An artificial intelligence powered platform for auto-analyses of spine alignment irrespective of image quality with prospective validation

Selecting the Substantially Touched Vertebra as the Lowest Instrumented Vertebra in Spinal Surgeries for B3GALT6-Related Disorders: Clinical Experience and Literature Review

OBJECTIVES

B3GALT6-related disorders are characterized by severe early-onset spinal deformities requiring surgical corrections but are associated with increased risks of perioperative complications. This study reports the clinical experience and outcomes of selecting the substantially touched vertebra (STV) as the lowest instrumented vertebra (LIV) in spinal surgeries for patients with B3GALT6-related disorders, a group of extremely rare skeletal and connective tissue disorders.

METHODS

This retrospective study included patients who were molecularly diagnosed with B3GALT6-related disorders and received spinal surgeries for (kypho)scoliosis between 2017 and June 2023. Their medical records were reviewed. We also conducted a systematic literature review to identify (kypho)scoliosis management in patients with B3GALT6-related disorders.

RESULTS

We identified a total of four patients. Patient 1 presented with severe kyphoscoliosis and segmentation defects and received a pedicle subtraction osteotomy with short fusion and dual growing rods from T3 to L3. However, coronal imbalance was observed at the 18-month follow-up. Genetic testing revealed biallelic disease-causing variants in B3GALT6. A revision surgery was successfully performed, with the level of the LIV extended to the STV (L4). The LIV was similarly extended to the STV in the index surgery for subsequent Patients 2 and 3 who received preoperative genetic testing results, and no complication has been observed. Patient 4 underwent preoperative Halo-pelvic traction to minimize complications, followed by posterior spinal fusion. The curves were successfully reduced without complications. A systematic literature review identified 86 articles reporting (kypho) scoliosis management in 12 of the 63 patients with B3GALT6-related disorders. Limited surgical experience has been reported, with an increased rate of complications, including death.

CONCLUSIONS

Selecting the STV as the LIV is recommended in spinal surgeries for patients with B3GALT6-related disorders, considering the characteristic joint hypermobility associated with the condition. Additionally, preoperative Halo-pelvic traction may also be safe and effective. Furthermore, preoperative molecular diagnosis is essential for enabling precision medicine and minimizing complications.

The Application of Artificial Intelligence in Spine Surgery: A Scoping Review

BACKGROUND

A comprehensive review on the application of artificial intelligence (AI) within spine surgery as a specialty remains lacking.

METHODS

This scoping review was conducted upon PubMed and EMBASE databases according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Our analysis focused on publications from January 1, 2020, to March 31, 2024, with a specific focus on AI in the field of spine surgery. Review articles and articles predominantly concerning secondary validation of algorithms, medical physics, electronic devices, biomechanics, preclinical, and with a lack of clinical emphasis were excluded.

RESULTS

One hundred five studies were included after our inclusion/exclusion criteria were applied. Most studies (n = 100) were conducted through supervised learning upon prelabeled data sets. Overall, 38 studies used conventional machine learning methods upon predefined features, whereas 67 used deep learning methods, predominantly for medical image analyses. Only 25.7% of studies (27/105) collected data from more than 1,000 patients for model development and validation. Data originated from only a single center in 72 studies. The most common application was prognostication (38/105), followed by diagnosis (35/105), medical image processing (29/105), and surgical assistance (3/105).

CONCLUSION

The application of AI within the domain of spine surgery has significant potential to advance patient-specific diagnosis, management, and surgical execution.

SiameseNet based on multiple instance learning for accurate identification of the histological grade of ICC tumors

Background

After hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC) is the second most common primary liver cancer. Timely and accurate identification of ICC histological grade is critical for guiding clinical diagnosis and treatment planning.

Method

We proposed a dual-branch deep neural network (SiameseNet) based on multiple-instance learning and cross-attention mechanisms to address tumor heterogeneity in ICC histological grade prediction. The study included 424 ICC patients (381 in training, 43 in testing). The model integrated imaging data from two modalities through cross-attention, optimizing feature representation for grade classification.

