Association between sagittal alignment and loads at the adjacent segment in the fused spine: a combined clinical and musculoskeletal modeling study of 205 patients with adult spinal deformity

Fusion surgery; Sagittal alignment; Spine surgeryCirugía de fusión; Alineación sagital; Cirugía de columnaCirurgia de fusió; Alineació sagital; Cirurgia de columnaPurpose Sagittal malalignment is a risk factor for mechanical complications after surgery for adult spinal deformity (ASD). Spinal loads, modulated by sagittal alignment, may explain this relationship. The aims of this study were to investigate the relationships between: (1) postoperative changes in loads at the proximal segment and realignment, and (2) absolute postoperative loads and postoperative alignment measures. Methods A previously validated musculoskeletal model of the whole spine was applied to study a clinical sample of 205 patients with ASD. Based on clinical and radiographic data, pre-and postoperative patient-specific alignments were simulated to predict loads at the proximal segment adjacent to the spinal fusion. Results Weak-to-moderate associations were found between pre-to-postop changes in lumbar lordosis, LL (r =  − 0.23, r =  − 0.43; p < 0.001), global tilt, GT (r = 0.26, r = 0.38; p < 0.001) and the Global Alignment and Proportion score, GAP (r = 0.26, r = 0.37; p < 0.001), and changes in compressive and shear forces at the proximal segment. GAP score parameters, thoracic kyphosis measurements and the slope of upper instrumented vertebra were associated with changes in shear. In patients with T10-pelvis fusion, moderate-to-strong associations were found between postoperative sagittal alignment measures and compressive and shear loads, with GT showing the strongest correlations (r = 0.75, r = 0.73, p < 0.001). Conclusions Spinal loads were estimated for patient-specific full spinal alignment profiles in a large cohort of patients with ASD pre-and postoperatively. Loads on the proximal segments were greater in association with sagittal malalignment and malorientation of proximal vertebra. Future work should explore whether they provide a causative mechanism explaining the associated risk of proximal junction complications.Study funding was provided by Maxi Foundation. Open access funding was provided by Swiss Federal Institute of Technology Zurich

Automatic Calculation of Cervical Spine Parameters Using Deep Learning: Development and Validation on an External Dataset

STUDY DESIGN Retrospective data analysis. OBJECTIVES This study aims to develop a deep learning model for the automatic calculation of some important spine parameters from lateral cervical radiographs. METHODS We collected two datasets from two different institutions. The first dataset of 1498 images was used to train and optimize the model to find the best hyperparameters while the second dataset of 79 images was used as an external validation set to evaluate the robustness and generalizability of our model. The performance of the model was assessed by calculating the median absolute errors between the model prediction and the ground truth for the following parameters: T1 slope, C7 slope, C2-C7 angle, C2-C6 angle, Sagittal Vertical Axis (SVA), C0-C2, Redlund-Johnell distance (RJD), the cranial tilting (CT) and the craniocervical angle (CCA). RESULTS Regarding the angles, we found median errors of 1.66° (SD 2.46°), 1.56° (1.95°), 2.46° (SD 2.55), 1.85° (SD 3.93°), 1.25° (SD 1.83°), .29° (SD .31°) and .67° (SD .77°) for T1 slope, C7 slope, C2-C7, C2-C6, C0-C2, CT, and CCA respectively. As concerns the distances, we found median errors of .55 mm (SD .47 mm) and .47 mm (.62 mm) for SVA and RJD respectively. CONCLUSIONS In this work, we developed a model that was able to accurately predict cervical spine parameters from lateral cervical radiographs. In particular, the performances on the external validation set demonstrate the robustness and the high degree of generalizability of our model on images acquired in a different institution

The influence of gravimetric moisture content on studded shoe–surface interactions in soccer

