Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Background: Personalized 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.
Methods: We 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.
Results: In 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".
Conclusion: Surgomics 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.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9613751 | PMC |
| http://dx.doi.org/10.1007/s00464-022-09611-1 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study.
Surg Endosc
November 2023
Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany.
Background: With Surgomics, we aim for personalized prediction of the patient's surgical outcome using machine-learning (ML) on multimodal intraoperative data to extract surgomic features as surgical process characteristics. As high-quality annotations by medical experts are crucial, but still a bottleneck, we prospectively investigate active learning (AL) to reduce annotation effort and present automatic recognition of surgomic features.
Methods: To establish a process for development of surgomic features, ten video-based features related to bleeding, as highly relevant intraoperative complication, were chosen.
Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data.
Surg Endosc
November 2022
Department of Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany.
Article Synopsis
- Surgomics is a new approach to personalized medicine that focuses on analyzing intraoperative surgical data using machine learning to improve individualized surgical care.
- A study identified 52 surgomic features from various data sources, with experts rating "surgical skill and quality of performance" as the most clinically relevant and "Instrument" as the most feasible to extract automatically.
- The findings suggest that integrating Surgomics with other preoperative data can enhance patient care by understanding the processes of surgery better and predicting outcomes more accurately.