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Page 263 Toossi et al. Art Int Surg 2024;4:258-66 https://dx.doi.org/10.20517/ais.2024.27

regression tree is a type of decision tree used for making predictions, particularly when numbers or
continuous data are studied. It avoids bias by splitting data into different groups based on certain
conditions.

Challenges and future directions
Despite its potential benefits, the integration of ML into spinal deformity correction surgery faces several
challenges. These include the need for large, high-quality datasets, the interpretability of ML models, and
the ethical and regulatory implications of ML algorithmic decision-making [46,47] . Some investigators have
suggested using biological samples (muscle and bone sampling, assessment of circulating biomarkers,…) to
improve the accuracy of ML predictions in the future [48,49] . Furthermore, most current studies employ a
random split approach, in which the majority (70%-90%) of the available data are used for training the
model, while the remaining 10%-30% for testing its performance. This approach is not generally deemed
[50]
sufficient for the aim of “external” validation . Moreover, the extent to which ML-based predictions
meaningfully affect clinical decisions and practices in real life has yet to be investigated. Future research
should focus on addressing these challenges, as well as exploring new applications of ML, such as
personalized surgical planning and robotic-assisted surgery, to further improve patient outcomes.

For spine surgeons embarking on ML collaborations, key considerations include:

1. Data quality and privacy: ensure high-quality, well-annotated data while adhering to patient privacy
regulations like HIPAA.
2. Interdisciplinary communication: foster clear communication between clinicians and data scientists to
bridge the gap between medical expertise and technical execution.
3. Clinical relevance: focus on models that address specific clinical challenges, ensuring they provide
actionable insights in surgical planning, outcome prediction, or patient monitoring.
4. Validation and bias: rigorously validate ML models in diverse clinical settings to avoid biases and ensure
generalizability.
5. Regulatory compliance: stay informed about the evolving regulatory landscape for AI in healthcare to
ensure compliance with FDA or other relevant bodies.

These considerations are crucial for developing impactful and ethical ML solutions in spine surgery.

CONCLUSION
ML has the potential to revolutionize spinal deformity correction surgery by enhancing preoperative
planning, intraoperative guidance, and postoperative care. By leveraging the power of large datasets and
advanced algorithms, ML can assist surgeons in achieving more precise and personalized surgical outcomes,
ultimately benefiting patients with spinal deformities.

DECLARATIONS
Authors’ contributions
Made substantial contributions to the conception and design of the study and drafted the manuscript:
Toossi N
Performed literature search, as well as providing administrative, technical, and material support: Jerry O

Availability of data and materials
Not applicable.