Global Sagittal Angle and T9-tilt seem to be the most clinically and functionally relevant global alignment parameters in patients with Adult Spinal Deformity.

Introduction: Radiographic analysis is necessary for the assessment and the surgical planning in adults with spinal deformity (ASD). Restoration of global alignment is key to improving patient's quality of life. However, the large number of existing global alignment parameters can be confusing for surgeons. Research question: To determine the most clinically and functionally relevant global alignment parameters in ASD. Material and methods: ASD and controls underwent full body biplanar X-ray to calculate global alignment parameters: odontoid to hip axis angle (OD-HA), global sagittal angle (GSA), global tilt (GT), SVA, center of auditory meatus to hip axis (CAM-HA), SSA, T1-tilt and T9-tilt. All subjects filled HRQoL questionnaires: ODI, SF-36, VAS for pain and BDI (Beck's Depression Inventory). 3D gait analysis was performed to calculate kinematic and spatio-temporal parameters. A machine learning model predicted gait parameters and HRQoL scores from global alignment parameters. Results: 124 primary ASD and 47 controls were enrolled. T9 tilt predicted the most BDI (31%), hip flexion/extension during gait (36%), and double support time (39%). GSA predicted the most ODI (26%), thorax flexion/extension during gait (33%), and cadence (36%). Discussion and conclusion: Among all global alignment parameters, GSA, evaluating both trunk shift and knee flexion, and T9 tilt, evaluating the shift of the center of mass, were the best predictors for most of HRQoL scores and gait kinematics. Therefore, we recommend using GSA and T9 tilt in clinical practice when evaluating ASD because they represent the most quality of life and functional kinematic of these patients.

Functional assessment using 3D movement analysis can better predict health-related quality of life outcomes in patients with adult spinal deformity: a machine learning approach.

Introduction: Adult spinal deformity (ASD) is classically evaluated by health-related quality of life (HRQoL) questionnaires and static radiographic spino-pelvic and global alignment parameters. Recently, 3D movement analysis (3DMA) was used for functional assessment of ASD to objectively quantify patient's independence during daily life activities. The aim of this study was to determine the role of both static and functional assessments in the prediction of HRQoL outcomes using machine learning methods. Methods: ASD patients and controls underwent full-body biplanar low-dose x-rays with 3D reconstruction of skeletal segment as well as 3DMA of gait and filled HRQoL questionnaires: SF-36 physical and mental components (PCS&MCS), Oswestry Disability Index (ODI), Beck's Depression Inventory (BDI), and visual analog scale (VAS) for pain. A random forest machine learning (ML) model was used to predict HRQoL outcomes based on three simulations: (1) radiographic, (2) kinematic, (3) both radiographic and kinematic parameters. Accuracy of prediction and RMSE of the model were evaluated using 10-fold cross validation in each simulation and compared between simulations. The model was also used to investigate the possibility of predicting HRQoL outcomes in ASD after treatment. Results: In total, 173 primary ASD and 57 controls were enrolled; 30 ASD were followed-up after surgical or medical treatment. The first ML simulation had a median accuracy of 83.4%. The second simulation had a median accuracy of 84.7%. The third simulation had a median accuracy of 87%. Simulations 2 and 3 had comparable accuracies of prediction for all HRQoL outcomes and higher predictions compared to Simulation 1 (i.e., accuracy for PCS = 85 ± 5 vs. 88.4 ± 4 and 89.7% ± 4%, for MCS = 83.7 ± 8.3 vs. 86.3 ± 5.6 and 87.7% ± 6.8% for simulations 1, 2 and 3 resp., p < 0.05). Similar results were reported when the 3 simulations were tested on ASD after treatment. Discussion: This study showed that kinematic parameters can better predict HRQoL outcomes than stand-alone classical radiographic parameters, not only for physical but also for mental scores. Moreover, 3DMA was shown to be a good predictive of HRQoL outcomes for ASD follow-up after medical or surgical treatment. Thus, the assessment of ASD patients should no longer rely on radiographs alone but on movement analysis as well.

Spinopelvic Adaptations in Standing and Sitting Positions in Patients With Adult Spinal Deformity.

