Full-body Postural Alignment Analysis Through Barycentremetry.

STUDY DESIGN: Multicentric retrospective. OBJECTIVE: The study of center of mass (COM) locations (i.e. barycentremetry) can help us understand postural alignment. This study goal was to determine relationships between COM locations and global postural alignment X-ray parameters in healthy subjects. The second objective was to determine the impact on spinopelvic alignment of increased distance between anterior body envelope and spine at lumbar apex level. SUMMARY OF BACKGROUND DATA: Unexplored relationship between COM location and spinopelvic parameters. METHODS: This study included healthy volunteers with full-body biplanar radiograph including body envelope reconstruction, allowing the estimation of COM location. The following parameters were analyzed: lumbar lordosis (LL), thoracic kyphosis (TK), cervical lordosis (CL), pelvic tilt (PT), Sacro-femoral angle (SFA), Knee flexion angle (KFA), sagittal odontoid-hip axis angle (ODHA). The following COM in the sagittal plane were located: whole body, at thoracolumbar inflexion point, and body segment above TK apex. The body envelope reconstruction also provided the distance between anterior skin and the LL apex vertebral body center ("SV-L distance"). RESULTS: This study included 124 volunteers, with a mean age of 44±19.3. Multivariate analysis confirmed posterior translation of COM above TK apex with increasing LL (P=0.002) through its proximal component, and posterior shift of COM at inflexion point with increasing TK (P=0.008). Increased SV-L distance was associated with greater ODHA (r=0.4) and more anterior body COM (r=0.8), caused by increased TK (r=0.2) and decreased proximal and distal LL (both r=0.3), resulting in an augmentation in SFA (r=0.3) (all P<0.01). CONCLUSIONS: Barycentremetry showed that greater LL was associated with posterior shift of COM above thoracic apex while greater TK was correlated with more posterior COM at inflexion point. Whole-body COM was strongly correlated with ODHA. This study also exhibited significant alignment disruption associated with increased abdominal volume, with compensatory hip extension. LEVEL OF EVIDENCE: II.

Validation of the Total Disability Index (TDI) in French Version.

INTRODUCTION: The Total Disability Index (TDI) questionnaire has been developed to provide a more complete assessment of low back and neck pain, as they frequently co-occur. This study aimed at validating the TDI questionnaire in French, to determine if it could be used in France. HYPOTHESIS: The TDI French version is valid, reproducible and comparable to the English version. METHODS: This multicentric study prospectively included French-speaking volunteers, both patients admitted for spine surgery in two specialized spine centers and healthy individuals. Healthy subjects were recruited among students of an engineering school and medical staff. A booklet was given to the participants containing a Lumbar and Cervical Visual Analog Scale (respectively LVAS and CVAS), and the French versions of Oswestry Disability Index (ODI), Neck Disability Index (NDI) and TDI questionnaires. Statistical analysis included Cronbach's α calculation for internal consistency assessment, correlation analysis with ODI and NDI items for convergent validity, principal component analyses and factor analysis. Discriminant validity was assessed by comparing healthy subjects and patients using Student's t tests, and floor and ceiling effects search. RESULTS: 71 participants were included, with 34 (48%) healthy volunteers and 37 (52%) patients. Mean age was 45.2±19.6 years and 57% of the cohort were males. Internal consistency was good: Cronbach's α was calculated at 0.96 (95%CI: [0.95-0.98]). For each TDI item, a high correlation was found with ODI corresponding items, between 0.81 and 0.97 (p<0.001), and good correlation with NDI items, ranging from 0.65 to 0.96 (p<0.001). TDI correlated also with LVAS and CVAS (respectively 0.70 and 0.65, p<0.001). Principal component analyses indicated good correlation between the TDI items and between each item and TDI total score. Factor analysis indicated two main factors explaining 77% of TDI variance, constituted by all TDI items. Regarding discriminative validity, healthy subjects and patients presented significantly different TDI scores (p-values ≤0.01 for each item). Barplot representations of each TDI item revealed no major floor nor ceiling effects. CONCLUSION: This study confirms the reliability, feasibility and validity of the Total Disability Index questionnaire in its French version. Its validation allows its use in France. LEVEL OF EVIDENCE: II.