Results

In the testing cohort, the model achieved an accuracy of 86.0%, AUC of 86.2%, sensitivity of 84.6%, and specificity of 86.7%, demonstrating robust predictive performance.

Conclusion

The proposed framework effectively mitigates performance degradation caused by tumor heterogeneity. Its high accuracy and generalizability suggest potential clinical utility in assisting histopathological assessment and personalized treatment planning for ICC patients.

Deep learning in Cobb angle automated measurement on X-rays: a systematic review and meta-analysis

PURPOSE

This study aims to provide an overview of different deep learning algorithms (DLAs), identify the limitations, and summarize potential solutions to improve the performance of DLAs.

METHODS

We reviewed eligible studies on DLAs for automated Cobb angle estimation on X-rays and conducted a meta-analysis. A systematic literature search was conducted in six databases up until September 2023. Our meta-analysis included an evaluation of reported circular mean absolute error (CMAE) from the studies, as well as a subgroup analysis of implementation strategies. Risk of bias was assessed using the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). This study was registered in PROSPERO prior to initiation (CRD42023403057).

RESULTS

We identified 120 articles from our systematic search (n = 3022), eventually including 50 studies in the systematic review and 17 studies in the meta-analysis. The overall estimate for CMAE was 2.99 (95% CI 2.61-3.38), with high heterogeneity (94%, p < 0.01). Segmentation-based methods showed greater accuracy (p < 0.01), with a CMAE of 2.40 (95% CI 1.85-2.95), compared to landmark-based methods, which had a CMAE of 3.31 (95% CI 2.89-3.72).

CONCLUSIONS

According to our limited meta-analysis results, DLAs have shown relatively high accuracy for automated Cobb angle measurement. In terms of CMAE, segmentation-based methods may perform better than landmark-based methods. We also summarized potential ways to improve model design in future studies. It is important to follow quality guidelines when reporting on DLAs.

Superior performance of a center-point AI model over VFLDNet in automated cobb angle estimation for scoliosis assessment

PURPOSE

Aims to establish the superiority of our proposed model over the state-of-the-art vertebra-focused landmark detection network (VFLDNet) in automating Cobb angle estimation from spinal radiographs.

METHODS

Utilizing a private dataset for external validation, we compared the performance of our center-point detection-based vertebra localization and tilt estimation network (VLTENet) with the key-point detection-based VFLDNet. Both models' Cobb angle predictions were rigorously evaluated against manual consensus score using metrics such as mean absolute error (MAE), correlation coefficient, intraclass correlation coefficient (ICC), Fleiss' kappa, Bland-Altman analysis, and classification metrics [sensitivity (SN), specificity, accuracy] focusing on major curve estimation and scoliosis severity classification.

RESULTS

A retrospective analysis of 118 cases with 342 Cobb angle measurements revealed that our model achieved a MAE of 2.15° for total Cobb angles and 1.89° for the major curve, significantly outperforming VFLDNet's MAE of 2.80°and 2.57°, respectively. Both models demonstrated robust correlation and ICC, but our model excelled in classification consistency, particularly in predicting major curve magnitude (ours: kappa = 0.83; VFLDNet: kappa = 0.67). In subgroup analyses by scoliosis severity, our model consistently surpassed VFLDNet, displaying superior mean (SD) differences, narrower limits of agreement, and higher SN, specificity, and accuracy, most notably in moderate (ours: SN = 86.84%; VFLDNet: SN = 83.16%) to severe (ours: SN = 92.86%; VFLDNet: SN = 85.71%) scoliosis.

CONCLUSION

Our model emerges as the superior choice for automated Cobb angle estimation, particularly in assessing major curve and moderate to severe scoliosis, underscoring its potential to revolutionize clinical workflows and enhance patient care.

Applications of artificial intelligence for adolescent idiopathic scoliosis: mapping the evidence

PURPOSE

Adolescent idiopathic scoliosis (AIS) is a common spinal deformity with varying progression, complicating treatment decisions. Artificial intelligence (AI) and machine learning (ML) are increasingly prominent in orthopedic care, aiding in diagnosis, risk-stratification, and treatment guidance. This scoping review outlines AI applications in AIS.

