Artificial Intelligence (AI) and Machine Learning (ML) are significantly transforming spinal fusion procedures by offering advanced tools for diagnosis, treatment planning, and surgical precision, while also presenting important considerations regarding ethics and data management.

AI in Spinal Fusion Procedures

Spinal fusion, including Posterior Cervical Fusion (PCF) and Lumbar Fusion, aims to stabilize the spine and alleviate pain caused by various conditions such as cervical instability, degenerative diseases, or spinal deformities1…. AI and ML are central to enhancing these complex procedures.

In Posterior Cervical Fusion (PCF):

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PCF is used to treat cervical instability and other cervical pathologies that can lead to pain, disability, and neurological dysfunction13.

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While Lateral Mass Screws (LMS) are common, Cervical Pedicle Screws (CPS) offer safe and effective fixation in cervical spine reconstructions, though misplacement can cause serious injury to the vertebral artery, nerve root, or spinal cord4. Conventional fluoroscopy has shown misplaced screw rates between 6.7% and 29.1%4.

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3D navigation, a reliable AI-driven tool, improves efficiency and screw accuracy in posterior cervical surgical approaches5. It is associated with a significantly lower risk of pedicle perforation compared to non-navigated methods5.

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The use of Stealth-Midas™ High-Speed drilling system with navigation leads to a lower number of misplaced screws and minimizes unwanted cervical movements5.

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ML models have been developed to predict short-term postoperative outcomes following PCF surgery, such as prolonged length of stay, non-home discharges, and 30-day readmissions6…. These models aim to improve risk assessment and prognosis10.

In Lumbar Fusion:

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AI is revolutionizing lumbar fusion by enabling the creation of personalized interbody devices211. Standard prefabricated implants may not precisely match a patient’s unique 3D vertebral topography, leading to decreased implant stability, risk of subsidence (erosion into bone), and imprecise correction of spinal deformities212.

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Using a patient’s CT scan, AI helps to create 3D images of the lumbar spine, determine optimal spinal alignment, and design personalized devices that perfectly fit the patient’s bone shape and surgical goals12…. These devices are fabricated using 3D printing technology1215.

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Deep-learning 3D lumbar spine MRI with “CT-like” contrast can be effectively used for virtual pedicle screw planning and geometric measurements in robotic-navigated spinal surgery, showing equivalency to CT for most lumbar vertebrae16…. This supports pre-operative planning in patients considered for robotic-navigated spine surgery16.

Advantages of AI in Spinal Fusion

AI offers numerous advantages across various stages of spinal care, from diagnosis to rehabilitation:

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Enhanced Surgical Precision and Accuracy19…:

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AI-guided navigation systems provide real-time feedback during surgery, allowing for small incisions and precise surgical corridors that minimize tissue disruption20.

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Robotic-assisted spine surgery improves precision and patient care, guiding surgeons to ensure accurate alignment and reducing human error21.

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It leads to a lower number of misplaced screws and minimizes unwanted cervical movements in PCF5.

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Personalized 3D-printed devices for lumbar fusion ensure a perfect fit for the patient’s anatomy, promoting optimal biomechanical stability and successful fusion1214. This eliminates the imprecision of traditional implants and can get the exact planned correction, especially in complex minimally invasive scoliosis surgery1215.

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Improved Diagnostics and Treatment Planning19…:

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AI provides advanced diagnostic tools for more accurate detection of spinal conditions and personalized treatment plans19.

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Machine learning processes medical data to improve diagnostics for conditions like herniated discs and scoliosis23.

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AI-guided surgical planning uses 3D spinal imaging to pinpoint issues, reduce misdiagnosis, and recommend personalized, minimally invasive treatment options23.

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Predictive models can guide treatment plans and foresee patient outcomes, driving a shift towards more personalized care25.

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Reduced Risks and Complications19…:

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AI-powered technologies reduce surgical risks and improve patient outcomes19.

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The use of O-arm™ imaging and Stealth™ Navigation systems is associated with a significant reduction in intraoperative blood loss5.

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No neurovascular injuries were reported in cervical pedicle screw fixation procedures performed using O-arm™ Imaging and Stealth™28. This is crucial as CPS misplacement can injure the vertebral artery, nerve root, or spinal cord4.

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AI can help reduce the risks of post-operative complications27. For example, AI-supported surgery planning tools can reduce the risk of complications following an osteotomy from 22% to 4.7%27.

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Personalized devices for lumbar fusion decrease the risk of subsidence and lower the risk of reoperation due to non-fusion or loosening of screws/rods12.

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Increased Efficiency and Reduced Operative Time520:

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3D navigation improves efficiency in posterior cervical surgical approaches5.

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Operative time can be reduced when using 3D navigation compared to fluoroscopy-guided procedures28.

