Machine Learning and Deep Learning for Diagnosis of Lumbar Spinal Stenosis: Systematic Review and Meta-Analysis

Abstract

Background: Lumbar spinal stenosis (LSS) is a major cause of pain and disability in older individuals worldwide. Although increasing studies of traditional machine learning (TML) and deep learning (DL) were conducted in the field of diagnosing LSS and gained prominent results, the performance of these models has not been analyzed systematically.

Objective: This systematic review and meta-analysis aimed to pool the results and evaluate the heterogeneity of the current studies in using TML or DL models to diagnose LSS, thereby providing more comprehensive information for further clinical application.

Methods: This review was performed under the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines using articles extracted from PubMed, Embase databases, and Cochrane Library databases. Studies that evaluated DL or TML algorithms assessment value on diagnosing LSS were included, while those with duplicated or unavailable data were excluded. Quality Assessment of Diagnostic Accuracy Studies 2 was used to estimate the risk of bias in each study. The MIDAS module and the METAPROP module of Stata (StataCorp) were used for data synthesis and statistical analyses.

Results: A total of 12 studies with 15,044 patients reported the assessment value of TML or DL models for diagnosing LSS. The risk of bias assessment yielded 4 studies with high risk of bias, 3 with unclear risk of bias, and 5 with completely low risk of bias. The pooled sensitivity and specificity were 0.84 (95% CI: 0.82-0.86; I²=99.06%) and 0.87 (95% CI 0.84-0.90; I²=98.7%), respectively. The diagnostic odds ratio was 36 (95% CI 26-49), the positive likelihood ratio (LR+) was 6.6 (95% CI 5.1-8.4), and the negative likelihood ratio (LR-) was 0.18 (95% CI 0.16-0.21). The summary receiver operating characteristic curves, the area under the curve of TML or DL models for diagnosing LSS of 0.92 (95% CI 0.89-0.94), indicating a high diagnostic value.

Conclusions: This systematic review and meta-analysis emphasize that despite the generally satisfactory diagnostic performance of artificial intelligence systems in the experimental stage for the diagnosis of LSS, none of them is reliable and practical enough to apply in real clinical practice. Further efforts, including optimization of model balance, widely accepted objective reference standards, multimodal strategy, large dataset for training and testing, external validation, and sufficient and scientific report, should be made to bridge the distance between current TML or DL models and real-life clinical applications in future studies.

Trial registration: PROSPERO CRD42024566535; https://tinyurl.com/msx59x8k.

Keywords: AI; LSS; ML; artificial intelligence; deep learning; diagnosis; diagnostic; early detection; lumbar; lumbar spinal stenosis; machine learning; older adult; predictive model; spine stenosis.

Figure 1

Flowchart depicting PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) search strategy.

Figure 2

Methodological assessment by Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). (A) The proportion of risk of bias for all domains and proportion of applicability concerns in three domains. (B) Summary of the risk of bias for each study. Green, yellow, and red circles indicate low, unclear, and high risk of bias, respectively [8,20,22,24-28,30,32-34].

Figure 3

Forest plots in sensitivity and specificity of traditional machine learning (TML) or deep learning (DL) models. The pooled sensitivity and specificity were 0.84 (95% CI 0.82-0.86) and 0.87 (95% CI 0.84-0.90), respectively.

Figure 4

The SROC graph for the studies. The AUC of summary receiver operating characteristic (TML) or deep learning (DL) models for diagnosing LSS was 0.92 (95% CI 0.89-0.94). SROC: summary receiver operating characteristic; AUC: area under the curve; SENS: sensitivity; SPEC: specificity.

Figure 5

Fagan nomogram of traditional machine learning (TML) or deep learning (DL) models for diagnosing lumbar spinal stenosis (LSS). The first column of this nomogram represents the pretest probability, the second column represents the likelihood ratio, and the third shows the posttest probability.

Figure 6

Likelihood ratio (LR) dot plot of traditional machine learning (TML) or deep learning (DL) prediction models. The summary point of TML or DL models was in the right lower quadrant (LR+<10 and LR–>0.1: no exclusion or confirmation). LRN: negative likelihood ratio; LRP: positive likelihood ratio; LUQ: left upper quadrant; RLQ: right lower quadrant; RUQ: right upper quadrant.