METHODS

This study followed PRISMA-ScR guidelines and included articles that reported the development, use, or validation of AI models for treating, diagnosing, or predicting clinical outcomes in AIS.

RESULTS

40 full-text articles were included, with most studies published in the last 5 years (77.5%). Common ML techniques were convolutional neural networks (55%), decision trees and random forests (15%), and artificial neural networks (15%). Most AI applications in AIS were for imaging analysis (25/40; 62.5%), focusing on automatic measurement of Cobb angle, and axial vertebral rotation (13/25; 52%) and curve classification/severity (13/25; 52%). Prediction was the second most common application (15/40; 37.5%), with studies predicting curve progression (9/15; 60%), and Cobb angles (9/15; 60%). Only 15 studies (37.5%) reported clinical implementation guidelines for AI in AIS management. 52.5% of studies reported model accuracy, with an average of 85.4%.

CONCLUSION

This review highlights the applications of AI in AIS care, notably including automatic radiographic analysis, curve type classification, prediction of curve progression, and AIS diagnosis. However, the current lack of clear clinical implementation guidelines, model transparency, and external validation of studied models limits clinician trust and the generalizability and applicability of AI in AIS management.

Fully Automated Measurement of Cobb Angles in Coronal Plane Spine Radiographs

Background/Objectives: scoliosis is a three-dimensional structural deformity characterized by lateral and rotational curvature of the spine. The current gold-standard method to assess scoliosis is the measurement of lateral curvature of the spine using the Cobb angle in coronal plane radiographs. The interrater variability for Cobb angle measurements reaches up to 10°. The purpose of this study was to describe and assess the performance of a fully automated method for measuring Cobb angles using a commercially available artificial intelligence (AI) model trained on over 17,000 images, and investigate its interrater/intrarater agreement with a reference standard. Methods: in total, 196 AP/PA full-spine radiographs were included in this study. A reference standard was established by four radiologists, defined as the median of their Cobb angle measurements. Independently, an AI-based software, IB Lab SQUIRREL (version 1.0), also performed Cobb angle measurements on the same radiographs. Results: after comparing the readers' Cobb angle end vertebrae selection to the AI's outputs, 194 curvatures were considered valid for performance assessment, displaying an accuracy of 88.58% in end vertebrae selection. The AI's performance showed very low absolute bias, with a mean difference and standard deviation of differences from the reference standard of 0.16° ± 0.35° in the Cobb angle measurements. The ICC comparing the reference standard and the AI's measurements was 0.97. Conclusions: the AI model demonstrated good results in the determination of end vertebrae and excellent results in automated Cobb angle measurements compared to radiologists and could serve as a reliable tool in clinical practice and research.

EUFormer: Learning Driven 3D Spine Deformity Assessment with Orthogonal Optical Images

In clinical settings, the screening, diagnosis, and monitoring of adolescent idiopathic scoliosis (AIS) typically involve physical or radiographic examinations. However, physical examinations are subjective, while radiographic examinations expose patients to harmful radiation. Consequently, we propose a pipeline that can accurately determine scoliosis severity. This pipeline utilizes posteroanterior (PA) and lateral (LAT) RGB images as input to generate spine curve maps, which are then used to reconstruct the three-dimensional (3D) spine curve for AIS severity grading. To generate the 2D spine curves accurately and efficiently, we further propose an Efficient U-shape transFormer (EUFormer) as the generator. It can efficiently utilize the learned feature across channels, therefore producing consecutive spine curves from both PA and LAT views. Experimental results demonstrate superior performance of EUFormer on spine curve generation against other classical U-shape models. This finding demonstrates that the proposed method for grading the severity of AIS, based on a 3D spine curve, is more accurate when compared to using a 2D spine curve.

