ArticleSurgical endoscopy2024
Surgical optomics: hyperspectral imaging and deep learning towards precision intraoperative automatic tissue recognition-results from the EX-MACHYNA trial.
Article in Surgical endoscopy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Quantitative assessment of gastric tube perfusion during Ivor Lewis esophagectomy using indocyanine-green: results from the prospective interventional trial SPY Q-ICG PAS study.Surgical endoscopy · 2026Trial
- Deep learning assisting the surgical management of gynecologic cancers.Current opinion in oncology · 2026Review
- Xeno-learning: knowledge transfer across species in deep learning-based spectral image analysis.Nature biomedical engineering · 2026Article
- Multimodal imaging platform for rapid non-destructive evaluation of pancreatic puncture specimens.Journal of biomedical optics · 2026Article
- Hyperspectral Fingerprints of Abdominal and Pelvic Organs.Journal of imaging · 2026Article
- Artificial intelligence in gastrointestinal and hepatopancreatobiliary surgery: innovations, integration, and future directions.Annals of medicine and surgery (2012) · 2026Review
- Hyperspectral imaging for orthopedic surgery: lightweight AI enables real-time bone and cartilage segmentation.Medical & biological engineering & computing · 2026Article
- From Image-Guided Surgery to Computer-Assisted Real-Time Diagnosis with Hyperspectral and Multispectral Imaging: A Systematic Review in Gynecologic Oncology.Diagnostics (Basel, Switzerland) · 2026Review
- Laparoscopic near-infrared hyperspectral imaging system for identifying living porcine nerves and unexposed arteries.Biomedical optics express · 2025Article
- The Integration of Artificial Intelligence into Robotic Cancer Surgery: A Systematic Review.Journal of clinical medicine · 2025Review
- [An update on surgical treatment options for inflammatory bowel disease].Innere Medizin (Heidelberg, Germany) · 2025Review
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Authors and funding
19 authors.
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Abstract
backgroundHyperspectral imaging (HSI), combined with machine learning, can help to identify characteristic tissue signatures enabling automatic tissue recognition during surgery. This study aims to develop the first HSI-based automatic abdominal tissue recognition with human data in a prospective bi-center setting.
methodsData were collected from patients undergoing elective open abdominal surgery at two international tertiary referral hospitals from September 2020 to June 2021. HS images were captured at various time points throughout the surgical procedure. Resulting RGB images were annotated with 13 distinct organ labels. Convolutional Neural Networks (CNNs) were employed for the analysis, with both external and internal validation settings utilized.
resultsA total of 169 patients were included, 73 (43.2%) from Strasbourg and 96 (56.8%) from Verona. The internal validation within centers combined patients from both centers into a single cohort, randomly allocated to the training (127 patients, 75.1%, 585 images) and test sets (42 patients, 24.9%, 181 images). This validation setting showed the best performance. The highest true positive rate was achieved for the skin (100%) and the liver (97%). Misclassifications included tissues with a similar embryological origin (omentum and mesentery: 32%) or with overlaying boundaries (liver and hepatic ligament: 22%). The median DICE score for ten tissue classes exceeded 80%.
conclusionTo improve automatic surgical scene segmentation and to drive clinical translation, multicenter accurate HSI datasets are essential, but further work is needed to quantify the clinical value of HSI. HSI might be included in a new omics science, namely surgical optomics, which uses light to extract quantifiable tissue features during surgery.
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