Assessment of a novel deep learning-based marker-less motion capture system for gait study.

BACKGROUND: Marker-less systems based on digital video cameras and deep learning for gait analysis could have a deep impact in clinical routine. A recently developed system has shown promising results in terms of joint center position but has not been yet evaluated in terms of gait outcomes. RESEARCH QUESTION: How does this novel marker-less system compare to a marker-based reference system in terms of clinically relevant gait parameters? METHODS: The deep learning method behind the developed marker-less system was trained on a dedicated dataset consisting of forty-one asymptomatic and pathological subjects each performing ten walking trials. The system could estimate the three-dimensional position of seventeen joint centers or keypoints (e.g., neck, shoulders, hip, knee, and ankles). We evaluated the marker-less system against a marker-based system in terms of differences in joint position (Euclidean distance), detection of gait events (e.g., heel strike and toe-off), spatiotemporal parameters (e.g., step length, time), kinematic parameters (e.g., hip and knee extension-flexion), and inter-trial reliability for kinematic parameters. RESULTS: The marker-less system was able to estimate the three-dimensional position of joint centers with a mean difference of 13.1 mm (SD = 10.2 mm). 99% of the estimated gait events were estimated within 10 ms of the corresponding reference values. Estimated spatiotemporal parameters showed zero bias. The mean and standard deviation of the differences of the estimated kinematic parameters varied by parameter (for example, the mean and standard deviation for knee extension flexion angle were -3.0° and 2.7°). Inter-trial reliability of the measured parameters was similar to that of the marker-based references. SIGNIFICANCE: The developed marker-less system can measure the spatiotemporal parameters within the range of the minimum detectable changes obtained using the marker-based reference system. Moreover, except for hip extension flexion, the system showed promising results in terms of several kinematic parameters.

A novel dataset and deep learning-based approach for marker-less motion capture during gait.

BACKGROUND: The deep learning-based human pose estimation methods, which can estimate joint centers position, have achieved promising results on the publicly available human pose datasets (e.g., Human3.6 M). However, these datasets may be less efficient for gait study, particularly for clinical applications, because of the limited number of subjects, their homogeneity (all asymptomatic adults), and the errors introduced by marker placement on subjects' regular clothing. RESEARCH QUESTION: How a new human pose dataset, adapted for gait study, could contribute to the advancement and evaluation of marker-less motion capture systems? METHODS: A marker-less system, based on deep learning-based pose estimation methods, was proposed. A new dataset (ENSAM dataset) was collected. Twenty-two asymptomatic adults, one adult with scoliosis, one adult with spondylolisthesis, and seven children with bone disease performed ten walking trials, while being recorded both by the proposed marker-less system and a reference system - combining a marker-based motion capture system and a medical imaging system (EOS). The dataset was split into training and test sets. The pose estimation method, already trained on the Human3.6 M dataset, was evaluated on the ENSAM test set, then reevaluated after further training on the ENSAM training set. The joints coordinates were evaluated, using Bland-Altman bias and 95 % confidence interval, and joint position error (the Euclidean distance between the estimated joint centers and the corresponding reference values). RESULTS: The Bland-Altman 95 % confidence intervals were substantially improved after finetuning the pose estimation method on the ENSAM training set (e.g., from 106.9 mm to 17.4 mm for the hip joint). With the new dataset and approach, the mean joint position error varied from 6.2 mm for ankles to 21.1 mm for shoulders. SIGNIFICANCE: The proposed marker-less system achieved promising results in terms of joint position errors. Future studies are necessary to assess the system in terms of gait parameters.