Deep learning algorithm for fully automated measurement of sagittal balance in adult spinal deformity.

AIM: Deep learning (DL) algorithms can be used for automated analysis of medical imaging. The aim of this study was to assess the accuracy of an innovative, fully automated DL algorithm for analysis of sagittal balance in adult spinal deformity (ASD). MATERIAL AND METHODS: Sagittal balance (sacral slope, pelvic tilt, pelvic incidence, lumbar lordosis and sagittal vertical axis) was evaluated in 141 preoperative and postoperative radiographs of patients with ASD. The DL, landmark-based measurements, were compared with the ground truth values from validated manual measurements. RESULTS: The DL algorithm showed an excellent consistency with the ground truth measurements. The intra-class correlation coefficient between the DL and ground truth measurements was 0.71-0.99 for preoperative and 0.72-0.96 for postoperative measurements. The DL detection rate was 91.5% and 84% for preoperative and postoperative images, respectively. CONCLUSION: This is the first study evaluating a complete automated DL algorithm for analysis of sagittal balance with high accuracy for all evaluated parameters. The excellent accuracy in the challenging pathology of ASD with long construct instrumentation demonstrates the eligibility and possibility for implementation in clinical routine.

Can artificial intelligence support or even replace physicians in measuring sagittal balance? A validation study on preoperative and postoperative full spine images of 170 patients.

PURPOSE: Sagittal balance (SB) plays an important role in the surgical treatment of spinal disorders. The aim of this research study is to provide a detailed evaluation of a new, fully automated algorithm based on artificial intelligence (AI) for the determination of SB parameters on a large number of patients with and without instrumentation. METHODS: Pre- and postoperative sagittal full body radiographs of 170 patients were measured by two human raters, twice by one rater and by the AI algorithm which determined: pelvic incidence, pelvic tilt, sacral slope, L1-S1 lordosis, T4-T12 thoracic kyphosis (TK) and the spino-sacral angle (SSA). To evaluate the agreement between human raters and AI, the mean error (95% confidence interval (CI)), standard deviation and an intra- and inter-rater reliability was conducted using intra-class correlation (ICC) coefficients. RESULTS: ICC values for the assessment of the intra- (range: 0.88-0.97) and inter-rater (0.86-0.97) reliability of human raters are excellent. The algorithm is able to determine all parameters in 95% of all pre- and in 91% of all postoperative images with excellent ICC values (PreOP-range: 0.83-0.91, PostOP: 0.72-0.89). Mean errors are smallest for the SSA (PreOP: -0.1° (95%-CI: -0.9°-0.6°); PostOP: -0.5° (-1.4°-0.4°)) and largest for TK (7.0° (6.1°-7.8°); 7.1° (6.1°-8.1°)). CONCLUSION: A new, fully automated algorithm that determines SB parameters has excellent reliability and agreement with human raters, particularly on preoperative full spine images. The presented solution will relieve physicians from time-consuming routine work of measuring SB parameters and allow the analysis of large databases efficiently.

Application of a novel spinal posture and motion measurement system in active and static sitting.

The quantification of work-related musculoskeletal risk factors is of great importance; however, only a few tools allow objective, unrestricted measurements of spinal posture and motion in workplaces. This study was performed to evaluate the applicability of the Epionics system in a sedentary workplace. The system is mobile and wireless and assesses lumbar lordosis, pelvic orientation and spinal motion, without restricting subjects in their movements. In total, 10 males were monitored while sitting for 2 h on static and dynamic office chairs and on an exercise ball, to evaluate the effect of dynamic sitting. The volunteers were able to perform their work unhampered. No differences among the tested furniture could be detected with respect to either the lordosis or the number of spinal movements after habituation to the furniture; however, differences in pelvic orientation were statistically significant. The results of the present study indicate that Epionics may be useful for the quantitative assessment of work-related risk factors. Practitioner Summary: Only a few tools allow objective, unrestricted measurements of spinal posture and motion in the workplace. Epionics SPINE measures lumbar lordosis, pelvic orientation and spinal motion under nearly unrestricted conditions and can be used to quantify work-related musculoskeletal risk factors. We demonstrated the use of this tool in the workplace-analysis.

