ArticleSurgical endoscopy2022
Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data.
Article in Surgical endoscopy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Radiomics and anaesthetic planning: Quantitative imaging as a new frontier in preoperative risk assessment.World journal of radiology · 2026Review
- Intraoperative neurophysiological monitoring (IONM) in neurosurgery: A critical appraisal of established practices, ongoing controversies, and future trajectories.Journal of anesthesia and translational medicine · 2026Review
- A framework for Surgical Quality Assurance (SQA) in randomized controlled trials in gastrointestinal surgery: an international Delphi consensus study.EClinicalMedicine · 2025Article
- Transferable situation recognition system for scenario-independent context-aware surgical assistance systems: a proof of concept.International journal of computer assisted radiology and surgery · 2025Article
- Virtual 3D models, augmented reality systems and virtual laparoscopic simulations in complicated pancreatic surgeries: state of art, future perspectives, and challenges.International journal of surgery (London, England) · 2025Review
- The role of artificial intelligence in pancreatic surgery: Current and future perspectives.Annals of hepato-biliary-pancreatic surgery · 2025Article
- Towards multimodal graph neural networks for surgical instrument anticipation.International journal of computer assisted radiology and surgery · 2024Article
- A surgical activity model of laparoscopic cholecystectomy for co-operation with collaborative robots.Surgical endoscopy · 2024Article
- Parameter-efficient framework for surgical action triplet recognition.International journal of computer assisted radiology and surgery · 2024Article
- [The digital operating room : Chances and risks of artificial intelligence].Chirurgie (Heidelberg, Germany) · 2024Review
- Review
- Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study.Surgical endoscopy · 2023Article
- Acquisition and usage of robotic surgical data for machine learning analysis.Surgical endoscopy · 2023Article
- Prediction model of atrial fibrillation recurrence after Cox-Maze IV procedure in patients with chronic valvular disease and atrial fibrillation based on machine learning algorithm.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2023Article
- Machine learning-based analysis of risk factors for atrial fibrillation recurrence after Cox-Maze IV procedure in patients with atrial fibrillation and chronic valvular disease: A retrospective cohort study with a control group.Frontiers in cardiovascular medicine · 2023Article
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Authors and funding
24 authors.
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Abstract
backgroundPersonalized medicine requires the integration and analysis of vast amounts of patient data to realize individualized care. With Surgomics, we aim to facilitate personalized therapy recommendations in surgery by integration of intraoperative surgical data and their analysis with machine learning methods to leverage the potential of this data in analogy to Radiomics and Genomics.
methodsWe defined Surgomics as the entirety of surgomic features that are process characteristics of a surgical procedure automatically derived from multimodal intraoperative data to quantify processes in the operating room. In a multidisciplinary team we discussed potential data sources like endoscopic videos, vital sign monitoring, medical devices and instruments and respective surgomic features. Subsequently, an online questionnaire was sent to experts from surgery and (computer) science at multiple centers for rating the features' clinical relevance and technical feasibility.
resultsIn total, 52 surgomic features were identified and assigned to eight feature categories. Based on the expert survey (n = 66 participants) the feature category with the highest clinical relevance as rated by surgeons was "surgical skill and quality of performance" for morbidity and mortality (9.0 ± 1.3 on a numerical rating scale from 1 to 10) as well as for long-term (oncological) outcome (8.2 ± 1.8). The feature category with the highest feasibility to be automatically extracted as rated by (computer) scientists was "Instrument" (8.5 ± 1.7). Among the surgomic features ranked as most relevant in their respective category were "intraoperative adverse events", "action performed with instruments", "vital sign monitoring", and "difficulty of surgery".
conclusionSurgomics is a promising concept for the analysis of intraoperative data. Surgomics may be used together with preoperative features from clinical data and Radiomics to predict postoperative morbidity, mortality and long-term outcome, as well as to provide tailored feedback for surgeons.
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