RASpine: Regional Attention Lateral Spinal Segmentation based on Anatomical Prior Knowledge

In the clinical diagnosis and treatment of spinal disorders, segmenting the spine from X-ray images provides clear visualization of the spinal structure and morphology. However, while existing spine segmentation methods perform well on anteroposterior X-ray images, their performance is poor on lateral X-rays. This is mainly due to the low contrast and severe occlusion of the thoracic vertebrae on lateral X-rays, resulting in overlapping vertebrae in segmentation results. To address this issue, this paper proposes a segmentation network called Region Attention and Spine Prior-based Network (RASpine). By utilizing the anatomical prior knowledge of non-overlapping regions between different vertebrae, an overlap detector is designed to identify overlapping parts of different vertebrae in the segmentation results. Moreover, a loss function is designed to penalize the overlapping regions, thereby avoiding overlapping segmentation results for the vertebrae. Finally, region attention is employed to enhance the segmentation accuracy in challenging regions. The proposed RASpine is trained, validated, and tested on a clinical dataset. Experimental results demonstrate that compared to existing mainstream medical image segmentation algorithms, RASpine effectively addresses the overlapping parts in lateral X-ray spine segmentation results and achieves more satisfactory performance in multiple evaluation metrics.

Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances

Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.

Development of Automatic Assessment Framework for Spine Deformity Using Freehand 3-D Ultrasound Imaging System

A 3-D ultrasound (US) imaging technique has been studied to facilitate the diagnosis of spinal deformity without radiation. The objective of this article is to propose an assessment framework to automatically estimate spinal deformity in US spine images. The proposed framework comprises four major components, a US spine image generator, a novel transformer-based lightweight spine detector network, an angle evaluator, and a 3-D modeler. The principal component analysis (PCA) and discriminative scale space tracking (DSST) method are first adopted to generate the US spine images. The proposed detector is equipped with a redundancy queries removal (RQR) module and a regularization item to realize accurate and unique detection of spine images. Two clinical datasets, a total of 273 images from adolescents with idiopathic scoliosis, are used for the investigation of the proposed framework. The curvature is estimated by the angle evaluator, and the 3-D mesh model is established by the parametric modeling technique. The accuracy rate (AR) of the proposed detector can be achieved at 99.5%, with a minimal redundancy rate (RR) of 1.5%. The correlations between automatic curve measurements on US spine images from two datasets and manual measurements on radiographs are 0.91 and 0.88, respectively. The mean absolute difference (MAD) and standard deviation (SD) are 2.72° ± 2.14° and 2.91° ± 2.36° , respectively. The results demonstrate the effectiveness of the proposed framework to advance the application of the 3-D US imaging technique in clinical practice for scoliosis mass screening and monitoring.

Artificial Intelligence in Scoliosis Classification: An Investigation of Language-Based Models

Open-source artificial intelligence models are finding free application in various industries, including computer science and medicine. Their clinical potential, especially in assisting diagnosis and therapy, is the subject of increasingly intensive research. Due to the growing interest in AI for diagnostics, we conducted a study evaluating the abilities of AI models, including ChatGPT, Microsoft Bing, and Scholar AI, in classifying single-curve scoliosis based on radiological descriptions. Fifty-six posturographic images depicting single-curve scoliosis were selected and assessed by two independent neurosurgery specialists, who classified them as mild, moderate, or severe based on Cobb angles. Subsequently, descriptions were developed that accurately characterized the degree of spinal deformation, based on the measured values of Cobb angles. These descriptions were then provided to AI language models to assess their proficiency in diagnosing spinal pathologies. The artificial intelligence models conducted classification using the provided data. Our study also focused on identifying specific sources of information and criteria applied in their decision-making algorithms, aiming for a deeper understanding of the determinants influencing AI decision processes in scoliosis classification. The classification quality of the predictions was evaluated using performance evaluation metrics such as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and balanced accuracy. Our study strongly supported our hypothesis, showing that among four AI models, ChatGPT 4 and Scholar AI Premium excelled in classifying single-curve scoliosis with perfect sensitivity and specificity. These models demonstrated unmatched rater concordance and excellent performance metrics. In comparing real and AI-generated scoliosis classifications, they showed impeccable precision in all posturographic images, indicating total accuracy (1.0, MAE = 0.0) and remarkable inter-rater agreement, with a perfect Fleiss' Kappa score. This was consistent across scoliosis cases with a Cobb's angle range of 11-92 degrees. Despite high accuracy in classification, each model used an incorrect angular range for the mild stage of scoliosis. Our findings highlight the immense potential of AI in analyzing medical data sets. However, the diversity in competencies of AI models indicates the need for their further development to more effectively meet specific needs in clinical practice.