Measurement of the number of lumbar spinal movements in the sagittal plane in a 24-hour period.

PURPOSE: Little is known about the number of spinal movements in the sagittal plane in daily life, mainly due to the lack of adequate techniques to assess these movements. Our aim was to measure these movements in asymptomatic volunteers. METHODS: Two sensor strips based on strain gauge technology (Epionics SPINE system) were fixed on the skin surface of the back parallel to the spine on a total of 208 volunteers without back pain. First, the lordosis angle was determined during relaxed standing. The volunteers were then released to daily life. The increases and decreases in the back lumbar lordosis angle over a period of 24 h were determined and classified into 5° increments. Changes in the lordosis angle greater than 5° were considered. RESULTS: The median number of spinal movements performed within 24 h was approximately 4,400. Of these movements, 66 % were between 5° and 10°. The proportions of higher-magnitude lordosis angle changes were much lower (e.g., 3 % for the 20-25° movement bin). Surprisingly, the median total number of movements was significantly higher (29 %) in women than in men. Large inter-individual differences were observed in the number of movements performed. The volunteers spent a median of 4.9 h with the lumbar spine flexed between 20° and 30° and only 24 min with the spine extended relative to the reference standing position. A median of 50 movements reached or exceeded full-flexion angle and zero movements full-extension angle. CONCLUSIONS: These data illustrate the predominantly small range of movement of the spine during daily activities and the small amount of time spent in extension. These unique data strongly contribute to the understanding of patients' everyday behavior, which might affect the development and testing of spinal implants and the evaluation of surgical and nonsurgical treatments.

Novel AI-Based Algorithm for the Automated Measurement of Cervical Sagittal Balance Parameters. A Validation Study on Pre- and Postoperative Radiographs of 129 Patients.

STUDY DESIGN: Retrospective, mono-centric cohort research study. OBJECTIVES: The analysis of cervical sagittal balance parameters is essential for preoperative planning and dependent on the physician's experience. A fully automated artificial intelligence-based algorithm could contribute to an objective analysis and save time. Therefore, this algorithm should be validated in this study. METHODS: Two surgeons measured C2-C7 lordosis, C1-C7 Sagittal Vertical Axis (SVA), C2-C7-SVA, C7-slope and T1-slope in pre- and postoperative lateral cervical X-rays of 129 patients undergoing anterior cervical surgery. All parameters were measured twice by surgeons and compared to the measurements by the AI algorithm consisting of 4 deep convolutional neural networks. Agreement between raters was quantified, among other metrics, by mean errors and single measure intraclass correlation coefficients for absolute agreement. RESULTS: ICC-values for intra- (range: .92-1.0) and inter-rater (.91-1.0) reliability reflect excellent agreement between human raters. The AI-algorithm could determine all parameters with excellent ICC-values (preop:0.80-1.0; postop:0.86-.99). For a comparison between the AI algorithm and 1 surgeon, mean errors were smallest for C1-C7 SVA (preop: -.3 mm (95% CI:-.6 to -.1 mm), post: .3 mm (.0-.7 mm)) and largest for C2-C7 lordosis (preop:-2.2° (-2.9 to -1.6°), postop: 2.3°(-3.0 to -1.7°)). The automatic measurement was possible in 99% and 98% of pre- and postoperative images for all parameters except T1 slope, which had a detection rate of 48% and 51% in pre- and postoperative images. CONCLUSION: This study validates that an AI-algorithm can reliably measure cervical sagittal balance parameters automatically in patients suffering from degenerative spinal diseases. It may simplify manual measurements and autonomously analyze large-scale datasets. Further studies are required to validate the algorithm on a larger and more diverse patient cohort.

Spine surgeon versus AI algorithm full-length radiographic measurements: a validation study of complex adult spinal deformity patients.

