Artificial intelligence in severe rigid Scoliosis: Opportunities, challenges, and the road ahead

Abstract

Severe rigid scoliosis remains one of the most complex challenges in spine surgery, characterized by three-dimensional deformity, progressive rigidity, and high perioperative risks. Traditional surgical planning and correction strategies are limited by variability in anatomical presentations, difficulty in predicting outcomes, and the need for meticulous intraoperative decision-making. In recent years, artificial intelligence (AI) has emerged as a promising tool to address these challenges, offering applications in imaging analysis, deformity classification, surgical planning, navigation, and outcome prediction. This review explores the current opportunities of AI in the management of severe rigid scoliosis, critically examines its limitations, and outlines the future roadmap toward integration in precision spine surgery.

1. Introduction

Severe rigid scoliosis represents one of the most challenging spinal deformities to manage, characterized by pronounced structural rigidity, complex three-dimensional curvature, and a heightened risk of neurological and surgical complications.^1,2 Traditional management relies on detailed imaging, meticulous surgical planning, and intraoperative precision; however, even with expert care, outcomes can be unpredictable and technically demanding.³

Artificial intelligence (AI) has recently emerged as a transformative tool across healthcare, demonstrating remarkable capabilities in medical imaging, surgical navigation, and predictive analytics.⁴ In scoliosis, AI has shown promise in automating vertebral segmentation, estimating Cobb angles with high accuracy, predicting curve progression, and optimizing surgical planning.⁵ AI-driven technologies; including three-dimensional reconstruction, robotics, augmented reality, and digital twins; offer opportunities to enhance surgical precision, reduce intraoperative risk, and improve patient-specific outcomes.⁶

Challenges such as limited representative datasets, heterogeneous imaging, algorithm interpretability, workflow integration, cost, ethical considerations, and a lack of prospective validation constrain clinical adoption.⁷ Addressing these barriers requires a comprehensive roadmap that integrates multicenter data collection, explainable AI development, seamless surgical integration, global accessibility, ethical frameworks, and rigorous prospective clinical trials.

This review explores the current opportunities, limitations, and future directions for AI in the management of severe rigid scoliosis, highlighting its potential to transform surgical decision-making and patient care while emphasizing the critical steps needed for safe and effective clinical implementation.

2. Opportunities for AI in Severe rigid Scoliosis

AI is emerging as a powerful tool in severe rigid scoliosis, improving imaging accuracy, surgical planning, intraoperative navigation, and outcome prediction.⁵ By enabling precise vertebral segmentation, Cobb angle estimation, 3D reconstruction, and real-time guidance, AI has the potential to enhance surgical precision, personalize interventions, and improve patient outcomes in this complex and high-risk population.⁷ The opportunities of AI in severe rigid scoliosis can be summarized as (Figure. 1).

Fig. 1.

Opportunities limitations and challenges for AI in severe rigid scoliosis.

2.1. Imaging and deformity analysis

AI has transformed scoliosis imaging by improving deformity analysis, segmentation, and progression prediction. Vertebral segmentation Dice scores reach 95 %, Cobb angle errors are <3°, and lesion localization is within 2 mm.^8,9 convolutional neural networks (CNNs) on back surface data (n = 160 AIS) achieved the mean absolute error and root mean square error were 4.0° and 5.4°, respectively, diagnostic accuracy 94 %, sensitivity 99 %, and PPV 95 % for scoliosis >10°.¹⁰

Time-series CNNs predict progression >85 % accuracy, while feedforward neural network (FNN) predict intervertebral angles with mean absolute error 1.5°, Pearson r 0.86, accuracy 0.85, sensitivity 0.65, specificity 0.91.¹¹ AI-driven 3D reconstruction from CT/MRI enables detailed visualization of rigidity and rotation, supports advanced curve sub classifications, and predicts bone quality via CT texture analysis to guide fixation planning.

In addition to conventional radiography and CT-based imaging, bone MRI is emerging as a valuable modality for assessing vertebral integrity, cortical and trabecular bone quality, and spinal cord relationships in severe rigid scoliosis. Deep learning–based algorithms can enhance MRI interpretation by automatically segmenting vertebrae, detecting bone density variations, and predicting areas at risk for intraoperative fracture or implant failure. AI-driven analysis of MRI data allows for more precise preoperative planning, including optimal osteotomy levels, fixation strategies, and risk stratification, particularly in patients with severe rigidity or fusion-related anatomical distortion. Incorporating bone MRI into AI-assisted workflows broadens the imaging spectrum and may improve patient-specific surgical decision-making in complex spinal deformities.

