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Page 2 Glaser et al. Art Int Surg. 2025;5:1-15 https://dx.doi.org/10.20517/ais.2024.36

Conclusion: Deep learning demonstrates promising accuracy for automated spinopelvic measurement, potentially
rivaling experienced human readers. However, further optimization and rigorous multicenter validation are required
before clinical implementation. These technologies may eventually improve the efficiency and reliability of
quantitative spine image analysis.

Keywords: Deep learning, spine parameters, pelvic parameters

INTRODUCTION
Quantitative evaluation of spine and pelvis anatomy has long interested clinicians and researchers in fields
such as orthopedics, neurosurgery, and radiology. Assessing sagittal spinal balance - the geometric
relationships between spinal curves and pelvic parameters - is considered essential for understanding
[1]
normal posture and alignment . Sagittal balance encompasses important radiographic measures such as
cervical and lumbar lordosis, thoracic kyphosis, pelvic tilt (PT), pelvic incidence (PI), and sacral slope
[2,3]
(SS) . Abnormal spinopelvic alignment has been associated with pain, disability, and poor health
outcomes .
[4]
Traditionally, spinopelvic parameters were manually measured from plain radiographs using techniques like
the Cobb method, with known limitations in accuracy and objectivity . Computer-assisted analysis tools
[5]
later emerged to potentially improve measurement consistency, though substantial human input was still
[6]
required . Deep learning has rapidly advanced in recent years but traces its origins back decades. The
concepts of neural networks were initially developed in the 1950s and 60s. However, computational power
limited applications. In the 1980s and 90s, techniques like convolutional neural networks (CNNs) were
pioneered, laying the groundwork for modern deep learning. Major advancements in computing, along
with the availability of large datasets, then enabled deep neural networks to surpass previous benchmarks
across diverse tasks. Beginning in the 2010s, deep learning achieved remarkable performance in computer
vision, natural language processing, and medical imaging analysis. The latest methods like CNNs now offer
transformative opportunities to extract information from complex data. Over the past decade, advances in
artificial intelligence and machine learning have enabled more automated approaches for quantitative
radiology and medical imaging .
[7,8]

Machine learning utilizes statistical models trained on known data to recognize patterns in new data . Deep
[9]
learning is a subset of machine learning based on layered neural networks that can automatically learn
optimal features directly from raw data, unlike traditional techniques requiring hand-crafted feature
[10]
engineering . The latest deep learning methods have become integral for the automated analysis of medical
images across specialties [11,12] , including quantitative characterization of spine disorders from radiographs
and CT scans [13,14] .

Several recent studies have applied deep CNNs for automated measurement of key spinopelvic parameters
from standard radiographs . Reported accuracy has been promising but varies widely across studies.
[15]
However, a comprehensive synthesis of the latest achievements, methodological innovations, and measured
performance has been lacking. This review aims to systematically summarize and critically appraise the
existing literature on deep learning-based assessment of sagittal spinopelvic alignment on radiographs. It
elucidates the current state of the field and future directions to potentially improve clinical adoption.