Journal
SURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES
Volume 36, Issue 11, Pages 8568-8591Publisher
SPRINGER
DOI: 10.1007/s00464-022-09611-1
Keywords
Artificial intelligence; Minimally invasive surgery; Radiomics; Prediction model; Surgical data science; Precision medicine
Categories
Funding
- Projekt DEAL
- German Federal Ministry of Health (BMG) [BMG 2520DAT82]
- German Research Foundation DFG within the Cluster of Excellence EXC 2050: Center for Tactile Internet with Human-in-the-Loop (CeTI) [390696704]
- Else Kroner Fresenius Center for Digital Health (EKFZ), Dresden, Germany
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Surgomics is a promising concept that integrates intraoperative surgical data with other clinical data to predict postoperative morbidity, mortality, and long-term outcome, as well as provide tailored feedback for surgeons.
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. [GRAPHICS] .
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