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Page 261 Toossi et al. Art Int Surg 2024;4:258-66 https://dx.doi.org/10.20517/ais.2024.27

[17]
expectations and perceptions of the corrective surgery clinical outcomes . Mekhael et al. used a random
forest ML model to accurately predict health-related quality of life outcomes after ASD surgery in 173
patients. They found that three-dimensional movement assessment of ASD patients can better predict
clinical outcomes than stand-alone radiographic parameters, not only for physical but also for mental
scores . Random forest is an ensemble learning method that constructs multiple decision trees during
[18]
training and merges their results to improve accuracy and control overfitting.

In another study, the researchers used a “ML model based on random forest regression and a systematic
decision tree-like approach” to predict health-related quality of life scores, gait kinematics, and spatial-
temporal parameters based on radiographic global alignment parameters . They found that Global Sagittal
[19]
[20]
[21]
Angle and T9 tilt were the best predictors of joint kinematics and health-related quality of life scores
based on the results from 127 primary ASD patients and 47 controls.

Conditional inference tree run ML analysis was used to identify the baseline threshold for different
radiographic parameters to achieve a good outcome following ASD surgery in 431 patients. These
parameters were: sagittal vertical axis, pelvic incidence-lumbar lordosis mismatch, pelvic tilt, T1 pelvic
angle, L1 pelvic angle, L4-S1 lordosis, C2-C7 sagittal vertical axis, C2-T3, C2 slope . Conditional inference
[22]
tree is a type of decision tree used in ML that addresses some of the biases found in traditional decision
trees. It helps to make decisions by asking questions about data and splitting it accordingly, aiming
to improve predictions and analyses.

Aiming to preoperatively predict proximal junctional kyphosis (PJK) after ASD corrective surgery in 191
patients, a team of researchers included preoperative thoracic T1 MRIs in a deep learning ML model
(convolutional neural network) to increase the accuracy of the prediction . Using a large prospective
[23]
multi-center database, a group of investigators constructed a supervised ensemble of decision trees to
preoperatively predict the risk of pseudarthrosis at 2 years after ASD surgery in 336 patients with 91%
[24]
accuracy .
Intraoperative guidance
During surgery, ML algorithms can provide surgeons with real-time guidance, enhancing the accuracy of
instrument placement and overall surgical technique. By integrating with navigation systems, ML can track
the position of surgical instruments relative to the patient’s anatomy, ensuring precise correction of spinal
deformity. Using ML methodology, Burström et al. were able to accurately place pedicle screws during CT-
[25]
based navigation . Preplanning the pedicle screw trajectory using the ML system has yielded highly
accurate results . ML can also be used to analyze intraoperative data, such as neuromonitoring signals, to
[26]
alert surgeons of potential complications, such as nerve injury, enabling prompt intervention. Real-time
automated decision-making systems regularly integrate inputs from intraoperative neuromonitoring and
the operating room environment, utilizing predictive models to generate instructions or warnings for the
surgical team. These systems continuously update their predictive models and decision-making processes
based on new data and feedback from the surgeon and neurophysiologist, ensuring adaptive and accurate
responses during surgery .
[27]
Using perioperative data, ML-based risk calculators can predict the 30-day complication and mortality risk
following ASD corrective surgery in 9,143 patients from The American College of Surgeons National
Surgical Quality Improvement Program (ACS-NSQIP) database . Kim et al. could use ML algorithms to
[28]
predict mortality and medical complications following ASD surgery. Using the data of 4,073 patients