AI-Driven Risk Stratification in Spine Surgery

Artificial Intelligence (AI) is a broad field of machine-driven problem-solving, data analysis, and pattern recognition, which aims to mimic traditionally human cognition.[1] As a subset of AI, machine learning is focused on simulating human processing mechanisms using data input and algorithms, which formulate specialized systems that can predict specific outcomes.[1,2] Since its development in the 1950s, AI has rapidly expanded our knowledge and resources to better understand the world by sifting through large volumes of data to identify patterns and outliers and solve difficult problems.[1]

The widespread availability of AI systems such as Chatsonic or ChatGPT has allowed the public to incorporate AI into everyday life, including AI-powered assistants, fraud protection, personalized learning, and autonomous vehicles. These systems are typically used as an enhancement tool of skilled professionals, rather than a replacement of human workflow. As an adaptable and available tool, its use increases efficiency by increasing productivity, reducing time and capital expenditure, and provides information for a plethora of areas such as economic, social, and governmental structures.[3]

As AI rapidly advances, many professionals show concern regarding its future applications and the possibility of the outright replacement of human labor. While these concerns are valid, we aim to reinforce the grand benefits of utilizing AI as an advancement tool regarding risk stratification in spine surgery.

Conventional vs AI Risk Stratification

Spine surgery risk assessment remains challenging. Conventionally, multiple risk assessment tools have been validated using patient factors and comorbidities. Certain metrics, including the American Society of Anesthesiologists classification, modified Charlson Comorbidity Index, and modified Frailty Index, have been retrospectively identified as important contributors for predicting surgical risks.[4-6] However, these tools vary in accuracy and clinical applicability. For instance, Pulido et al demonstrated that adverse events can be predicted by the Modified Frailty Index, yet Lakomkin et al ascertained contrasting results.[4,5] Additionally, Lakomkin et al established that the Charlson Comorbidity Index demonstrated superior predictive capacity over the Modified Frailty Index, but it could be used to predict mortality and length of hospital stay.[5] While some established clinical guidelines and existing risk calculators, such as the American College of Surgeons National Surgery Quality Improvement Program (ACS NSQIP) Surgical Risk Calculator, may help guide treatment decisions, risk stratification heavily relies on the expertise of the spine surgeon. Furthermore, although preoperative metrics provide valuable insights, these may be limited by human biases and a lack of adaptability.

Recently, AI-driven risk calculators have been developed as an alternative to these traditional risk stratification assessment tools and have been shown to have a greater effect toward improving patient outcomes over conventional risk stratification methods.[7]

Machine-learning algorithms, including decision trees and support vector machines, can analyze extensive datasets, including radiographical imaging, to identify patterns and risk factors that are often not observable by humans.[8,9] This approach evaluates pre-established input and output variables to predict the outcome variable(s) from patients with similar input data.[9] These novel algorithms can also “learn” to adapt and improve using additional patient data or patient variables to improve their reliability in determining precise risk assessments and surgical candidacy.[7] Arvind et al demonstrated that machine learning outperformed more conventional risk stratification tools, including the American Society of Anesthesiologists classification, in predicting surgical complications for anterior cervical discectomy and fusion patients ( p < 0.05).[10] However, the reliability of these algorithms entirely depends on the reliability of the incorporated dataset and differed between algorithms.[10,11]

Use of AI in Risk Stratification

AI risk stratification can occur throughout each stage of a patient’s journey—preoperatively, intraoperatively, and postoperatively—and may also assist in the location and grading of spinal pathology at the time of diagnosis.[12]

Decision trees are widely utilized predictive models that support clinician decision-making, typically toward identifying the optimal management method for specific patients.[13] Their use has been demonstrated to be an effective and accurate tool to determine the optimal management method in spinal cord injuries[14] and the risk classification of curve progression in scoliosis.[15] By supplementing the decision-making process, clinicians spend less time determining management options, thus increasing workflow efficiency.[12] Similar models may also be used to identify optimal surgical candidates and to highlight modifications in surgical approach to further benefit patient outcomes. Examples include prediction models that identify the surgical intervention with the greatest success rate and lowest risk of patient complications in adult spinal deformity patients,[16] models which predict failure risk in the holding power of pedicle screws in lumbar fusion,[17] and prediction of nonhome discharge following elective anterior cervical discectomy and fusion.[18] Along with AI, navigation and robotics have also become a frequently incorporated tool within spinal surgery.[19] It is expected that AI, navigation, and robotics are likely to amalgamate to increase surgical accuracy, minimize iatrogenic complications, and improve patient postoperative outcomes by accounting for patient anatomical variations.[19,20]