SpineHRformer: A Transformer-Based Deep Learning Model for Automatic Spine Deformity Assessment with Prospective Validation

The Cobb angle (CA) serves as the principal method for assessing spinal deformity, but manual measurements of the CA are time-consuming and susceptible to inter- and intra-observer variability. While learning-based methods, such as SpineHRNet+, have demonstrated potential in automating CA measurement, their accuracy can be influenced by the severity of spinal deformity, image quality, relative position of rib and vertebrae, etc. Our aim is to create a reliable learning-based approach that provides consistent and highly accurate measurements of the CA from posteroanterior (PA) X-rays, surpassing the state-of-the-art method. To accomplish this, we introduce SpineHRformer, which identifies anatomical landmarks, including the vertices of endplates from the 7th cervical vertebra (C7) to the 5th lumbar vertebra (L5) and the end vertebrae with different output heads, enabling the calculation of CAs. Within our SpineHRformer, a backbone HRNet first extracts multi-scale features from the input X-ray, while transformer blocks extract local and global features from the HRNet outputs. Subsequently, an output head to generate heatmaps of the endplate landmarks or end vertebra landmarks facilitates the computation of CAs. We used a dataset of 1934 PA X-rays with diverse degrees of spinal deformity and image quality, following an 8:2 ratio to train and test the model. The experimental results indicate that SpineHRformer outperforms SpineHRNet+ in landmark detection (Mean Euclidean Distance: 2.47 pixels vs. 2.74 pixels), CA prediction (Pearson correlation coefficient: 0.86 vs. 0.83), and severity grading (sensitivity: normal-mild; 0.93 vs. 0.74, moderate; 0.74 vs. 0.77, severe; 0.74 vs. 0.7). Our approach demonstrates greater robustness and accuracy compared to SpineHRNet+, offering substantial potential for improving the efficiency and reliability of CA measurements in clinical settings.

Deep Learning Model to Classify and Monitor Idiopathic Scoliosis in Adolescents Using a Single Smartphone Photograph

Importance

Adolescent idiopathic scoliosis (AIS) is the most common pediatric spinal disorder. Routine physical examinations by trained personnel are critical to diagnose severity and monitor curve progression in AIS. In the presence of concerning malformation, radiographs are necessary for diagnosis or follow-up, guiding further management, such as bracing correction for moderate malformation and spine surgery for severe malformation. If left unattended, progressive deterioration occurs in two-thirds of patients, leading to significant health concerns for growing children.

Objective

To assess the ability of an open platform application (app) using a validated deep learning model to classify AIS severity and curve type, as well as identify progression.

Design, Setting, and Participants

This diagnostic study was performed with data from radiographs and smartphone photographs of the backs of adolescent patients at spine clinics. The ScolioNets deep learning model was developed and validated in a prospective training cohort, then incorporated and tested in the AlignProCARE open platform app in 2022. Ground truths (GTs) included severity, curve type, and progression as manually annotated by 2 experienced spine specialists based on the radiographic examinations of the participants' spines. The GTs and app results were blindly compared with another 2 spine surgeons' assessments of unclothed back appearance. Data were analyzed from October 2022 to February 2023.

Exposure

Acquisitions of unclothed back photographs using a mobile app.

Main Outcomes and Measures

Outcomes of interest were classification of AIS severity and progression. Quantitative statistical analyses were performed to assess the performance of the deep learning model in classifying the deformity as well as in distinguishing progression during 6-month follow-up.

