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Page 325 Turlip et al. Art Int Surg 2024;4:324-30 https://dx.doi.org/10.20517/ais.2024.29

INTRODUCTION
The integration of artificial intelligence (AI) into spine surgery has given rise to significant improvements in
[1]
patient safety, peri-operative decision making, and clinical outcomes . As new technological innovations
herald faster, more efficient, and more accurate AI models, it is imperative for surgeons to understand the
impact of AI on current treatment paradigms and where spine surgeons’ focus should lie as we assist in the
development of AI-enabled personalized and precision medicine.

At the cornerstone of clinical advancement with AI are machine learning (ML) models, capable of
identifying and extracting patterns from large datasets and making predictions based on learned trends. As
the availability of data grows, ML model performance continues to improve; therefore, the advancement of
AI in medicine is uniquely tied to our ability to provide these models with accurate and pertinent
datapoints. In this perspective, we provide a brief historical outline of current ML and AI applications in
spine surgery. We then offer our thoughts on where the future of AI and spine surgery lies, and how the
unique relationship between model accuracy and data volume will shape the future of how AI is
implemented in clinical contexts.

CURRENT AI APPLICATIONS IN SPINE SURGERY
One of the earliest and most compelling uses of ML in spine surgery has been the use of models to
automatically decipher radiographic images. For example, the classification of lumbar disc degeneration
from 2-dimensional magnetic resonance image (MRI) using ML has now reached levels comparable to
[1-3]
expert radiologists . The morphology of the discs is first described according to their pathological features
and classified according to the standardized grading system proposed by Pfirrmann et al. . A convolutional
[4]
neural network (CNN) is then used to extract image features from the training data set to make predictions
based on the radiologists’ interpretations. CNNs, a specialized subtype of deep learning (DL) algorithms,
parallel the architecture of human visual cortex processing and rely on unsupervised pattern recognition to
classify images. CNN-based models for image classification are typically validated through a combination of
k-fold cross-validation on training data and then tested on independent and external datasets to ensure
generalizability. Other groups have also explored the use of generative models to create image-to-image
translations of the musculoskeletal system . Clinically, this can provide a means to correct poor image
[5,6]
resolution or blurriness due to patient motion during image acquisition.

As DL algorithms became more prevalent, they have gradually been implemented to automatically
determine spinal landmarks to calculate deformity parameters. DL models are trained on large datasets to
identify and classify complex phenomena through non-linear analysis in artificial neurons, similar in
structure to the mammalian brain . The automated analysis of the Cobb angle to describe the severity of
[7]
scoliotic curvature has been addressed through several DL techniques [8-10] . Korez et al. also used DL to
identify anatomical landmarks in X-ray images and measure spinopelvic parameters, finding no difference
between DL and manual identification .
[11]

The transformative capability of AI can expedite diagnosis and treatment planning, and has the potential to
standardize surgical treatment strategies for various spinal pathologies after taking patient-specific factors
into account. Widespread implementation, however, faces substantial ethical challenges as the prospect of
removing human interpretation may lead to more patient distrust in conclusions. It is unlikely, then, that
human radiologists will be replaced by AI technology; instead, their diagnostic accuracy will be improved as
models continue to advance.