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Toossi et al. Art Int Surg 2024;4:258-66 https://dx.doi.org/10.20517/ais.2024.27 Page 260

Figure 1. An overview of the most common applications of ML in ASD. ML: Machine learning; ASD: adult spinal deformity.

ML algorithms can analyze preoperative imaging studies, such as X-rays, CT scans, and MRI scans, to
[5-7]
provide detailed insights into the patients’ spinal alignments . This includes assessing the degree of
deformity, identifying critical structures, and predicting the optimal surgical approach . ML models can
[8]
also assist in selecting the appropriate implants (like pre-bent patient-specific rods) and predicting the
postoperative spinal alignment, helping surgeons customize their surgical plan for each patient [9,10] . Using
ML algorithms, a group of investigators could accurately predict spinopelvic parameters and thoracic
[11]
kyphosis after deformity correction surgery in 20 adult patients with spinal deformity . ML models can
preoperatively be used to estimate the likelihood of extended length of stay following ASD surgery [12,13] .
Thus, the surgeon can optimize modifiable risk factors, enhance preoperative planning, and manage
patients’ expectations. Other investigators have developed predictive models to estimate the risk of
[14]
rehabilitation discharge for adult patients undergoing elective surgeries, including ASD patients .

Lafage et al. used artificial neural network based on preoperative data and alignment goals to accurately
(81%) predict the upper instrumented vertebra (UIV) in a series of 143 ASD patients. This study showed
how “to employ a neural network to mimic surgeon decision-making for UIV selection” . A neural
[15]
network is a type of ML model inspired by the human brain’s structure and functioning. It consists of
interconnected nodes or neurons organized into layers: an input layer, one or more hidden layers, and an
output layer. Each connection between neurons has a weight that adjusts during training to minimize error.

Additionally, prognosis can be predicted by using ML algorithms to identify different patient phenotypes
preoperatively. In a recent prospective multi-center study on 570 ASD patients conducted by European and
US-based Spine Study Group, investigators could identify three different qualitative preoperative
phenotypes in ASD patients based on demographics, surgical history, frailty, radiographic measures, and
patient-reported outcome measures. These phenotypes had been identified through unsupervised machine-
based clustering. Based on these phenotypes, one can augment preoperative decision-making, predict the
[16]
clinical outcome of deformity surgery (prognostic values), and tailor treatment approaches .
An international team of researchers used a predictive ML model preoperatively to predict the individual
answers to the Scoliosis Research Society-22R (SRS-22R) questionnaire at 1 and 2 years after ASD surgery in
561 patients. This prediction provides the patients with reasonable preoperative counseling based on their