Fat beyond muscle: Assessing epimuscular fat of the lumbar spine and its association with vertebral level, demographics, BMI, and low back pain.

Introduction: Epimuscular fat (EF) has rarely been studied in the context of low back pain (LBP).

Research Question: This study aims to assess the presence and extent of EF in the lumbar muscles and its association with vertebral level in patients with low back disorders and to explore correlations between EF, demographics, BMI, and LBP.

Material And Methods: T2 axial MRIs from L1 to L5 were manually segmented to analyze the cross-sectional area (CSA) of EF (mm), and fat infiltration (FI,%) of 40 patients (23 females, 17 males; mean age:65.

3D external shape analysis and barycentremetry can provide early signs of progression in adolescent idiopathic scoliosis.

Purpose: Our objective was to analysis the barycentremetry, obtained from the external envelope reconstruction of biplanar radiographs, in adolescent idiopathic scoliosis (AIS) and to determine whether assessing would help predict the distinction between progressive and stable AIS at the early stage.

Methods: A retrospective study with a multicentre cohort of 205 AIS was conducted. All AIS underwent a biplanar X-ray between 2013 and 2020.

Methodological considerations in calculating the minimal clinically important change score for the core outcome measures index (COMI): insights from a large single-centre spine surgery registry.

Introduction: The Minimal Clinically Important Change (MCIC) is used in conjunction with Patient-Reported Outcome Measures (PROMs) to determine the clinical relevance of changes in health status. MCIC measures a change within the same person or group over time. This study aims to evaluate the variability in computing MCIC for the Core Outcome Measure Index (COMI) using different methods.

Sex Differences in Patient-rated Outcomes After Lumbar Spinal Fusion for Degenerative Disease: A Multicenter Cohort Study.

Study Design: Heterogeneous data collection via a mix of prospective, retrospective, and ambispective methods.

Objective: To evaluate the effect of biological sex on patient-reported outcomes after spinal fusion surgery for lumbar degenerative disease.

Summary Of Background Data: Current literature suggests sex differences regarding clinical outcome after spine surgery may exist.

Estimating lumbar bone mineral density from conventional MRI and radiographs with deep learning in spine patients.

Purpose: This study aimed to develop machine learning methods to estimate bone mineral density and detect osteopenia/osteoporosis from conventional lumbar MRI (T1-weighted and T2-weighted images) and planar radiography in combination with clinical data and imaging parameters of the acquisition protocol.

Methods: A database of 429 patients subjected to lumbar MRI, radiographs and dual-energy x-ray absorptiometry within 6 months was created from an institutional database. Several machine learning models were trained and tested (373 patients for training, 86 for testing) with the following objectives: (1) direct estimation of the vertebral bone mineral density; (2) classification of T-score lower than - 1 or (3) lower than - 2.

Multicenter external validation of prediction models for clinical outcomes after spinal fusion for lumbar degenerative disease.

Background: Clinical prediction models (CPM), such as the SCOAP-CERTAIN tool, can be utilized to enhance decision-making for lumbar spinal fusion surgery by providing quantitative estimates of outcomes, aiding surgeons in assessing potential benefits and risks for each individual patient. External validation is crucial in CPM to assess generalizability beyond the initial dataset. This ensures performance in diverse populations, reliability and real-world applicability of the results.

Dataset of Finite Element Models of Normal and Deformed Thoracolumbar Spine.

Adult spine deformity (ASD) is prevalent and leads to a sagittal misalignment in the vertebral column. Computational methods, including Finite Element (FE) Models, have emerged as valuable tools for investigating the causes and treatment of ASD through biomechanical simulations. However, the process of generating personalised FE models is often complex and time-consuming.

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.

Automatic grading of intervertebral disc degeneration in lumbar dog spines.

Background: Intervertebral disc degeneration is frequent in dogs and can be associated with symptoms and functional impairments. The degree of disc degeneration can be assessed on T2-weighted MRI scans using the Pfirrmann classification scheme, which was developed for the human spine. However, it could also be used to quantify the effectiveness of disc regeneration therapies.

Comparing image normalization techniques in an end-to-end model for automated modic changes classification from MRI images.

Introduction: Modic Changes (MCs) are MRI alterations in spine vertebrae's signal intensity. This study introduces an end-to-end model to automatically detect and classify MCs in lumbar MRIs. The model's two-step process involves locating intervertebral regions and then categorizing MC types (MC0, MC1, MC2) using paired T1-and T2-weighted images.

A decade of experience in over 300 surgically treated spine patients with long-term oral anticoagulation: a propensity score matched cohort study.