Results

The training data set consisted of 1780 patients (1295 [72.8%] female; mean [SD] age, 14.3 [3.3] years), and the prospective testing data sets consisted of 378 patients (279 [73.8%] female; mean [SD] age, 14.3 [3.8] years) and 376 follow-ups (294 [78.2%] female; mean [SD] age, 15.6 [2.9] years). The model recommended follow-up with an area under receiver operating characteristic curve (AUC) of 0.839 (95% CI, 0.789-0.882) and considering surgery with an AUC of 0.902 (95% CI, 0.859-0.936), while showing good ability to distinguish among thoracic (AUC, 0.777 [95% CI, 0.745-0.808]), thoracolumbar or lumbar (AUC, 0.760 [95% CI, 0.727-0.791]), or mixed (AUC, 0.860 [95% CI, 0.834-0.887]) curve types. For follow-ups, the model distinguished participants with or without curve progression with an AUC of 0.757 (95% CI, 0.630-0.858). Compared with both surgeons, the model could recognize severities and curve types with a higher sensitivity (eg, sensitivity for recommending follow-up: model, 84.88% [95% CI, 75.54%-91.70%]; senior surgeon, 44.19%; junior surgeon, 62.79%) and negative predictive values (NPVs; eg, NPV for recommending follow-up: model, 89.22% [95% CI, 84.25%-93.70%]; senior surgeon, 71.76%; junior surgeon, 79.35%). For distinguishing curve progression, the sensitivity and NPV were comparable with the senior surgeons (sensitivity, 63.33% [95% CI, 43.86%-80.87%] vs 77.42%; NPV, 68.57% [95% CI, 56.78%-78.37%] vs 72.00%). The junior surgeon reported an inability to identify curve types and progression by observing the unclothed back alone.

Conclusions

This diagnostic study of adolescent patients screened for AIS found that the deep learning app had the potential for out-of-hospital accessible and radiation-free management of children with scoliosis, with comparable performance as spine surgeons experienced in AIS management.

Radiograph-comparable image synthesis for spine alignment analysis using deep learning with prospective clinical validation

Background

Adolescent idiopathic scoliosis (AIS) is the most common type of spinal disorder affecting children. Clinical screening and diagnosis require physical and radiographic examinations, which are either subjective or increase radiation exposure. We therefore developed and validated a radiation-free portable system and device utilising light-based depth sensing and deep learning technologies to analyse AIS by landmark detection and image synthesis.

Methods

Consecutive patients with AIS attending two local scoliosis clinics in Hong Kong between October 9, 2019, and May 21, 2022, were recruited. Patients were excluded if they had psychological and/or systematic neural disorders that could influence the compliance of the study and/or the mobility of the patients. For each participant, a Red Green Blue-Depth (RGBD) image of the nude back was collected using our in-house radiation-free device. Manually labelled landmarks and alignment parameters by our spine surgeons were considered as the ground truth (GT). Images from training and internal validation cohorts (n = 1936) were used to develop the deep learning models. The model was then prospectively validated on another cohort (n = 302) which was collected in Hong Kong and had the same demographic properties as the training cohort. We evaluated the prediction accuracy of the model on nude back landmark detection as well as the performance on radiograph-comparable image (RCI) synthesis. The obtained RCIs contain sufficient anatomical information that can quantify disease severities and curve types.

Findings

Our model had a consistently high accuracy in predicting the nude back anatomical landmarks with a less than 4-pixel error regarding the mean Euclidian and Manhattan distance. The synthesized RCI for AIS severity classification achieved a sensitivity and negative predictive value of over 0.909 and 0.933, and the performance for curve type classification was 0.974 and 0.908, with spine specialists' manual assessment results on real radiographs as GT. The estimated Cobb angle from synthesized RCIs had a strong correlation with the GT angles (R² = 0.984, p < 0.001).

Interpretation

The radiation-free medical device powered by depth sensing and deep learning techniques can provide instantaneous and harmless spine alignment analysis which has the potential for integration into routine screening for adolescents.

Funding

Innovation and Technology Fund (MRP/038/20X), Health Services Research Fund (HMRF) 08192266.