2.2. Surgical planning and simulation

AI aids complex surgical planning, including osteotomy selection, fusion levels, and correction targets. Self-organizing maps (n = 1776 AIS) matched expert fusion region decisions 88 % of the time.¹² The “SpinePose” model predicted spinopelvic parameters (SVA, PT, SS, PI, LL, T1PA, L1PA) with median errors 1.1°–2.6° and ICC 0.91–1.0.¹³ AI-driven predictive modeling for custom rods in 48 AIS cases improved sagittal alignment and patient-reported outcomes.¹⁴ Virtual simulations and AI-guided screw trajectory planning optimize implant placement, potentially reducing complications.

2.3. Intraoperative navigation and robotics

AI-enhanced navigation improves pedicle screw placement accuracy but may increase operative time by around 30 min.¹⁵ Marker-less AR systems achieved 96 % registration success, target registration error 2.7 mm, screw trajectory error 1.6°, and entry point error 2.4 mm; ex vivo tests showed 100 % screw accuracy.¹⁶ Robotics and ML-based intraoperative feedback enhance correction precision and alert surgeons to risks like excessive forces or neurological compromise.

2.4. Predictive analytics for outcomes and complications

AI predicts postoperative complications and outcomes with high accuracy. In adult deformity (n = 510), ML models forecast PJF/PJK with 86 % accuracy, AUC 0.89.¹⁷ SMOTE-based models improved PJK prediction to AUC 0.944, accuracy 0.909, F1 0.667.¹⁸ Perioperative risk prediction in 4046 posterior spinal fusions yielded PPV 92.6 % and NPV 98.45 % for surgical site infection.¹⁹ Other models predict survival, ambulation, and outcomes with AUC 0.80–0.90.²⁰ Combining RandomOverSampler and SMOTE in smaller cohorts (n = 244) achieved 76 % accuracy, F1 67 %, supporting risk stratification, resource optimization, and reduced ICU burden.²¹

To further contextualize the potential of AI in the surgical management of severe rigid scoliosis, we present two illustrative hypothetical vignettes. These examples are not derived from real patient cases but are constructed to demonstrate how AI applications, described in the literature, could influence surgical decision-making in complex deformity correction.22, 23, 24, 25

AI-Assisted Osteotomy Level Selection: Consider a 17-year-old patient with a severe rigid thoracolumbar scoliosis measuring 110° (Cobb angle), marked vertebral rotation, and sagittal imbalance. Traditional planning in such cases often requires prolonged intraoperative deliberation to determine the optimal osteotomy level. An AI-based surgical planning platform, modeled after systems that integrate preoperative imaging with biomechanical simulations, could assist by suggesting a pedicle subtraction osteotomy at L3 rather than a more proximal level. Such a recommendation, grounded in predictive modeling, may forecast improved lumbar lordosis restoration with reduced operative time and blood loss, while leaving the final decision to the surgeon's judgment.^24,25

AI-Guided Rod Contouring Prediction: In another hypothetical scenario, a 22-year-old patient with a rigid double-major scoliosis (thoracic curve 95°, lumbar curve 85°) is planned for posterior-only correction. Achieving precise rod contouring is essential to avoid undercorrection or junctional complications. Leveraging preoperative CT data, an AI-driven simulation platform could generate a virtual model of the deformity and predict optimal rod bend angles to achieve target sagittal and coronal alignment. Similar predictive approaches have been reported in the context of personalized rod contouring and surgical simulation.^22,23 Intraoperatively, pre-bent rods prepared according to these specifications may facilitate smoother correction manoeuvres, minimise repeated rod adjustments, and improve final alignment. Together, these hypothetical vignettes underscore how AI-guided planning and prediction might enhance surgical efficiency and reproducibility in severe rigid scoliosis, while reinforcing the ongoing need for clinical validation and surgeon oversight.

Building on these opportunities, several AI platforms and models have been clinically validated to support real-world management of severe rigid scoliosis. FDA-approved systems such as UNiD™ Adaptive Spine Intelligence (Medtronic) and Momentum Spine (Cedars-Sinai) have demonstrated robust performance in surgical planning, personalized rod design, sagittal alignment restoration, and radiation-free 3D spinal imaging, leveraging large patient datasets and predictive analytics to enhance decision-making in clinical practice (Table 1). In parallel, peer-reviewed studies have validated AI models; including deep learning–based prediction tools and convolutional neural networks (CNNs); for accurate vertebral segmentation, curve classification, and surgical outcome prediction, though these models remain investigational and are not yet FDA-cleared for routine use (Table 2). Together, these platforms and models illustrate the translation of AI research into practical applications, emphasizing both the promise and current limitations of AI in treating complex spinal deformities.