Purpose: The aim of this study was to investigate the risks and outcomes of patients with long-term oral anticoagulation (OAC) undergoing spine surgery.

Methods: All patients on long-term OAC who underwent spine surgery between 01/2005 and 06/2015 were included. Data were prospectively collected within our in-house Spine Surgery registry and retrospectively supplemented with patient chart and administrative database information.

Image annotation and curation in radiology: an overview for machine learning practitioners.

"Garbage in, garbage out" summarises well the importance of high-quality data in machine learning and artificial intelligence. All data used to train and validate models should indeed be consistent, standardised, traceable, correctly annotated, and de-identified, considering local regulations. This narrative review presents a summary of the techniques that are used to ensure that all these requirements are fulfilled, with special emphasis on radiological imaging and freely available software solutions that can be directly employed by the interested researcher.

Predicted vs. measured paraspinal muscle activity in adolescent idiopathic scoliosis patients: EMG validation of optimization-based musculoskeletal simulations.

Musculoskeletal (MSK) models offer great potential for predicting the muscle forces required to inform more detailed simulations of vertebral endplate loading in adolescent idiopathic scoliosis (AIS). In this work, simulations based on static optimization were compared with in vivo measurements in two AIS patients to determine whether computational approaches alone are sufficient for accurate prediction of paraspinal muscle activity during functional activities. We used biplanar radiographs and marker-based motion capture, ground reaction force, and electromyography (EMG) data from two patients with mild and moderate thoracolumbar AIS (Cobb angles: 21° and 45°, respectively) during standing while holding two weights in front (reference position), walking, running, and object lifting.

Prediction of trunk muscle activation and spinal forces in adolescent idiopathic scoliosis during simulated trunk motion: A musculoskeletal modelling study.

Due to lack of reference validation data, the common strategy in characterizing adolescent idiopathic scoliosis (AIS) by musculoskeletal modelling approach consists in adapting structure and parameters of validated body models of adult individuals with physiological alignments. Until now, only static postures have been replicated and investigated in AIS subjects. When aiming to simulate trunk motion, two critical factors need consideration: how distributing movement along the vertebral motion levels (lumbar spine rhythm), and if neglecting or accounting for the contribution of the stiffness of the motion segments (disc stiffness).

Cross-sectional area and fat infiltration of the lumbar spine muscles in patients with back disorders: a deep learning-based big data analysis.

Purpose: Validated deep learning models represent a valuable option to perform large-scale research studies aiming to evaluate muscle quality and quantity of paravertebral lumbar muscles at the population level. This study aimed to assess lumbar spine muscle cross-sectional area (CSA) and fat infiltration (FI) in a large cohort of subjects with back disorders through a validated deep learning model.

Methods: T2 axial MRI images of 4434 patients (n = 2609 females, n = 1825 males; mean age: 56.

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.

Effect of a mattress on lumbar spine alignment in supine position in healthy subjects: an MRI study.

Background: Humans should sleep for about a third of their lifetime and the choice of the mattress is very important from a quality-of-life perspective. Therefore, the primary aim of this study was to assess the changes of lumbar angles, evaluated in a supine position using magnetic resonance imaging (MRI), on a mattress versus a rigid surface.

Methods: Twenty healthy subjects (10 females, 10 males), aged 32.

Developing a new tool for scoliosis screening in a tertiary specialistic setting using artificial intelligence: a retrospective study on 10,813 patients: 2023 SOSORT award winner.

Purpose: The study aims to assess if the angle of trunk rotation (ATR) in combination with other readily measurable clinical parameters allows for effective non-invasive scoliosis screening.

Methods: We analysed 10,813 patients (4-18 years old) who underwent clinical and radiological evaluation for scoliosis in a tertiary clinic specialised in spinal deformities. We considered as predictors ATR, Prominence (mm), visible asymmetry of the waist, scapulae and shoulders, familiarity, sex, BMI, age, menarche, and localisation of the curve.

The lumbar adjacent-level syndrome: analysis of clinical, radiological, and surgical parameters in a large single-center series.

Objective: The development of specific clinical and neurological symptoms and radiological degeneration affecting the segment adjacent to a spinal arthrodesis comprise the framework of adjacent-level syndrome. Through the analysis of a large surgical series, this study aimed to identify possible demographic, clinical, radiological, and surgical risk factors involved in the development of adjacent-level syndrome.

Methods: A single-center retrospective analysis of adult patients undergoing lumbar fusion procedures between January 2014 and December 2018 was performed.