There is evidence that specific variants of scapular morphology are associated with dynamic and static posterior shoulder instability. To this date, observations regarding glenoid and/or acromial variants were analyzed independently, with two-dimensional imaging or without comparison with a healthy control group. Therefore, the purpose of this study was to analyze and describe the three-dimensional (3D) shape of the scapula in healthy and in shoulders with static or dynamic posterior instability using 3D surface models and 3D measurement methods. In this study, 30 patients with unidirectional posterior instability and 20 patients with static posterior humeral head subluxation (static posterior instability, Walch B1) were analyzed. Both cohorts were compared with a control group of 40 patients with stable, centered shoulders and without any clinical symptoms. 3D surface models were obtained through segmentation of computed tomography images and 3D measurements were performed for glenoid (version and inclination) and acromion (tilt, coverage, height). Overall, the scapulae of patients with dynamic and static instability differed only marginally among themselves. Compared with the control group, the glenoid was 2.5° (P = .032), respectively, 5.7° (P = .001) more retroverted and 2.9° (P = .025), respectively, 3.7° (P = .014) more downward tilted in dynamic, respectively, static instability. The acromial roof of dynamic instability was significantly higher and on average 6.2° (P = .007) less posterior covering with an increased posterior acromial height of +4.8mm (P = .001). The acromial roof of static instability was on average 4.8° (P = .041) more externally rotated (axial tilt), 7.3° (P = .004) flatter (sagittal tilt), 8.3° (P = .001) less posterior covered with an increased posterior acromial height of +5.8 mm (0.001). The scapula of shoulders with dynamic and static posterior instability is characterized by an increased glenoid retroversion and an acromion that is shorter posterolaterally, higher, and more horizontal in the sagittal plane. All these deviations from the normal scapula values were more pronounced in static posterior instability.

Spinal fusion surgery requires highly accurate implantation of pedicle screw implants, which must be conducted in critical proximity to vital structures with a limited view of the anatomy. Robotic surgery systems have been proposed to improve placement accuracy. Despite remarkable advances, current robotic systems still lack advanced mechanisms for continuous updating of surgical plans during procedures, which hinders attaining higher levels of robotic autonomy. These systems adhere to conventional rigid registration concepts, relying on the alignment of preoperative planning to the intraoperative anatomy. In this paper, we propose a safe deep reinforcement learning (DRL) planning approach (SafeRPlan) for robotic spine surgery that leverages intraoperative observation for continuous path planning of pedicle screw placement. The main contributions of our method are (1) the capability to ensure safe actions by introducing an uncertainty-aware distance-based safety filter; (2) the ability to compensate for incomplete intraoperative anatomical information, by encoding a-priori knowledge of anatomical structures with neural networks pre-trained on pre-operative images; and (3) the capability to generalize over unseen observation noise thanks to the novel domain randomization techniques. Planning quality was assessed by quantitative comparison with the baseline approaches, gold standard (GS) and qualitative evaluation by expert surgeons. In experiments with human model datasets, our approach was capable of achieving over 5% higher safety rates compared to baseline approaches, even under realistic observation noise. To the best of our knowledge, SafeRPlan is the first safety-aware DRL planning approach specifically designed for robotic spine surgery.

Background: In computer-assisted reconstructive surgeries, the contralateral anatomy is established as the best available reconstruction template. However, existing intra-individual bilateral differences or a pathological, contralateral humerus may limit the applicability of the method. The aim of the study was to evaluate whether a statistical shape model (SSM) has the potential to predict accurately the pretraumatic anatomy of the humerus from the posttraumatic condition. Methods: Three-dimensional (3D) triangular surface models were extracted from the computed tomographic data of 100 paired cadaveric humeri without a pathological condition. An SSM was constructed, encoding the characteristic shape variations among the individuals. To predict the patient-specific anatomy of the proximal (or distal) part of the humerus with the SSM, we generated segments of the humerus of predefined length excluding the part to predict. The proximal and distal humeral prediction (p-HP and d-HP) errors, defined as the deviation of the predicted (bone) model from the original (bone) model, were evaluated. For comparison with the state-of-the-art technique, i.e., the contralateral registration method, we used the same segments of the humerus to evaluate whether the SSM or the contralateral anatomy yields a more accurate reconstruction template. Results: The p-HP error (mean and standard deviation, 3.8° ± 1.9°) using 85% of the distal end of the humerus to predict the proximal humeral anatomy was significantly smaller (p = 0.001) compared with the contralateral registration method. The difference between the d-HP error (mean, 5.5° ± 2.9°), using 85% of the proximal part of the humerus to predict the distal humeral anatomy, and the contralateral registration method was not significant (p = 0.61). The restoration of the humeral length was not significantly different between the SSM and the contralateral registration method. Conclusions: SSMs accurately predict the patient-specific anatomy of the proximal and distal aspects of the humerus. The prediction errors of the SSM depend on the size of the healthy part of the humerus. Clinical Relevance: The prediction of the patient-specific anatomy of the humerus is of fundamental importance for computer-assisted reconstructive surgeries.