Table 1.

FDA-approved AI platforms for scoliosis management.

Platform	Application Area	Clinical Validation	FDA Approval
UNiD™ Adaptive Spine Intelligence (Medtronic)	Surgical planning, personalized rod design, sagittal alignment restoration	Utilizes a database of over 10,000 spine surgeries for predictive modeling	FDA-cleared for use in clinical settings for personalized spinal surgery planning
Momentum Spine (Cedars-Sinai)	Radiation-free 3D spinal imaging and monitoring	Converts 45-s videos into 3D models for Cobb angle assessment	FDA-cleared for remote monitoring of postural asymmetries, including scoliosis
NSite Scoliosis Assessment App (NSite Medical)	Spinal curvature analysis without ionizing radiation	Analyzes spinal curvature with high precision using AI algorithms	FDA-cleared for use in clinical settings for scoliosis assessment
CoLumbo Version 3 (CoLumbo)	Spinal MRI analysis for scoliosis and fractures	Supports DICOM images of patients with scoliosis and fractures	FDA-cleared for use in clinical settings for lumbar spine MRI analysis

Table 2.

AI models validated in clinical research.

Model/Platform	Application Area	Clinical Validation	Status
Deep Learning Model (2025 Study)	Surgical decision-making & outcome prediction	Demonstrated high accuracy in predicting surgical outcomes for adolescent idiopathic scoliosis	Published in peer-reviewed journal; not yet FDA-approved
Convolutional Neural Network (CNN) System	Imaging analysis & curve classification	Demonstrated high accuracy in identifying vertebral boundaries and classifying spinal curvature	Published in peer-reviewed journal; not yet FDA-approved
AI-Based Surgical Navigation Systems	Surgical planning & navigation	Enhanced accuracy in preoperative planning and intraoperative guidance for spinal surgeries	Under development; not yet FDA-approved
ChatGPT-4	Patient education & scoliosis assessment	Evaluated for answering patient FAQs and photographic scoliosis assessment; reliable for education, limited for advanced diagnostic tasks	Clinically researched; not FDA-approved
Scholar AI Premium	Scoliosis classification	Compared with ChatGPT-4 for single-curve scoliosis classification; showed perfect sensitivity and specificity	Clinically researched; not FDA-approved

3. Challenges for AI in Severe rigid Scoliosis

Although AI has shown substantial potential in scoliosis care, its widespread adoption in severe rigid scoliosis faces several critical barriers, spanning data quality, algorithm transparency, surgical workflow integration, ethical considerations, and clinical validation (Figure. 1).

3.1. Data scarcity and heterogeneity

Severe rigid scoliosis is relatively uncommon compared to adolescent idiopathic scoliosis (AIS), which has historically dominated AI research.²⁶ Consequently, most available AI algorithms are trained on AIS cohorts with abundant imaging and outcome data.⁷ Large datasets, such as those from the Scoliosis Research Society (SRS) or multicenter registries, include only a small fraction of rigid deformity cases, limiting AI generalizability to this subgroup.²⁷

Imaging heterogeneity presents another challenge. Clinical modalities; including EOS imaging, standing radiographs, CT, and MRI; differ in resolution and quality, introducing inconsistency in input data. This variability directly impacts AI performance: segmentation accuracy for spinal anatomy varies by imaging modality; high-resolution CT yields Dice coefficients exceeding ∼95 %, while analyses of standard lumbar X-ray images (anteroposterior views) report Dice coefficients in the range of 0.82–0.96, averaging around 0.91, illustrating the vulnerability of AI in real-world heterogeneous imaging environments.²⁸

3.2. Algorithm transparency and interpretability

Most AI tools for scoliosis rely on deep learning, functioning as “black boxes” that generate predictions without clear reasoning. In severe rigid scoliosis, where intraoperative decisions carry high stakes; including risks of neurological injury; this lack of interpretability undermines surgeon confidence.²⁹

This is especially critical in decisions such as osteotomy selection or rod-bending strategies, where a single error could have catastrophic consequences. Surgeons are more likely to adopt AI tools that provide explainable outputs, such as highlighting relevant anatomical landmarks or key features influencing recommendations. Without such transparency, AI integration into high-risk spinal deformity surgery remains limited.³⁰

3.3. Integration into surgical workflow

Integrating AI into scoliosis surgery poses logistical and practical challenges. High costs; USD 500,000–1 million for AI-enabled navigation or robotic systems, plus maintenance; limit accessibility, particularly in low- and middle-income countries where severe rigid scoliosis is often diagnosed late.³¹