It is desirable for the studs of a soccer shoe to penetrate the sport surface and provide the player with sufficient traction when accelerating. Mechanical tests are often used to measure the traction of shoe–surface combinations. Mechanical testing offers a repeatable measure of shoe–surface traction, eliminating the inherent uncertainties that exist when human participant testing is employed, and are hence used to directly compare the performance of shoe–surface combinations. However, the influence specific surface characteristics has on traction is often overlooked. Examining the influence of surface characteristics on mechanical test results improves the understanding of the traction mechanisms at the shoe–surface interface. This allows footwear developers to make informed decisions on the design of studded outsoles. The aim of this paper is to understand the effect gravimetric moisture content has on the tribological mechanisms at play during stud–surface interaction. This study investigates the relationships between: the gravimetric moisture content of a natural sand-based soccer surface; surface stiffness measured via a bespoke impact test device; and surface traction measured via a bespoke mechanical test device. Regression analysis revealed that surface stiffness decreases linearly with increased gravimetric moisture content (p = 0.04). Traction was found to initially increase and then decrease with gravimetric moisture content. It was observed that: a surface of low moisture content provides low stud penetration and therefore reduced traction; a surface of high moisture content provides high stud penetration but also reduced traction due to a lubricating effect; and surfaces with moisture content in between the two extremes provide increased traction. In this study a standard commercially available stud was used and other studs may provide slightly different results. The results provide insight into the traction mechanisms at the stud–surface interface which are described in the paper. The variation between traction measurements shows the influence gravimetric moisture content will have on player performance. This highlights the requirement to understand surface conditions prior to making comparative shoe–surface traction studies and the importance of using a studded outsole that is appropriate to the surface condition during play

Are large language models valid tools for patient information on lumbar disc herniation? The spine surgeons' perspective

Introduction: Generative AI is revolutionizing patient education in healthcare, particularly through chatbots that offer personalized, clear medical information. Reliability and accuracy are vital in AI-driven patient education. Research question: How effective are Large Language Models (LLM), such as ChatGPT and Google Bard, in delivering accurate and understandable patient education on lumbar disc herniation? Material and methods: Ten Frequently Asked Questions about lumbar disc herniation were selected from 133 questions and were submitted to three LLMs. Six experienced spine surgeons rated the responses on a scale from “excellent” to “unsatisfactory,” and evaluated the answers for exhaustiveness, clarity, empathy, and length. Statistical analysis involved Fleiss Kappa, Chi-square, and Friedman tests. Results: Out of the responses, 27.2% were excellent, 43.9% satisfactory with minimal clarification, 18.3% satisfactory with moderate clarification, and 10.6% unsatisfactory. There were no significant differences in overall ratings among the LLMs (p = 0.90); however, inter-rater reliability was not achieved, and large differences among raters were detected in the distribution of answer frequencies. Overall, ratings varied among the 10 answers (p = 0.043). The average ratings for exhaustiveness, clarity, empathy, and length were above 3.5/5. Discussion and conclusion: LLMs show potential in patient education for lumbar spine surgery, with generally positive feedback from evaluators. The new EU AI Act, enforcing strict regulation on AI systems, highlights the need for rigorous oversight in medical contexts. In the current study, the variability in evaluations and occasional inaccuracies underline the need for continuous improvement. Future research should involve more advanced models to enhance patient-physician communication

Are large language models valid tools for patient information on lumbar disc herniation? The spine surgeons' perspective

Introduction: Generative AI is revolutionizing patient education in healthcare, particularly through chatbots that offer personalized, clear medical information. Reliability and accuracy are vital in AI-driven patient education. Research question: How effective are Large Language Models (LLM), such as ChatGPT and Google Bard, in delivering accurate and understandable patient education on lumbar disc herniation? Material and methods: Ten Frequently Asked Questions about lumbar disc herniation were selected from 133 questions and were submitted to three LLMs. Six experienced spine surgeons rated the responses on a scale from “excellent” to “unsatisfactory,” and evaluated the answers for exhaustiveness, clarity, empathy, and length. Statistical analysis involved Fleiss Kappa, Chi-square, and Friedman tests. Results: Out of the responses, 27.2% were excellent, 43.9% satisfactory with minimal clarification, 18.3% satisfactory with moderate clarification, and 10.6% unsatisfactory. There were no significant differences in overall ratings among the LLMs (p = 0.90); however, inter-rater reliability was not achieved, and large differences among raters were detected in the distribution of answer frequencies. Overall, ratings varied among the 10 answers (p = 0.043). The average ratings for exhaustiveness, clarity, empathy, and length were above 3.5/5. Discussion and conclusion: LLMs show potential in patient education for lumbar spine surgery, with generally positive feedback from evaluators. The new EU AI Act, enforcing strict regulation on AI systems, highlights the need for rigorous oversight in medical contexts. In the current study, the variability in evaluations and occasional inaccuracies underline the need for continuous improvement. Future research should involve more advanced models to enhance patient-physician communication

Automatic Calculation of Cervical Spine Parameters Using Deep Learning: Development and Validation on an External Dataset