State-of-the-art research of traditional computer vision is increasingly leveraged in the surgical domain. A particular focus in computer-assisted surgery is to replace marker-based tracking systems for instrument localization with pure image-based 6DoF pose estimation using deep-learning methods. However, state-of-the-art single-view pose estimation methods do not yet meet the accuracy required for surgical navigation. In this context, we investigate the benefits of multi-view setups for highly accurate and occlusion-robust 6DoF pose estimation of surgical instruments and derive recommendations for an ideal camera system that addresses the challenges in the operating room. Our contributions are threefold. First, we present a multi-view RGB-D video dataset of ex-vivo spine surgeries, captured with static and head-mounted cameras and including rich annotations for surgeon, instruments, and patient anatomy. Second, we perform an extensive evaluation of three state-of-the-art single-view and multi-view pose estimation methods, analyzing the impact of camera quantities and positioning, limited real-world data, and static, hybrid, or fully mobile camera setups on the pose accuracy, occlusion robustness, and generalizability. Third, we design a multi-camera system for marker-less surgical instrument tracking, achieving an average position error of 1.01mm and orientation error of 0.89° for a surgical drill, and 2.79mm and 3.33° for a screwdriver under optimal conditions. Our results demonstrate that marker-less tracking of surgical instruments is becoming a feasible alternative to existing marker-based systems.

Opening-wedge osteotomies of the distal radius, performed with three-dimensional printed patient-specific instruments, are a promising technique for accurate correction of malunions. Nevertheless, reports of residual malalignments and discrepancies in the plate and screw position from the planned fixation exist. Consequently, we developed a patient-specific ramp-guide technique, combining navigation of plate positioning, osteotomy cutting, and reduction. The aim of this study is to compare the accuracy of navigation of three-dimensional planned opening-wedge osteotomies, using a ramp-guide, over state-of-the-art guide techniques relying solely on pre-drilled holes. A retrospective analysis was carried out on opening-wedge osteotomies of the distal radius, performed between May 2016 and April 2017, with patient-specific instruments. Eight patients were identified in which a ramp-guide for the distal plate fixation was used. We compared the reduction accuracy with a control group of seven patients, where the reduction was performed with pre-drilled screw holes placed with the patient-specific instruments. The navigation accuracy was assessed by comparing the preoperative plans with the postoperative segmented, computed tomography scans. The accuracy was expressed using a 3D angle and in measurements of all six degrees of freedom (3 translations, 3 rotations), with respect to an anatomical coordinate system. The duration of the surgery of the ramp-guide group was significantly shorter compared to the control group. Significantly less rotational and translational residual malalignment error was observed in the open-wedged osteotomies, where patient-specific instruments with ramp-guides were used. On average, a residual rotational malalignment error of 2.0° (± 2.2°) and a translational malalignment error of 0.6 mm (± 0.2 mm) was observed in the ramp-guide group, as compared to the 4.2° (± 15.0°) and 1.0 mm (± 0.4 mm) error in the control group. The used plate was not significantly positioned more accurately, but significantly fewer screws (15.6%) were misaligned in the distal fragment compared to the control group (51.9%). The use of the presented ramp-guide technique in opening-wedge osteotomies is improving reduction accuracy, screw position, and surgical duration, compared to the existing patient-specific instrument based navigation methods.