Enhancing Anterior Quadratus Lumborum Block Accuracy with Artificial Intelligence: A Segmentation Approach Evaluated by Dice Score Metrics

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Abstract

Anterior quadratus lumborum (QL) block is a regional anesthesia technique shown to provide both somatic and visceral pain relief by targeting lower thoracic nerves and the thoracic sympathetic trunk. Despite its clinical benefits, success depends on accurate sonoanatomic identification, which can be challenging due to individual anatomical variations. In this study, we developed an artificial intelligence (AI) model to automatically segment key sonoanatomic landmarks for the anterior QL block. A total of 82 ultrasound videos from 42 healthy volunteers yielded 460 labeled images capturing the vertebral body (L3/L4), posterior renal fascia, transverse abdominal muscle, quadratus lumborum muscle, psoas major muscle, and the injection point. We trained a 2D U-Net-based model (nnU-Net) with five-fold cross-validation. Training data was split into an 80% training set (368 images) and 20% validation set (92 images). The performance of the AI model was tested on images obtained from 20 patients receiving the anterior QL block as a part of standard treatment. The model achieved a moderate-high Dice score of 0.62 across six classes, with especially high segmentation performance for vertebral bodies (Dice 0.90) and the psoas major muscle (Dice 0.85). Low-moderate performance was observed for the posterior renal fascia (Dice 0.35) and the injection point (Dice 0.38), likely reflecting their subtle sonographic appearance. In conclusion, this is the first AI model that can delineate the sonoanatomy of the anterior QL block region. Our findings underscore the potential of AI to improve the precision and consistency of ultrasound-guided anterior QL blocks. KEY MESSAGESO_ST_ABSWhat is already known on this topicC_ST_ABSThe anterior quadratus lumborum (QL) block effectively reduces postoperative opioid consumption and pain but relies on precise sonoanatomic identification. The efficacy of the block has been concluded in a recent systematic review with meta-analyses. What this study addsIt presents the first AI model to segment key landmarks for the anterior QL block in a clinical setting. How this study might affect research, practice or policyAI-based segmentation may improve consistency, reduce operator dependence, and enhance beneficial patient outcomes.

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