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Artificial Intelligence-enabled, Real-time Intraoperative Ultrasound Imaging of Neural Structures Within the Psoas
- Source :
- Spine
- Publication Year :
- 2020
- Publisher :
- Lippincott Williams & Wilkins, 2020.
-
Abstract
- A porcine model was used to evaluate an Artificial Intelligence (AI)-enabled ultrasound imaging system. Image processing and machine learning algorithms were developed to enable intraoperative ultrasonic detection, segmentation, classification, and display of neural structures within the psoas. The imaging system's performance was assessed with tissue dissection and Dice coefficient calculation.<br />Study Design. Experimental in-vivo animal study. Objective. The aim of this study was to evaluate an Artificial Intelligence (AI)-enabled ultrasound imaging system's ability to detect, segment, classify, and display neural and other structures during trans-psoas spine surgery. Summary of Background Data. Current methodologies for intraoperatively localizing and visualizing neural structures within the psoas are limited and can impact the safety of lateral lumbar interbody fusion (LLIF). Ultrasound technology, enhanced with AI-derived neural detection algorithms, could prove useful for this task. Methods. The study was conducted using an in vivo porcine model (50 subjects). Image processing and machine learning algorithms were developed to detect neural and other anatomic structures within and adjacent to the psoas muscle while using an ultrasound imaging system during lateral lumbar spine surgery (SonoVision,™ Tissue Differentiation Intelligence, USA). The imaging system's ability to detect and classify the anatomic structures was assessed with subsequent tissue dissection. Dice coefficients were calculated to quantify the performance of the image segmentation. Results. The AI-trained ultrasound system detected, segmented, classified, and displayed nerve, psoas muscle, and vertebral body surface with high sensitivity and specificity. The mean Dice coefficient score for each tissue type was >80%, indicating that the detected region and ground truth were >80% similar to each other. The mean specificity of nerve detection was 92%; for bone and muscle, it was >95%. The accuracy of nerve detection was >95%. Conclusion. This study demonstrates that a combination of AI-derived image processing and machine learning algorithms can be developed to enable real-time ultrasonic detection, segmentation, classification, and display of critical anatomic structures, including neural tissue, during spine surgery. AI-enhanced ultrasound imaging can provide a visual map of important anatomy in and adjacent to the psoas, thereby providing the surgeon with critical information intended to increase the safety of LLIF surgery. Level of Evidence: N/A
- Subjects :
- Lumbar Vertebrae
Intraoperative Neurophysiological Monitoring
ultrasound
Swine
Reproducibility of Results
artificial intelligence
neural anatomy
lateral spine surgery
Machine Learning
Spinal Fusion
Basic Science
Artificial Intelligence
Models, Animal
Image Processing, Computer-Assisted
Animals
Humans
Female
image guidance
porcine model
psoas muscle
Algorithms
Psoas Muscles
Ultrasonography
Subjects
Details
- Language :
- English
- ISSN :
- 15281159 and 03622436
- Volume :
- 46
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- Spine
- Accession number :
- edsair.pmid..........144369dd8a0b0907e329b98057efbcbb