AI predictive models can be utilized preoperatively to assess patients’ perioperative complication risk before undergoing surgery. Shah et al successfully developed the first predictive risk calculator for C5 nerve root palsy after instrumented cervical fusion.[21] In separate studies, Karhade et al devised algorithms that accurately predict mortality in patients with spinal epidural abscesses and those with spinal metastatic disease.[22,23] Moreover, they also developed a predictive algorithm to identify patients at risk of prolonged postoperative opioid use after lumbar disc herniation surgery.[24] Assessing risks preoperatively allows for tailored patient care aimed at minimizing potential complications. Furthermore, when surgical eligibility is unclear, AI can offer additional insights to assist surgeons in making informed decisions.

The benefit of AI in spine surgery is evident. However, it is still in the early stages of development. AI can be used as a clinician’s aid to improve patient care through risk stratification but is certainly not suitable to replace the clinician. If used effectively, AI reduces surgeon workload, freeing the surgeon to have more operative time. Subsequently, AI improves the surgical team’s efficiency, allowing more patients with spinal pathology to be treated and reducing healthcare expenditure without compromising patient care.[19] Despite this, AI risk stratification has its limitations. Initially, AI models require a large dataset to be trained and validated, which takes considerable time and labor. Additionally, these datasets are typically institution specific. Therefore, while the model may be effective, its benefit may not be transferable when incorporated to different patient populations.

To overcome this limitation, compilation of patient data between institutions must take place to prevent the overfitting of relatively small datasets and to prevent risk stratification and treatment recommendation bias.[8,25,26] However, patient data privacy regarding the distribution of confidential patient information are great security concerns. Therefore, methods that can accomplish this safely must be implemented. Furthermore, developed models require external validation to establish their bias, calibration, and overall clinical application before being employed to an independent patient population from another institution. [23,27,28] Despite this, external validation of institution-specific models is sparse, further limiting their widespread clinical application.[23] Groot et al found that most predictive machine-learning models lack external validation.[29] Hence, collaboration among institutions and validation from external sources are crucial for the advancement of AI in spine surgery.

Furthermore, AI use in spinal surgery is still in the early stages of development and will take considerable time before being implemented further. Nonetheless, even at the current developmental stage, spinal surgeons are likely to trust AI risk prediction models and believe that its use will be integrated within half a decade.[30] However, AI and ML advancement is occurring at a greater rate than the generational turnover of orthopedic surgeons.[8] Therefore, orthopedic surgeons are required to continuously educate themselves throughout their career to maximize the benefit of this technology.[8] Moreover, while many professionals fear the possibility that AI will replace their job, most orthopedic surgeons do not have this belief because of the vital doctor-patient relationship.[31] Accordingly, while AI is superior in evaluating medical datasets, it can only evaluate the inputted measured variables; in contrast, surgeons are able to evaluate a patient’s medical history and social and personal factors through surgical consultations; therefore, they are able to take all aspects of a patient’s pathology into consideration.

Conclusion

Artificial intelligence has enormous potential to reform spinal surgery care and patient optimization. Although AI use in spinal surgery is relatively new, it has been shown to be an effective tool in preoperative workup, patient selection, outcome prediction, and perioperative assistance. AI-driven decision support tools offer a promising avenue for more accurate, personalized, and scalable risk assessments with the potential to enhance patient outcomes and surgical decision-making. However, as AI develops, one must consider the large patient dataset needed for universal use and the data privacy and security risks during development.

References

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Authors: Myles R.J. Allen, MBChB

Ashley Yeo Eun Kim, BA

Olivia C. Tuma, BS

Tomoyuki Asada, MD

Sravisht Iyer, MD

From the Hospital for Special Surgery in New York, New York.