The learning curve is another barrier: introducing AI-assisted navigation or robotics can initially add 20–30 min to operative time, as surgeons balance reliance on AI with intraoperative judgment.³² Furthermore, many AI systems function as standalone modules and do not integrate seamlessly with existing hospital infrastructure such as PACS, navigation platforms, or EMRs, reducing efficiency and adoption.³³

3.4. Data privacy, ethics, and liability

AI requires large multicenter datasets, raising patient privacy concerns under HIPAA in the U.S. and GDPR in Europe.³⁴ Algorithmic bias is also a concern: many scoliosis datasets are derived predominantly from Western populations, potentially reducing accuracy in Asian or African cohorts and perpetuating inequities in care.³⁵

Legal liability for AI-assisted surgical errors remains unresolved. If an AI tool recommends a surgical plan that results in neurological compromise or hardware failure, it is unclear whether responsibility lies with the surgeon, the institution, or the software vendor.³⁶ Without clear frameworks, these medicolegal risks may deter surgeons from trusting AI systems fully.³⁷

3.5. Limited prospective validation

Although AI tools have shown promise in retrospective studies, prospective validation in severe rigid scoliosis is limited.³⁸ Most models are internally validated via cross-validation within single-center datasets, which may not reflect real-world performance in high-rigidity populations.

For example, predictive models for proximal junctional kyphosis have achieved retrospective AUC values up to 0.89, demonstrating strong predictive ability.¹⁷ However, these models remain largely untested in prospective multicenter trials involving severe rigid scoliosis patients. Without such validation, it is difficult to assess whether these algorithms maintain accuracy under the diverse and high-stakes conditions encountered in clinical practice.

Despite promising advances, AI applications in severe rigid scoliosis are not without technical limitations. One notable challenge is the reduced accuracy in highly rotated vertebrae. Deep learning models trained on typical deformities may fail to correctly segment vertebrae in cases with extreme axial rotation, leading to errors in Cobb angle estimation or surgical planning.

Similarly, fused or ankylosed spinal segments pose difficulties for AI algorithms, as standard segmentation and vertebral landmark detection assume distinct intervertebral boundaries. In fused curves, these boundaries may be obscured, decreasing the reliability of automated measurements and predictive models.

Annotation errors and inter-observer variability further constrain AI performance. Many supervised learning algorithms rely on manually labelled datasets, which can vary between radiologists or spine surgeons. Inconsistent labelling propagates errors into training, reducing generalizability, especially in rare, severe, rigid scoliosis cases where high-quality annotated datasets are limited. Addressing these failure modes requires robust data augmentation, multimodal imaging integration (CT, MRI, EOS), and hybrid approaches that combine AI predictions with expert oversight to ensure safety in high-stakes surgical environments.

4. The road ahead for AI in Severe rigid Scoliosis

AI has shown transformative potential in musculoskeletal imaging, surgical navigation, and predictive analytics, yet its application in severe rigid scoliosis remains nascent. Realizing this potential requires a multipronged roadmap encompassing technological innovation, clinical validation, ethical alignment, and global accessibility (Figure. 2).

Fig. 2.

Future direction and the road Ahead in Severe Rigid Scoliosis.

4.1. Building multicenter, high-quality datasets

A major barrier is the scarcity of representative datasets for rigid scoliosis. Large registries, such as SRS and Eurospine, include <10 % severe rigid deformities, limiting AI training robustness. Establishing global, multicenter registries dedicated to rigid scoliosis is critical.²⁷ These should capture standardized imaging (EOS, CT, MRI), operative notes, perioperative complications, and long-term outcomes.

Federated learning offers a solution to privacy challenges, training AI locally while sharing only model parameters, aligning with HIPAA/GDPR.³⁹ Synthetic augmentation using generative adversarial networks (GANs) can further expand dataset diversity by generating realistic radiographs or 3D reconstructions, overcoming limited sample sizes.⁴⁰

4.2. Development of explainable and trustworthy AI (XAI)

Transparency is essential for AI adoption in high-stakes surgical decisions, such as vertebral column resections or multi-level osteotomies. Deep learning “black boxes” alone are insufficient; surgeons require explainable outputs.⁴¹

XAI approaches; heatmaps highlighting critical radiographic regions, feature-importance rankings (e.g., pelvic tilt, curve stiffness), and hybrid models combining statistical rules with deep learning; can enhance trust. For instance, if AI recommends a pedicle subtraction osteotomy, it should show how thoracic kyphosis and lumbar flexibility influenced the decision, supporting both clinical confidence and educational feedback.