INTRODUCTION: Spinal measurements play an integral role in surgical planning for a variety of spine procedures. Full-length imaging eliminates distortions that can occur with stitched images. However, these images take radiologists significantly longer to read than conventional radiographs. Artificial intelligence (AI) image analysis software that can make such measurements quickly and reliably would be advantageous to surgeons, radiologists, and the entire health system. MATERIALS AND METHODS: Institutional Review Board approval was obtained for this study. Preoperative full-length standing anterior-posterior and lateral radiographs of patients that were previously measured by fellowship-trained spine surgeons at our institution were obtained. The measurements included lumbar lordosis (LL), greatest coronal Cobb angle (GCC), pelvic incidence (PI), coronal balance (CB), and T1-pelvic angle (T1PA). Inter-rater intra-class correlation (ICC) values were calculated based on an overlapping sample of 10 patients measured by surgeons. Full-length standing radiographs of an additional 100 patients were provided for AI software training. The AI algorithm then measured the radiographs and ICC values were calculated. RESULTS: ICC values for inter-rater reliability between surgeons were excellent and calculated to 0.97 for LL (95% CI 0.88-0.99), 0.78 (0.33-0.94) for GCC, 0.86 (0.55-0.96) for PI, 0.99 for CB (0.93-0.99), and 0.95 for T1PA (0.82-0.99). The algorithm computed the five selected parameters with ICC values between 0.70 and 0.94, indicating excellent reliability. Exemplary for the comparison of AI and surgeons, the ICC for LL was 0.88 (95% CI 0.83-0.92) and 0.93 for CB (0.90-0.95). GCC, PI, and T1PA could be determined with ICC values of 0.81 (0.69-0.87), 0.70 (0.60-0.78), and 0.94 (0.91-0.96) respectively. CONCLUSIONS: The AI algorithm presented here demonstrates excellent reliability for most of the parameters and good reliability for PI, with ICC values corresponding to measurements conducted by experienced surgeons. In future, it may facilitate the analysis of large data sets and aid physicians in diagnostics, pre-operative planning, and post-operative quality control.

Parameters influencing the outcome after total disc replacement at the lumbosacral junction. Part 2: distraction and posterior translation lead to clinical failure after a mean follow-up of 5 years.

PURPOSE: The aim of the second part of the study was to investigate the influence of parameters that lead to increased facet joint contact or capsule tensile forces (disc height, lordosis, and sagittal misalignment) on the clinical outcome after total disc replacement (TDR) at the lumbosacral junction. METHODS: A total of 40 patients of a prospective cohort study who received TDR because of degenerative disc disease or osteochondrosis L5/S1 were invited to an additional follow-up for clinical (ODI and VAS for overall, back, and leg pain) and radiographic analysis (a change in disc height, lordosis, or sagittal vertebral misalignment compared with the preoperative state). Based on the final ODI, patients were retrospectively distributed into groups N (normal: <25 %) or F (failure ≥ 25 %) for radiographic parameter comparison. A correlation analysis was performed between the clinical and radiological results. RESULTS: A total of 34 patients were available at a mean follow-up of 59.5 months. Both groups (N = 24; F = 10 patients) presented a significant improvement in overall pain, back pain, and ODI over time. At the final follow-up, higher clinical scores correlated with a larger disc height, increased lordosis, and posterior translation of the superior vertebra, which was also reflected by significant differences in these parameters in the group comparison. CONCLUSIONS: Parameters associated with increased facet joint capsule tensile forces lead to an inferior clinical outcome at mid-term follow-up. When performing TDR, we therefore suggest avoiding iatrogenic posterior translation and overdistraction (and consecutive lordosis).

Accurate determination of hip implant wear, cup anteversion and inclination through AI automated 2D-3D registration.

The precise and accurate measurement of implant wear, acetabular cup anteversion and inclination from routine anterior-posterior radiographs still poses a challenge. Current approaches suffer from time-consuming procedures accompanied by low and observer-dependent accuracy and precision. We present and validate a novel, automated method for determining total hip arthroplasty parameters by comparing its accuracy and precision with methods in contemporary scientific literature. The algorithm uses CAD-model-based two dimensional-three dimensional (2D-3D)-registration supported by convolutional neural networks. Two in-vitro experimental set-ups were designed to validate the proposed 2D-3D-method. The set-ups provided 84 predefined wear values and 24 configurations of anteversion and inclination in 114 radiographs. Accuracy and precision were evaluated by systematically comparing the predefined ground truth and the automatically calculated values from in-vitro X-rays. In addition, an algorithm was developed and validated against physician's measurements on clinical X-rays to determine the inclination of the interteardrop (ITL) and biischial line (BL) to account for the individual patient's pelvic rotation in the frontal plane. Using X-rays from experimental set-ups, the determined mean error was 0.014 mm (standard deviation: 0.020 mm; root-mean-square-error: 0.024 mm) for wear in pelvic position, -0.01° (0.24°; 0.23°) for radiographic cup anteversion, and 0.11° (0.38°; 0.39°) for radiographic cup inclination. The inclination of ITL and BL was automatically determined in all clinical X-rays with excellent interclass correlation coefficients of 0.95 and 0.91, respectively. The presented algorithm allows the accurate and precise evaluation of total hip arthroplasty parameters without additional equipment. The method might help to investigate different implant designs, biomaterials, and surgical techniques with greater objectivity.