Purpose To describe spinopelvic adaptations in the standing and sitting positions in patients with adult spinal deformity (ASD). Methods Ninety-five patients with ASD and 32 controls completed health-related quality of life (HRQOL) questionnaires: short form 36 (SF36), Oswestry Disability Index (ODI), and visual analog scale (VAS) for pain. They underwent biplanar radiography in both standing and sitting positions. Patients with ASD were divided into ASD-front (frontal deformity Cobb > 20°, n = 24), ASD-sag (sagittal vertical axis (SVA) > 50 mm, pelvic tilt (PT) > 25°, or pelvic incidence (PI)-lumbar lordosis (LL) > 10°, n = 40), and ASD-hyper thoracic kyphosis (TK >60°, n = 31) groups. Flexibility was defined as the difference (Δ) in radiographic parameters between the standing and sitting positions. The radiographic parameters were compared between the groups. Correlations between HRQOL scores were evaluated. Results All participants increased their SVA from standing to sitting (ΔSVA<0), except for patients with ASD-sag, who tended to decrease their SVA (78-62 mm) and maximize their pelvic retroversion (27-40° vs 10-34° in controls, p<0.001). They also showed reduced thoracic and lumbar flexibility (ΔLL = 3.4 vs 37.1°; ΔTK = -1.7 vs 9.4° in controls, p<0.001). ASD-hyperTK showed a decreased PT while sitting (28.9 vs 34.4° in controls, p<0.001); they tended to decrease their LL and TK but could not reach values for controls (ΔLL = 22.8 vs 37.1° and ΔTK = 5.2 vs 9.4°, p<0.001). The ASD-front had normal standing and sitting postures. ΔSVA and ΔLL were negatively correlated with the physical component scale (PCS of SF36) and ODI (r = -0.39 and r = -0.46, respectively). Conclusion Patients with ASD present with different spinopelvic postures and adaptations from standing to sitting positions, with those having sagittal malalignment most affected. In addition, changes in standing and sitting postures were related to HRQOL outcomes. Therefore, surgeons should consider patient sitting adaptations in surgical planning and spinal fusion. Future studies on ASD should evaluate whether physical therapy or spinal surgery can improve sitting posture and QOL, especially for those with high SVA or PT.

Evaluation of how users interface with holographic augmented reality surgical scenes: Interactive planning MR-Guided prostate biopsies.

BACKGROUND: User interfaces play a vital role in the planning and execution of an interventional procedure. The objective of this study is to investigate the effect of using different user interfaces for planning transrectal robot-assisted MR-guided prostate biopsy (MRgPBx) in an augmented reality (AR) environment. METHOD: End-user studies were conducted by simulating an MRgPBx system with end- and side-firing modes. The information from the system to the operator was rendered on HoloLens as an output interface. Joystick, mouse/keyboard, and holographic menus were used as input interfaces to the system. RESULTS: The studies indicated that using a joystick improved the interactive capacity and enabled operator to plan MRgPBx in less time. It efficiently captures the operator's commands to manipulate the augmented environment representing the state of MRgPBx system. CONCLUSIONS: The study demonstrates an alternative to conventional input interfaces to interact and manipulate an AR environment within the context of MRgPBx planning.

End-user evaluation of software-generated intervention planning environment for transrectal magnetic resonance-guided prostate biopsies.

BACKGROUND: This study presents user evaluation studies to assess the effect of information rendered by an interventional planning software on the operator's ability to plan transrectal magnetic resonance (MR)-guided prostate biopsies using actuated robotic manipulators. METHODS: An intervention planning software was developed based on the clinical workflow followed for MR-guided transrectal prostate biopsies. The software was designed to interface with a generic virtual manipulator and simulate an intervention environment using 2D and 3D scenes. User studies were conducted with urologists using the developed software to plan virtual biopsies. RESULTS: User studies demonstrated that urologists with prior experience in using 3D software completed the planning less time. 3D scenes were required to control all degrees-of-freedom of the manipulator, while 2D scenes were sufficient for planar motion of the manipulator. CONCLUSIONS: The study provides insights on using 2D versus 3D environment from a urologist's perspective for different operational modes of MR-guided prostate biopsy systems.

Robust active shape models: a robust, generic and simple automatic segmentation tool.

This paper presents a new segmentation algorithm which combines active shape model and robust point matching techniques. It can use any simple feature detector to extract a large number of feature points in the image. Robust point matching is then used to search for the correspondences between feature and model points while the model is being deformed along the modes of variation of the active shape model. Although the algorithm is generic, it is particularly suited for medical imaging applications where prior knowledge is available. The value of the proposed method is examined with two different medical imaging modalities (Ultrasound, MRI) and in both 2D and 3D. The experiments have shown that the proposed algorithm is immune to missing feature points and noise. It has demonstrated significant improvements when compared to RPM-TPS and ASM alone.