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Reduced Radiation Exposure28:

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3D navigation significantly reduces the total radiation exposure time for both the patient and the operative team28.

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Enhanced Recovery and Post-operative Care24…:

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AI systems revolutionize post-operative care by developing personalized recovery protocols that integrate patient age, health status, and surgical parameters29.

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The technology continuously monitors patient progress, allowing for real-time adjustments to physical therapy and medication management29.

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AI can streamline administrative tasks like scheduling, billing, and patient follow-ups, and provide real-time data analytics for practice performance and patient needs31….

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Intraoperative Verification:

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Medtronic’s Surgical Synergy (Stealth™ Navigation, O-arm™ Imaging, and Stealth-Midas™ with Infinity™ or Vertex Select™ systems) allows the surgeon to plan, implant, and verify the success of the surgical procedure before the patient leaves the operating room28.

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Pre-operative Simulation and Clinical Decision-Making2734:

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Advanced imaging algorithms can create detailed 3D surgical simulations, allowing surgeons to practice complex procedures beforehand2735.

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AI systems can predict potential complications and suggest preventive measures based on analysis of thousands of similar cases27.

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ML models can predict whether patients with Adult Spinal Deformity (ASD) are managed operatively vs. non-operatively with 86% accuracy3436.

Risks and Ethical Considerations of AI in Spinal Fusion

Despite its significant advantages, the integration of AI in spinal fusion and other surgical procedures introduces several critical risks and ethical challenges:

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Data Privacy and Security37…:

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AI in healthcare heavily relies on large amounts of patient data37….

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Ensuring data is securely stored, used responsibly, and protected from misuse is critical for maintaining trust and confidentiality37…. Compliance with regulations like HIPAA is mandated38. Failure to do so can lead to privacy infringements and legal issues3840.

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Algorithmic Bias and Equity in Healthcare37…:

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Bias in training datasets can lead to disparities in care among different demographic groups3942.

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AI technologies should be accessible to all patients, regardless of socioeconomic status, to promote equity and address healthcare disparities3739. If advanced technology is only accessible to affluent hospitals, it could exacerbate quality divides44.

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AI recommendations based on biased data could result in suboptimal or detrimental surgical planning43.

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Human Oversight and Autonomy (Surgeon’s Judgment)45…:

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While AI enhances decision-making, human expertise remains essential4546.

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Surgeons and healthcare professionals must combine AI recommendations with their own judgment and patient preferences4546.

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There is a risk of over-reliance on technology, which could undermine surgeons’ autonomy and judgment4751. The burden of the ultimate decision and its ramifications remains with the surgeon49.

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AI operates based on algorithms and data, lacking inherently human elements like emotions, cultural contexts, or moral beliefs, which are fundamental to medical ethics48.

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Accountability and Liability37…:

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If an AI-driven diagnosis or treatment recommendation proves erroneous, or if an autonomous surgical robot causes a complication, determining who bears responsibility (healthcare provider, AI developers, or institution) becomes complex37…. This requires a comprehensive legal and ethical framework52.

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Data Quality and Limitations of Models53…:

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The performance of ML models hinges on the quality and diversity of the training data53…. Inaccurate, incomplete, or biased data can lead to flawed models and misguided clinical decisions46.

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Large national databases like NSQIP, while providing vast samples, may lack highly granular or surgery-specific data, limiting model performance53….

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A common pitfall is target leakage, where models use data not available at the time of prediction, leading to inflated and false results59.

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Interpretability and “Black Box” Algorithms3960:

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Many ML algorithms are “black box” models, meaning their internal workings and how they arrive at a prediction can be difficult to conceptualize or explain3960. This lack of transparency poses a challenge in clinical settings, especially when explaining decisions to patients39.

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Generalizability and External Validity6061:

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AI models must be thoroughly evaluated and validated using data not involved in their training to ensure their generalizability and reliability in real-world clinical deployment61. Models that perform well on training data may not perform well on new data without proper validation60.

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Cost and Accessibility62:

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Implementing AI technologies may require significant investment in new systems, which can be cost-prohibitive for smaller healthcare organizations62.

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Patient Education and Informed Consent41…:

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Patients must be comprehensively informed about the utilization of their data by AI systems and the influence of AI on their surgical treatment in a clear and accessible manner to ensure meaningful consent4143.

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Correlation vs. Causation64:

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ML algorithms are designed to identify patterns and correlations in data, but these correlations do not necessarily imply causation64. This distinction is important for clinical understanding and decision-making64.

In conclusion, AI is revolutionizing spinal fusion by significantly enhancing precision, patient outcomes, and surgical efficiency, particularly through 3D navigation and personalized implants. However, its responsible integration necessitates careful consideration of data privacy, algorithmic bias, the critical role of human oversight, and clear accountability frameworks