Novel AI-Based Algorithm for the Automated Computation of Coronal Parameters in Adolescent Idiopathic Scoliosis Patients: A Validation Study on 100 Preoperative Full Spine X-Rays.

STUDY DESIGN: Retrospective, mono-centric cohort research study. OBJECTIVES: The purpose of this study is to validate a novel artificial intelligence (AI)-based algorithm against human-generated ground truth for radiographic parameters of adolescent idiopathic scoliosis (AIS). METHODS: An AI-algorithm was developed that is capable of detecting anatomical structures of interest (clavicles, cervical, thoracic, lumbar spine and sacrum) and calculate essential radiographic parameters in AP spine X-rays fully automatically. The evaluated parameters included T1-tilt, clavicle angle (CA), coronal balance (CB), lumbar modifier, and Cobb angles in the proximal thoracic (C-PT), thoracic, and thoracolumbar regions. Measurements from 2 experienced physicians on 100 preoperative AP full spine X-rays of AIS patients were used as ground truth and to evaluate inter-rater and intra-rater reliability. The agreement between human raters and AI was compared by means of single measure Intra-class Correlation Coefficients (ICC; absolute agreement; >.75 rated as excellent), mean error and additional statistical metrics. RESULTS: The comparison between human raters resulted in excellent ICC values for intra- (range: .97-1) and inter-rater (.85-.99) reliability. The algorithm was able to determine all parameters in 100% of images with excellent ICC values (.78-.98). Consistently with the human raters, ICC values were typically smallest for C-PT (eg, rater 1A vs AI: .78, mean error: 4.7°) and largest for CB (.96, -.5 mm) as well as CA (.98, .2°). CONCLUSIONS: The AI-algorithm shows excellent reliability and agreement with human raters for coronal parameters in preoperative full spine images. The reliability and speed offered by the AI-algorithm could contribute to the efficient analysis of large datasets (eg, registry studies) and measurements in clinical practice.

Comparison of three validated systems to analyse spinal shape and motion.

The assessment of spinal shape and mobility is of great importance for long-term therapy evaluation. As frequent radiation should be avoided, especially in children, non-invasive measurements have gained increasing importance. Their comparability between each other however stays elusive. Three non-invasive measurement tools have been compared to each other: Idiag M360, raster stereography and Epionics SPINE. 30 volunteers (15 females/15 males) have each been assessed by each system, investigating lumbar lordosis, thoracic kyphosis and spinal range-of-motion in the sagittal plane. Lumbar lordosis differed significantly (p < 0.001) between measurement devices but correlated significant to each other (Pearson's r 0.5-0.6). Regarding thoracic kyphosis no significant difference and a high correlation (r = 0.8) could be shown between Idiag M360 and raster stereography. For lumbar mobility resulting measurements differed significantly and correlated only moderate between Idiag M360 and Epionics SPINE. Although the different measurement systems are moderate to high correlated to each other, their absolute agreement is limited. This might be explained by differences in their angle definition for lordotic and kyphotic angle, their measurement placement, or their capturing of mobility (static vs. dynamic assessment). Therefore, for long-term evaluation of the back profile, inter-modal comparison of values between different non-invasive devices should be avoided.

Automated Artificial Intelligence-Based Assessment of Lower Limb Alignment Validated on Weight-Bearing Pre- and Postoperative Full-Leg Radiographs.

