Objective: This study introduces a novel deep-learning-based orientation recognition approach for detecting intraoperative lung orientation during robot-assisted anatomical resections, including lobectomy and segmentectomy. This method can potentially aid in anatomical structure identification, facilitate training and education, improve procedural efficiency, and enhance intraoperative imaging navigation. Methods: We developed a unique dataset encompassing various pulmonary procedures, being the first to report on recognition of intraoperative orientation. The TeCNO model, initially developed for laparoscopic cholecystectomies, was adapted for this study. Model performance was evaluated using accuracy, precision, recall, and F1-score, and we explored the influence of dataset composition, intraoperative factors such as 3D model presence, and visual impairments. Results: The model achieved an overall accuracy of 70%, indicating potential in recognizing lung orientation. High performance was achieved in recognizing non-surgical sequences, ‘Fissure’, and ‘Inferior’ views. ‘Posterior’ and ‘Anterior’ views showed inferior performance. Variability in performance was attributed to the heterogeneity of orientation transitions and increased complexity compared to more standardized procedures. The limited dataset size and imbalances in label distribution potentially impacted model performance. Conclusion: This study demonstrates the feasibility of applying phase recognition to detect orientation of the lung and exploring how the unique characteristics of our dataset affect model performance opposed to surgical phase recognition. The results suggest promising applications for intraoperative imaging guidance and automated adjustment of 3D models, particularly for complex orientations like the interlobar ‘Fissure view’. Future research should focus on enhancing model performance and assessing its clinical implementation in diverse surgical settings.

In today’s medicine, 3D planning and PSI are not commonly used in revision TKA surgery and little is known about the accuracy and implementation of these techniques in revision TKA. Therefore, the question arises, what are the potential benefits of 3D planning and PSI in revision surgery? The goal of this thesis is to explore the potential benefits of 3D planning and PSI in revision surgery. To achieve this, a retrospective study and a prospective study are performed, and a first design of a 3D printed PSI is created. For both retrospective and prospective study, 3D plannings are made of patients undergoing revision TKA, comparing the pre-operative planning with the post-operative results based on size prediction, augmentation prediction and component placement analysis. The results of the studies conducted in this research align closely with each other, providing similar results for size and augmentation prediction and component placement analysis. All in all, these studies indicated consistency and reliability of the 3D prediction in different scenarios, affirming the potential of 3D planning in revision TKA surgery. Additionally, the first design for a PSI, a 3D-printed guide, is constructed. This design incorporates several important landmarks for component placement during revision TKA, increasing the outcome of these surgeries, especially if landmarks are missing or unidentifiable during surgery. Together 3D planning and PSI have a bright future in revision surgery, however, future research needs to be conducted to implement 3D planning and PSI in modern-day healthcare.

Background
Head-mounted augmented reality (AR) can be beneficial in orthopedic trauma surgery by projecting 3-dimensional (3D) holograms of preoperative imaging data, drilling or cutting planes, and osteosynthesis material, directly onto the surgeon’s field of view (FOV). For successful implementation of head-mounted AR in orthopedic trauma surgery, the surgeon’s experience is of utmost importance. This prospective clinical study aimed to evaluate the surgeon’s experience with head-mounted AR in orthopedic trauma surgery.
Methods
The Microsoft HoloLens 2 (Microsoft, Redmond, WA, USA) was used to project patient-specific 3D models of the patient’s fracture site as 3D holograms in the surgeon’s FOV during surgery of intra-articular fractures. The surgeon’s experience was recorded using the Simulator Sickness Questionnaire, Borg CR10 Scale, National Aeronautics and Space Administration Task Load Index, System Usability Scale, and a custom-made questionnaire.
Results
A total of 14 ORIF surgeries were performed while using a head-mounted AR device. Overall, surgeons were satisfied with AR HMDs (67.9 ± 22.3 (mean ± SD)) and wanted to use it again (70.0 ± 22.54 (mean ± SD)). Good results were reported regarding the usability of the AR HMD (65.7 ± 16.6 (mean ± SD)) and the interaction with 3D holograms 72.1 ± 15.8 (mean ± SD). However, the mental demand, size, and weight of the AR HMD were rated less favorable.
Conclusion
The surgeon’s experience with the intraoperative use of head-mounted AR in orthopedic trauma surgery is sufficient and head-mounted AR has great future potential in orthopedic trauma surgery. Future research has to focus on an ergonomic design of surgical AR HMDs, technological developments, a simple and fluent workflow, and proving clinical value of head-mounted AR.