SpineQ: Unsupervised 3D Lumbar Quantitative Assessment

Most lumbar quantitative assessment methods can only analyze the image from one view and require laborious manual annotation. We aim to develop an unsupervised pipeline for 3D quantitative assessment of the lumbar spine that can assess the MRI with different views. We combine rule-based and deep learning methods to generate multi-tissue segmentation, and parameters can be measured from segmentation results using the anatomical and geometric prior. Preliminary testing demonstrates that our proposed method can generate accurate segmentation and measurement results.Clinical Relevance- The proposed unsupervised 3D lumbar quantitative assessment pipeline can significantly improve the efficiency and consistency of clinical diagnosis and surgical planning.

Evaluating Scoliosis Severity Based on Posturographic X-ray Images Using a Contrastive Language-Image Pretraining Model

Assessing severe scoliosis requires the analysis of posturographic X-ray images. One way to analyse these images may involve the use of open-source artificial intelligence models (OSAIMs), such as the contrastive language-image pretraining (CLIP) system, which was designed to combine images with text. This study aims to determine whether the CLIP model can recognise visible severe scoliosis in posturographic X-ray images. This study used 23 posturographic images of patients diagnosed with severe scoliosis that were evaluated by two independent neurosurgery specialists. Subsequently, the X-ray images were input into the CLIP system, where they were subjected to a series of questions with varying levels of difficulty and comprehension. The predictions obtained using the CLIP models in the form of probabilities ranging from 0 to 1 were compared with the actual data. To evaluate the quality of image recognition, true positives, false negatives, and sensitivity were determined. The results of this study show that the CLIP system can perform a basic assessment of X-ray images showing visible severe scoliosis with a high level of sensitivity. It can be assumed that, in the future, OSAIMs dedicated to image analysis may become commonly used to assess X-ray images, including those of scoliosis.

Automatic deep learning-based assessment of spinopelvic coronal and sagittal alignment

PURPOSE

The purpose of this study was to evaluate an artificial intelligence (AI) solution for estimating coronal and sagittal spinopelvic alignment on conventional uniplanar two-dimensional whole-spine radiograph.

MATERIAL AND METHODS

This retrospective observational study included 100 patients (35 men, 65 women) with a median age of 14 years (IQR: 13, 15.25; age range: 3-64 years) who underwent conventional uniplanar two-dimensional whole-spine radiograph in standing position between January and July 2022. Ten most commonly used spinopelvic coronal and sagittal parameters were retrospectively measured without AI by a junior radiologist and approved or adjusted by a senior musculoskeletal radiologist to reach final measurements. Final measurements were used as the ground truth to assess AI performance for each parameter. AI performances were estimated using mean absolute errors (MAE), intraclass correlation coefficient (ICCs), and accuracy for selected clinically relevant thresholds. Readers visually classified AI outputs to assess reliability at a patient-level.

RESULTS

AI solution showed excellent consistency without bias in coronal (ICCs ≥ 0.95; MAE ≤ 2.9° or 1.97 mm) and sagittal (ICCs ≥ 0.85; MAE ≤ 4.4° or 2.7 mm) spinopelvic evaluation, except for kyphosis (ICC = 0.58; MAE = 8.7°). AI accuracy to classify low Cobb angle, severe scoliosis or frontal pelvic asymmetry was 91% (95% CI: 85-96), 99% (95% CI: 97-100) and 94% (95% CI: 89-98), respectively. Overall, AI provided reliable measurements in 72/100 patients (72%).

CONCLUSION

The AI solution used in this study for combined coronal and sagittal spinopelvic balance assessment provides results consistent with those of a senior musculoskeletal radiologist, and shows potential benefit for reducing workload in future routine implementation.

An Evolution Gaining Momentum—The Growing Role of Artificial Intelligence in the Diagnosis and Treatment of Spinal Diseases

In recent years, applications using artificial intelligence have been gaining importance in the diagnosis and treatment of spinal diseases. In our review, we describe the basic features of artificial intelligence which are currently applied in the field of spine diagnosis and treatment, and we provide an orientation of the recent technical developments and their applications. Furthermore, we point out the possible limitations and challenges in dealing with such technological advances. Despite the momentary limitations in practical application, artificial intelligence is gaining ground in the field of spine treatment. As an applying physician, it is therefore necessary to engage with it in order to benefit from those advances in the interest of the patient and to prevent these applications being misused by non-medical partners.