The assessment of the knee alignment using standing weight-bearing full-leg radiographs (FLR) is a standardized method. Determining the load-bearing axis of the leg requires time-consuming manual measurements. The aim of this study is to develop and validate a novel algorithm based on artificial intelligence (AI) for the automated assessment of lower limb alignment. In the first stage, a customized mask-RCNN model was trained to automatically detect and segment anatomical structures and implants in FLR. In the second stage, four region-specific neural network models (adaptations of UNet) were trained to automatically place anatomical landmarks. In the final stage, this information was used to automatically determine five key lower limb alignment angles. For the validation dataset, weight-bearing, antero-posterior FLR were captured preoperatively and 3 months postoperatively. Preoperative images were measured by the operating orthopedic surgeon and an independent physician. Postoperative images were measured by the second rater only. The final validation dataset consisted of 95 preoperative and 105 postoperative FLR. The detection rate for the different angles ranged between 92.4% and 98.9%. Human vs. human inter-(ICCs: 0.85−0.99) and intra-rater (ICCs: 0.95−1.0) reliability analysis achieved significant agreement. The ICC-values of human vs. AI inter-rater reliability analysis ranged between 0.8 and 1.0 preoperatively and between 0.83 and 0.99 postoperatively (all p < 0.001). An independent and external validation of the proposed algorithm on pre- and postoperative FLR, with excellent reliability for human measurements, could be demonstrated. Hence, the algorithm might allow for the objective and time saving analysis of large datasets and support physicians in daily routine.

Novel artificial intelligence algorithm: an accurate and independent measure of spinopelvic parameters.

OBJECTIVE: The analysis of sagittal alignment by measuring spinopelvic parameters has been widely adopted among spine surgeons globally, and sagittal imbalance is a well-documented cause of poor quality of life. These measurements are time-consuming but necessary to make, which creates a growing need for an automated analysis tool that measures spinopelvic parameters with speed, precision, and reproducibility without relying on user input. This study introduces and evaluates an algorithm based on artificial intelligence (AI) that fully automatically measures spinopelvic parameters. METHODS: Two hundred lateral lumbar radiographs (pre- and postoperative images from 100 patients undergoing lumbar fusion) were retrospectively analyzed by board-certified spine surgeons who digitally measured lumbar lordosis, pelvic incidence, pelvic tilt, and sacral slope. The novel AI algorithm was also used to measure the same parameters. To evaluate the agreement between human and AI-automated measurements, the mean error (95% CI, SD) was calculated and interrater reliability was assessed using the 2-way random single-measure intraclass correlation coefficient (ICC). ICC values larger than 0.75 were considered excellent. RESULTS: The AI algorithm determined all parameters in 98% of preoperative and in 95% of postoperative images with excellent ICC values (preoperative range 0.85-0.92, postoperative range 0.81-0.87). The mean errors were smallest for pelvic incidence both pre- and postoperatively (preoperatively -0.5° [95% CI -1.5° to 0.6°] and postoperatively 0.0° [95% CI -1.1° to 1.2°]) and largest preoperatively for sacral slope (-2.2° [95% CI -3.0° to -1.5°]) and postoperatively for lumbar lordosis (3.8° [95% CI 2.5° to 5.0°]). CONCLUSIONS: Advancements in AI translate to the arena of medical imaging analysis. This method of measuring spinopelvic parameters on spine radiographs has excellent reliability comparable to expert human raters. This application allows users to accurately obtain critical spinopelvic measurements automatically, which can be applied to clinical practice. This solution can assist physicians by saving time in routine work and by avoiding error-prone manual measurements.

Impact of material and morphological parameters on the mechanical response of the lumbar spine - A finite element sensitivity study.

Finite element models are frequently used to study lumbar spinal biomechanics. Deterministic models are used to reflect a certain configuration, including the means of geometrical and material properties, while probabilistic models account for the inherent variability in the population. Because model parameters are generally uncertain, their predictive power is frequently questioned. In the present study, we determined the sensitivities of spinal forces and motions to material parameters of intervertebral discs, vertebrae, and ligaments and to lumbar morphology. We performed 1200 model simulations using a generic model of the human lumbar spine loaded under pure moments. Coefficients of determination and of variation were determined for all parameter and response combinations. Material properties of the vertebrae displayed the least impact on results, whereas those of the discs and morphology impacted most. The most affected results were the axial compression forces in the vertebral body and in several ligaments during flexion and the facet-joint forces during extension. Intervertebral rotations were considerably affected only when several parameters were varied simultaneously. Results can be used to decide which model parameters require careful consideration in deterministic models and which parameters might be omitted in probabilistic studies. Findings allow quantitative estimation of a model׳s precision.

