introduce stress on the screw-bone interface of the pedicle screws, potentially leading to screw loosening or even pull-out. METHODS: We propose the first fully automated solution to the rod bending problem by leveraging the advantages of augmented reality and robotics. Augmented reality not only enables the surgeons to intraoperatively digitize the screw positions but also provides a human-computer interface to the wirelessly integrated custom-built rod bending machine. Furthermore, we introduce custom-built test rigs to quantify per screw absolute tensile/compressive residual forces on the screw-bone interface. Besides residual forces, we have evaluated the required bending times and reducer engagements, and compared our method to the freehand gold standard. RESULTS: We achieved a significant reduction of the average absolute residual forces from for the freehand gold standard to (p=0.0015) using the bending machine. Moreover, our bending machine reduced the average time to instrumentation per screw from to . Reducer engagements per rod were significantly decreased from an average of 1.00±1.14 to 0.11±0.32 (p=0.0037). CONCLUSION: The combination of augmented reality and robotics has the potential to improve surgical outcomes while minimizing the dependency on individual surgeon skill and dexterity.

Automatic registration with continuous pose updates for marker-less surgical navigation in spine surgery.

Established surgical navigation systems for pedicle screw placement have been proven to be accurate, but still reveal limitations in registration or surgical guidance. Registration of preoperative data to the intraoperative anatomy remains a time-consuming, error-prone task that includes exposure to harmful radiation. Surgical guidance through conventional displays has well-known drawbacks, as information cannot be presented in-situ and from the surgeon's perspective. Consequently, radiation-free and more automatic registration methods with subsequent surgeon-centric navigation feedback are desirable. In this work, we present a marker-less approach that automatically solves the registration problem for lumbar spinal fusion surgery in a radiation-free manner. A deep neural network was trained to segment the lumbar spine and simultaneously predict its orientation, yielding an initial pose for preoperative models, which then is refined for each vertebra individually and updated in real-time with GPU acceleration while handling surgeon occlusions. An intuitive surgical guidance is provided thanks to the integration into an augmented reality based navigation system. The registration method was verified on a public dataset with a median of 100% successful registrations, a median target registration error of 2.7 mm, a median screw trajectory error of 1.6°and a median screw entry point error of 2.3 mm. Additionally, the whole pipeline was validated in an ex-vivo surgery, yielding a 100% screw accuracy and a median target registration error of 1.0 mm. Our results meet clinical demands and emphasize the potential of RGB-D data for fully automatic registration approaches in combination with augmented reality guidance.