Cervical sagittal balance parameters are an important aid in surgical decision-making and influence outcome in cervical spine surgery. In current literature, the normative values of these parameters vary highly within and between patients. This variability might be attributed to body posture. The primary aim is to review the literature on the influence of body posture, including studies that compare sitting or standing positions possibly varying in arm positions, on cervical alignment measured in the sagittal plane on X-rays. A systematic review was performed. PubMed, Embase, Cochrane Library, Web of Science, and CINAHL were systematically searched for articles published up to and including May 2024. The primary outcome was variability in cervical alignment in the sagittal plane as measured in altering body postures on X-rays. Quality of the included articles was assessed with the QUADAS-2 tool. Out of 17,628 screened articles, 8 were eligible for inclusion. Articles were excluded based on irrelevant outcomes (n=10,372), duplicates (n=4,315), wrong study design (n=1,462), or the influence of body posture was not described (n=612). Overall cervical alignment ranged from 33.3° lordosis to 3.5° kyphosis. In all studies, statistically significant differences in T1 Slope (T1S), C2C7 Sagittal Vertical Axis (SVA), McGregor Slope (McGS), or C7 SVA were observed with respect to body posture. The observed differences between postures for cervical alignment ranged from 1° to 16.6°. Body posture significantly influences cervical alignment values measured in the sagittal plane on X-rays. Studies investigating this influence are scarce and vary highly in investigated body postures. Standardization of body posture is imperative to enable optimal comparison of cervical sagittal balance parameters within and between patients, but also between studies. Moreover, the high variability observed raises questions about the comparability of measured values in previously published studies.

Background: Qualitative motion analysis revealed that the cervical spine moves according to a consistent pattern. This analysis calculates the relative rotation between vertebral segments to determine the sequence in which they contribute to extension, demonstrating a mean sensitivity of 90% and specificity of 85%. However, the extensive time that is required limits its applicability. This study investigated the feasibility of implementing a deep-learning model to analyze qualitative cervical motion. Methods: A U-Net architecture was implemented as 2D and 2D+t models. Dice similarity coefficient (DSC) and Intersection over Union (IoU) were used to assess the performance of the models. Intraclass Correlation Coefficient (ICC) was used to compare the relative rotation of individual vertebrae and segments to the ground truth. Results: IoU ranged from 0.37 to 0.74 and DSC ranged from 0.53 to 0.80. The ICC scores for relative rotation ranged from 0.62 to 0.96 for individual vertebrae and from 0.28 to 0.72 for vertebral segments. For segments, 2D+t models presented higher ICC scores compared to 2D models. Conclusions: This study showed the feasibility of implementing deep-learning models to analyze qualitative cervical motion in dynamic X-ray recordings. Future research should focus on improving model segmentation by enhancing recording contrast and applying post-processing methods. Improved segmentation accuracy will enable routine use of the analysis of motion patterns in clinical research. The absence or presence of a motion pattern, or identification of new patterns has the potential to aid in clinical decision-making.

Finger arthroplasty is commonly used to treat pain in the finger joints due to osteoarthritis or rheumatoid arthritis. Despite the procedure having existed for a relatively long time, it is still unknown which characteristics influence implant survival. The Dutch Arthroplasty Registry (LROI) is one of the 4 registries worldwide registering finger arthroplasties. This study aimed to investigate impact factors for implant survival regarding finger joint arthroplasty and assess registration completeness using the national healthcare claims database to compare.

Cervical intervertebral disc height is often used to score cervical degenerative disc disease (CDDD) and can also aid in pre-operative surgical planning. For this purpose, MRI and X-ray imaging are routinely used. However, the reliability and agreement with computed tomography (CT), the gold standard for linear measurements, remain unknown. To assess the reliability and agreement of intervertebral disc height measurements between MRI, X-ray, and CT. Data was collected from all patients that received an MRI, X-ray, and CT within a timeframe of 6 months at Zuyderland Medical Center (ZMC) between 2014 and 2024. Mid, anterior, and posterior cervical intervertebral disc heights were measured from C2-C3 to C6-C7 in all three imaging modalities. 120 patients were included. The intraclass correlation coefficient (ICC) was calculated for the inter- and intraobserver reliability. A linear mixed model with Bonferroni correction was used to compare outcomes. ICCs ranged from moderate to excellent for X-ray. ICCs ranged from good to excellent for MRI and CT after sensitivity analysis for equal slice selection. Mean difference in disc height was .1 mm (p = 0.163) between MRI and X-ray, 1.3 mm (p < 0.001) between MRI and CT, and 1.3 mm (p < 0.001) between X-ray and CT. MRI and X-ray exhibit a systematic bias in the measurement of cervical intervertebral disc height when compared to CT. CT should be considered for the assessment of disc height. The mid-disc measurement is recommended to ensure high reliability.

Background: Qualitative motion analysis revealed that the cervical spine moves according to a consistent pattern. Current data analysis methods are limited by the extensive time required to process the retrieved data. A previous study demonstrated the feasibility of using a deep-learning model to automate analysis methods. However, segmentation accuracy needed to be improved. This study aims to improve segmentation model performance to enable reliable motion analysis. Methods: Four nnU-Net configurations were tested: baseline (A), pre-trained (B), with histogram equalization (C), and pre-trained with histogram equalization (D). Segmentation performance was evaluated using Dice Similarity Coefficient (DSC), Intersection over Union (IoU) and 95th percentile Hausdorff Distance (HD95). Vertebral rotation was estimated using mean shapes. Reliability was assessed using the Intraclass Correlation Coefficient (ICC). Sensitivity analyses were conducted. Results: Across all models, mean DSC ranged from 0.67 to 0.92, mean IoU from 0.55 to 0.85, and mean HD95 from 2.35 to 19.67 mm. After sensitivity analysis for low segmental range of motion (sROM) and low-quality recordings, the mean ICC ranged from 0.617 to 0.837 for model A, from 0.609 to 0.780 for model B, from 0.409 to 0.689 for model C, and from 0.480 to 0.835 for model D. Conclusions: This study shows that Models A and B can accurately analyze cervical motion patterns. High image contrast and an adequate sROM are essential for robust model performance. It also marks an important step toward automated qualitative motion analysis, increasing the accessibility of motion pattern evaluation.

Background: Qualitative motion analysis revealed that the cervical spine moves according to a consistent pattern. This analysis calculates the relative rotation between vertebral segments to determine the sequence in which they contribute to extension, demonstrating high sensitivity and specificity. However, the extensive time required limits its applicability. This study investigated the feasibility of implementing a deep-learning model to analyze qualitative cervical motion. Methods: A U-Net architecture was implemented as 2D and 2D+t models. Dice similarity coefficient (DSC) and Intersection over Union (IoU) were used to assess the performance of the models. Intraclass Correlation Coefficient (ICC) was used to compare the relative rotation of individual vertebrae and segments to the ground truth. Results: IoU ranged from 0.37 to 0.74 and DSC ranged from 0.53 to 0.80. The ICC scores for relative rotation ranged from 0.62 to 0.96 for individual vertebrae and from 0.28 to 0.72 for vertebral segments. For segments, 2D+t models presented higher ICC scores compared to 2D models. Conclusion: This study showed the feasibility of implementing deep-learning models to analyze qualitative cervical motion in dynamic X-ray recordings. Future research should focus on improving model segmentation by enhancing recording contrast and applying post-processing methods.