Sensitivity analysis of the position of the intervertebral centres of reaction in upright standing--a musculoskeletal model investigation of the lumbar spine.

The loads between adjacent vertebrae can be generalised as a single spatial force acting at the intervertebral centre of reaction. The exact position in vivo is unknown. However, in rigid body musculoskeletal models that simulate upright standing, the position is generally assumed to be located at the discs' centres of rotation. The influence of the antero-posterior position of the centre of reaction on muscle activity and joint loads remains unknown. Thus, by using an inverse dynamic model, we varied the position of the centre of reaction at L4/L5 (i), simultaneously at all lumbar levels (ii), and by optimisation at all lumbar levels (iii). Variation of the centres of reaction can considerably influence the activities of lumbar muscles and the joint forces between vertebrae. The optimisation of the position of the centre of reaction reduced the maximum lumbar muscle activity and axial joint forces at L4/L5 from 17.5% to 1.5% of the muscle strength and from 490 N to 390 N, respectively. Thus, when studying individual postures, such as for therapeutic or preventive evaluations, potential differences between the centre of reaction and the centre of rotation might influence the study results. These differences could be taken into account by sensitivity analyses.

Influence of lumbar spine rhythms and intra-abdominal pressure on spinal loads and trunk muscle forces during upper body inclination.

Improved knowledge on spinal loads and trunk muscle forces may clarify the mechanical causes of various spinal diseases and has the potential to improve the current treatment options. Using an inverse dynamic musculoskeletal model, this sensitivity analysis was aimed to investigate the influence of lumbar spine rhythms and intra-abdominal pressure on the compressive and shear forces in L4-L5 disc and the trunk muscle forces during upper body inclination. Based on in vivo data, three different spine rhythms (SRs) were used along with alternative settings (with/without) of intra-abdominal pressure (IAP). Compressive and shear forces in L4-L5 disc as well as trunk muscle forces were predicted by inverse static simulations from standing upright to 55° of intermediate trunk inclination. Alternate model settings of intra-abdominal pressure and different spine rhythms resulted in significant variation of compression (763 N) and shear forces (195 N) in the L4-L5 disc and in global (454 N) and local (156 N) trunk muscle forces at maximum flexed position. During upper body inclination, the compression forces at L4-L5 disc were mostly released by IAP and increased for larger intervertebral rotation in a lumbar spine rhythm. This study demonstrated that with various possible assumptions of lumbar spine rhythm and intra-abdominal pressure, variation in predicted loads and muscles forces increase with larger flexion. It is therefore, essential to adapt these model parameters for accurate prediction of spinal loads and trunk muscle forces.

Preload substantially influences the intervertebral disc stiffness in loading-unloading cycles of compression.

Disc hydration is controlled by fluid imbibition and exudation and hence by applied load magnitude and history, internal osmotic pressure and disc conditions. It affects both the internal load distribution and external load-bearing of a disc while variations therein give rise to the disc time-dependent characteristics. This study aimed to evaluate the effect of changes in compression preload magnitude on the disc axial cyclic compression stiffness under physiological loading. After 20h of free hydration, effects of various preload magnitudes (no preload, 0.06 and 0.28MPa, applied for eight hours) and disc-bone preparation conditions on disc height and axial stiffness were investigated using 36 disc-bone and 24 isolated disc (without bony endplates) bovine specimens. After preloading, specimens were subjected to ten loading/unloading cycles each of 7.5min compression at 0.5MPa followed by 7.5min at 0.06MPa. Under 0.06MPa preload, the specimen height losses during high loading periods of cyclic loading were greater than corresponding height recoveries during low loading phases. This resulted in a progressive reduction in the specimen height and increase in its stiffness. Differences between disc height losses in high cyclic loads and between stiffness in both load increase and release phases were significant for 0 and 0.06MPa vs. 0.28MPa preload. Results highlight the significant role of disc preload magnitude/history and hence disc height and hydration on disc stiffness in loading/unloading and disc height loss in loading periods. Proper preconditioning and hence hydration level should be achieved if recovery in height loss similar to in vivo conditions is expected.