4.3. Seamless integration with surgical technologies

AI's future lies in synergy with advanced surgical tools. AI-integrated robotics could enable real-time adjustment of screw trajectories based on neuromonitoring, reducing neurological risk. Preliminary robotic systems achieve up to 98 % pedicle screw accuracy, but AI-driven adaptability could further enhance outcomes.⁶

Augmented and virtual reality (AR/VR) combined with AI could project planned trajectories and osteotomy sites directly into the operative field, reducing fluoroscopy and radiation exposure by up to 40 %.⁴² Digital twins; patient-specific biomechanical models updated in real time; could simulate corrective forces, predict spinal cord stress, and forecast complications, guiding safer rigid deformity corrections.⁴³

4.4. Cost-effectiveness and accessibility

AI-enabled navigation and robotics remain expensive (USD 500,000–1 million), though long-term savings may arise from reduced complications, revisions, and ICU stays.³¹ Global accessibility requires tiered solutions. Low-resource settings could use lightweight AI tools, such as smartphone-based Cobb angle detection, which already demonstrate >90 % accuracy versus radiologists, democratizing AI benefits beyond tertiary centers.⁴⁴

4.5. Ethical and policy frameworks

Medico-legal accountability must be clarified; determining responsibility among surgeons, institutions, or software vendors when AI informs surgical planning. Equity is also crucial; datasets skewed toward Western populations risk bias in Asian, African, or South American cohorts. International collaborations must ensure inclusivity.

Patient engagement is equally important. Patients should be informed when AI contributes to planning, with opportunities to opt in or out, maintaining transparency and autonomy.⁴⁵

4.6. Prospective clinical trials and benchmarking

Rigorous prospective validation is critical. Most current AI systems are retrospectively validated, and retrospective AUCs (0.89 for PJK prediction) may not reflect real-world outcomes in rigid scoliosis.¹⁷

Future trials should assess correction rates, complication rates, neurological safety, and health-related quality of life. International benchmark datasets; analogous to ImageNet in computer vision; would enable standardized algorithm comparisons, reduce redundant development, and accelerate safe clinical integration.⁴⁶

5. Comparative Perspective: rule-based vs. deep learning algorithms

Classic rule-based planning algorithms in scoliosis surgery operate on predefined clinical rules and mathematical models. Their primary advantage lies in transparency and interpretability, as surgeons can readily understand the rationale behind each recommendation. However, these systems have limited adaptability and may struggle to account for complex, patient-specific deformities, especially in severe rigid scoliosis. In contrast, deep learning models can identify intricate patterns and optimize predictions based on large datasets, offering higher accuracy in curve progression estimation, osteotomy selection, and rod contouring. The trade-off is reduced explainability: these “black-box” models provide outputs without intuitive reasoning, potentially limiting surgeon trust and highlighting the need for explainable AI approaches in high-stakes surgical decisions.

6. Limitations

This narrative review has several limitations that should be acknowledged. First, the availability of high-quality datasets specific to severe rigid scoliosis is limited, which restricts the scope of evidence on AI applications in this patient population. Second, much of the current literature is based on retrospective studies or single-center analyses, limiting the generalizability of findings and highlighting the need for prospective multicenter validation. Third, gaps remain in ethical, legal, and regulatory frameworks governing the use of AI in spine surgery, including considerations of algorithm transparency, patient privacy, and medicolegal accountability. Finally, as this review is narrative in nature, it may not capture all emerging AI tools or recent clinical trials. Despite these limitations, this manuscript aims to provide a comprehensive overview of current opportunities, challenges, and future directions for AI in severe rigid scoliosis. Despite these limitations, the insights presented in this review underscore the transformative potential of AI in severe rigid scoliosis and lay the groundwork for future research, prospective validation, and clinical integration.

7. Conclusion

Artificial intelligence holds transformative potential in the management of severe rigid scoliosis by enhancing imaging interpretation, surgical planning, intraoperative navigation, and outcome prediction. While current applications are promising, challenges in data availability, algorithm transparency, integration, and ethical considerations must be addressed before routine adoption. The future lies in collaborative, multicenter efforts to build robust datasets, development of explainable and surgeon-friendly AI systems, and seamless integration with robotics and AR/VR platforms. With careful implementation, AI can help shift the paradigm from reactive deformity correction to proactive, precision-driven scoliosis surgery.

Use of AI tools

The authors have used Grammarly for English editing and improving the manuscript's readability, but have rechecked its final contents and take full responsibility.

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Not applicable. This manuscript is a narrative review and did not involve human participants or animals.

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Author's contribution

(AR, BBN) have equally contributed to conceptualization, literature review, manuscript preparation, edits and final approval.

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Financial support and sponsorship

This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Competing interests

The author(s) declare no competing interests.