ArticleSurgical endoscopy2025
Laparoscopic hyperspectral imaging for in vivo detection of the vagal nerve in upper gastrointestinal surgery.
Article in Surgical endoscopy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundAccurate intraoperative detection of nerves in critical anatomic regions during oncologic resection, such as the vagal nerve, is crucial. The vagal nerve regulates many internal organs and is at risk during surgeries, potentially leading to severe complications. While different types of intraoperative neuromonitoring methods exist for functional assessment, Hyperspectral Imaging (HSI) offers a non-invasive, real-time alternative for morphologic identification. This study is, to our knowledge, the first to assess laparoscopic HSI for imaging and delineating the vagal nerve during minimally invasive surgery.
methodsThis prospective cohort study examined the vagal nerves of 19 patients undergoing Ivor Lewis esophagectomy using a laparoscopic HSI system with a 30-degree optic during the thoracic part of the procedure. Measurements on the exposed vagal nerve collected spectral data from 500 to 995 nm at each pixel. Based on these reflectance spectra, four different state-of-the-art machine learning methods for binary and multi-class tissue differentiation were evaluated. The classifiers were validated using Leave-One-Patient-Out Cross-Validation (LOOCV) and k-fold CV.
resultsThe spectra of the tissue classes azygos vein, pleura, lung, vagal nerve, and esophagus showed high similarity, with wide inter-patient variability. All tested machine learning classifiers showed similar accuracy in differentiating vagal nerve tissue. Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) outperformed Logistic Regression (LR) and Multilayer Perceptron (MLP), with LDA showing the highest F1 score (harmonic mean of precision and recall) for binary classification (0.85), and SVM excelling in multi-class classification (0.74). Reflectance spectra without further pre-processing provided the best results for tissue differentiation.
conclusionThe first application of HSI to detect the vagal nerve during minimally invasive surgery has shown promising classification results for the five tissue classes considered and highlighted the technical challenges for clinical use. Further clinical research is needed to explore the full potential of HSI to improve the reliability of nerve classification during surgery.
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