Study design: Retrospective data analysis. Objectives: This study aims to develop a deep learning model for the automatic calculation of some important spine parameters from lateral cervical radiographs. Methods: We collected two datasets from two different institutions. The first dataset of 1498 images was used to train and optimize the model to find the best hyperparameters while the second dataset of 79 images was used as an external validation set to evaluate the robustness and generalizability of our model. The performance of the model was assessed by calculating the median absolute errors between the model prediction and the ground truth for the following parameters: T1 slope, C7 slope, C2-C7 angle, C2-C6 angle, Sagittal Vertical Axis (SVA), C0-C2, Redlund-Johnell distance (RJD), the cranial tilting (CT) and the craniocervical angle (CCA). Results: Regarding the angles, we found median errors of 1.66° (SD 2.46°), 1.56° (1.95°), 2.46° (SD 2.55), 1.85° (SD 3.93°), 1.25° (SD 1.83°), .29° (SD .31°) and .67° (SD .77°) for T1 slope, C7 slope, C2-C7, C2-C6, C0-C2, CT, and CCA respectively. As concerns the distances, we found median errors of .55 mm (SD .47 mm) and .47 mm (.62 mm) for SVA and RJD respectively. Conclusions: In this work, we developed a model that was able to accurately predict cervical spine parameters from lateral cervical radiographs. In particular, the performances on the external validation set demonstrate the robustness and the high degree of generalizability of our model on images acquired in a different institution.ISSN:2192-5690ISSN:2192-568

Association between sagittal alignment and loads at the adjacent segment in the fused spine: a combined clinical and musculoskeletal modeling study of 205 patients with adult spinal deformity

PurposeSagittal malalignment is a risk factor for mechanical complications after surgery for adult spinal deformity (ASD). Spinal loads, modulated by sagittal alignment, may explain this relationship. The aims of this study were to investigate the relationships between: (1) postoperative changes in loads at the proximal segment and realignment, and (2) absolute postoperative loads and postoperative alignment measures. MethodsA previously validated musculoskeletal model of the whole spine was applied to study a clinical sample of 205 patients with ASD. Based on clinical and radiographic data, pre-and postoperative patient-specific alignments were simulated to predict loads at the proximal segment adjacent to the spinal fusion. ResultsWeak-to-moderate associations were found between pre-to-postop changes in lumbar lordosis, LL (r = - 0.23, r = - 0.43; p < 0.001), global tilt, GT (r = 0.26, r = 0.38; p < 0.001) and the Global Alignment and Proportion score, GAP (r = 0.26, r = 0.37; p < 0.001), and changes in compressive and shear forces at the proximal segment. GAP score parameters, thoracic kyphosis measurements and the slope of upper instrumented vertebra were associated with changes in shear. In patients with T10-pelvis fusion, moderate-to-strong associations were found between postoperative sagittal alignment measures and compressive and shear loads, with GT showing the strongest correlations (r = 0.75, r = 0.73, p < 0.001). ConclusionsSpinal loads were estimated for patient-specific full spinal alignment profiles in a large cohort of patients with ASD pre-and postoperatively. Loads on the proximal segments were greater in association with sagittal malalignment and malorientation of proximal vertebra. Future work should explore whether they provide a causative mechanism explaining the associated risk of proximal junction complications.ISSN:0940-6719ISSN:1432-0932ISSN:00199115

Preclinical evaluation of posterior spine stabilization devices: can the current standards represent basic everyday life activities?

Purpose: To discuss whether the available standard methods for preclinical evaluation of posterior spine stabilization devices can represent basic everyday life activities and how to compare the results obtained with different procedures. Methods: A comparative finite element study compared ASTM F1717 and ISO 12189 standards to validated instrumented L2–L4 segments undergoing standing, upper body flexion and extension. The internal loads on the spinal rod and the maximum stress on the implant are analysed. Results: ISO recommended anterior support stiffness and force allow for reproducing bending moments measured in vivo on an instrumented physiological segment during upper body flexion. Despite the significance of ASTM model from an engineering point of view, the overly conservative vertebrectomy model represents an unrealistic worst case scenario. A method is proposed to determine the load to apply on assemblies with different anterior support stiffnesses to guarantee a comparable bending moment and reproduce specific everyday life activities. Conclusions: The study increases our awareness on the use of the current standards to achieve meaningful results easy to compare and interpret