In vivo loads on a vertebral body replacement during different lifting techniques.

The repeated lifting of heavy weights has been identified as a risk factor for low back pain (LBP). Whether squat lifting leads to lower spinal loads than stoop lifting and whether lifting a weight laterally results in smaller forces than lifting the same weight in front of the body remain matters of debate. Instrumented vertebral body replacements (VBRs) were used to measure the in vivo load in the lumbar spine in three patients at level L1 and in one patient at level L3. Stoop lifting and squat lifting were compared in 17 measuring sessions, in which both techniques were performed a total of 104 times. The trunk inclination and amount of knee bending were simultaneously estimated from recorded images. Compared with the aforementioned lifting tasks, the patients additionally lifted a weight laterally with one hand 26 times. Only a small difference (4%) in the measured resultant force was observed between stoop lifting and squat lifting, although the knee-bending angle (stoop 10°, squat 45°) and trunk inclination (stoop 52°, squat 39°) differed considerably at the time points of maximal resultant forces. Lifting a weight laterally caused 14% less implant force on average than lifting the same weight in front of the body. The current in vivo biomechanical study does not provide evidence that spinal loads differ substantially between stoop and squat lifting. The anterior-posterior position of the lifted weight relative to the spine appears to be crucial for spinal loading.

Differences between clinical "snap-shot" and "real-life" assessments of lumbar spine alignment and motion - What is the "real" lumbar lordosis of a human being?

The individual lumbar lordosis and lumbar motion have been identified to play an important role in pathogenesis of low back pain and are essential references for preoperative planning and postoperative evaluation. The clinical "gold-standard" for measuring lumbar lordosis and its motion are radiological "snap-shots" taken while standing and during upper-body flexion and extension. The extent to which these clinically assessed values characterise lumbar alignment and its motion in daily life merits discussion. A non-invasive measurement-system was employed to measure lumbar lordosis and lumbar motion in 208 volunteers (age: 20-74yrs; ♀/♂: 115/93). For an initial short-term measurement, comparable with the clinical "snap-shot", lumbar lordosis and its motion were assessed while standing and during flexion and extension. Subsequently, volunteers were released to their daily lives while wearing the device, and measurements were performed during the following 24h. The average lumbar lordosis during 24h (8.0°) differed significantly from the standardised measurement while standing (33.3°). Ranges of motion were significantly different throughout the day compared to standing measurements. The influence of the factors age and gender on lordosis and its motion resulted in conflicting results between long- and short-term-measurements. In conclusion, results of short-term examinations differ considerably from the average values during real-life. These findings might be important for surgical planning and increase the awareness of the biomechanical challenges that spinal structures and implants face in real-life. Furthermore, long-term assessments of spinal alignment and motion during daily life can provide valid data on spinal function and can reveal the importance of influential factors.

Estimation of loads on human lumbar spine: A review of in vivo and computational model studies.

Spinal loads are recognized to play a causative role in back disorders and pain. Knowledge of lumbar spinal loads is required in proper management of various spinal disorders, effective risk prevention and assessment in the workplace, sports and rehabilitation, realistic testing of spinal implants as well as adequate loading in in vitro studies. During the last few decades, researchers have used a number of techniques to estimate spinal loads by measuring in vivo changes in the intradiscal pressure, body height, or forces and moments transmitted via instrumented vertebral implants. In parallel, computational models have been employed to estimate muscle forces and spinal loads under various static and dynamic conditions. Noteworthy is the increasing growth in latter computational investigations. This paper aims to review, compare and critically evaluate the existing literature on in vivo measurements and computational model studies of lumbar spinal loads to lay the foundation for future biomechanical studies. Towards this goal, the paper reviews in separate sections models dealing with static postures (standing, sitting, lying) as well as slow and fast dynamic activities (lifting, sudden perturbations and vibrations). The findings are helpful in many areas such as work place safety design and ergonomics, injury prevention, performance enhancement, implant design and rehabilitation management.