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Toossi et al. Art Int Surg 2024;4:258-66 https://dx.doi.org/10.20517/ais.2024.27 Page 262

queried from the ACS-NSQIP database, they found that ML algorithms outperformed American Society of
[29]
Anesthesiologists score in predicting individual risk prognosis .
Using conditional inference tree analysis, a team of investigators could predict blood loss and perioperative
[30]
blood transfusion in 909 ASD patients undergoing surgery . The artificial neural network was used to
predict perioperative blood transfusion after ASD corrective surgery in 1,173 cases identified from the
[31]
NSQIP database between 2017 and 2019, with 81% accuracy . Furthermore, another group of researchers
found no difference between random forest and tree-based ML models to predict blood transfusion
[32]
following ASD corrective surgery in 1,029 patients .
A team of researchers used ML-based predictive models to estimate the likelihood of overall improvement
and surpassing the minimal clinically important difference (MCID) following adult spinal deformity (ASD)
surgery, testing their models with eight patient-reported outcome measure instruments. The models could
predict accurately and consistently whether a procedure would achieve MCID for a given patient using a
given outcome instrument across a given time interval [33,34] .

Postoperative care
After ASD surgery, ML can aid in monitoring patients’ recovery and predicting potential complications. By
analyzing postoperative imaging and clinical data, ML models can identify early signs of implant failure,
[35]
[36]
infection, or other complications, allowing for timely intervention . Since ASD surgery is fraught with
complications postoperatively, many different characterizations have been developed to predict the
complications after the surgery or determine risk profiles for the development of complications following
deformity correction. The success of computer vision, large language models, and genome-wide association
(incorporating advanced ML technologies) in predicting various complications in a cohort of ASD patients
has been shown recently by a group of investigators . Major medical complications, discharge to a facility,
[37]
and 90-day readmission were predicted using ML methods with decent accuracy [38,39] .

ML can also assist in predicting long-term complications, such as the risk of adjacent segment degeneration
and PJK, and help surgeons and patients make informed decisions about follow-up care. Korean
investigators recently developed and verified an online calculator for predicting PJK risk following ASD
surgery using a ML model. They based their study on the radiographic outcomes obtained from 16 surgical
centers . Moreover, to predict mechanical complications following ASD surgery, some investigators tried
[40]
different ML models and found that random forest had the best prediction accuracy of 73.2% .
[41]
Additionally, in a postoperatively well-aligned group of 244 patients following ASD surgery, some
researchers could predict the mechanical complications with moderate accuracy (74%) using extreme
gradient boosting ML algorithms. The mechanical complications investigated were: proximal junctional
kyphosis and failure, distal junctional kyphosis and failure, rod breakage, and implant-related
complications . Extreme gradient boosting is a technique that builds a strong predictive model by
[42]
combining several weaker models, learning from mistakes, and doing so in a very efficient way.

Lovecchio et al. used decision tree analysis to predict the risk of proximal junctional failure and PJK by
studying pre-discharge standing radiographs of 117 ASD patients . A group of Korean investigators could
[43]
identify risk factors for unplanned readmission after ASD in 210 patients and predict it using a ML
model .
[44]

Some researchers used a conditionally unbiased regression tree and random forest algorithm to predict cost
outliers in ASD correction up to 2 years after the index surgery in 210 patients . Conditionally unbiased
[45]