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. 2022 Nov;36(11):8568-8591.
doi: 10.1007/s00464-022-09611-1. Epub 2022 Sep 28.

Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data

Martin Wagner  1   2 , Johanna M Brandenburg  3   4 , Sebastian Bodenstedt  5   6 , André Schulze  3   4 , Alexander C Jenke  5 , Antonia Stern  7 , Marie T J Daum  3   4 , Lars Mündermann  7 , Fiona R Kolbinger  8   9 , Nithya Bhasker  5 , Gerd Schneider  10 , Grit Krause-Jüttler  8 , Hisham Alwanni  7 , Fleur Fritz-Kebede  10 , Oliver Burgert  11 , Dirk Wilhelm  12 , Johannes Fallert  7 , Felix Nickel  3 , Lena Maier-Hein  13 , Martin Dugas  10 , Marius Distler  8   14   15   16   17 , Jürgen Weitz  8   14   15   16   17 , Beat-Peter Müller-Stich  3   4 , Stefanie Speidel  5   6
Affiliations

Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data

Martin Wagner et al. Surg Endosc. 2022 Nov.

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.

Keywords: Artificial intelligence; Minimally invasive surgery; Precision medicine; Prediction model; Radiomics; Surgical data science.

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Conflict of interest statement

A. Stern, H. Alwanni, L. Mündermann, and J. Fallert are employees of KARL STORZ SE & Co. KG. M. Wagner, S. Bodenstedt, F. Fritz-Kebede, M. Dugas, M. Distler, J. Weitz, B. Müller-Stich and S. Speidel are project leaders of the Surgomics-project, funded by the German Federal Ministry of Health (Grant Number 2520DAT82) with medical device manufacturer KARL STORZ SE & Co. KG being a project partner. J. M. Brandenburg, A. Schulze, A. C. Jenke, M.T.J. Daum, F. R. Kolbinger, N. Bhasker, G. Schneider, G. Krause-Jüttler, O. Burgert, D. Wilhelm, F. Nickel, and L. Maier-Hein have no conflicts of interest or financial ties to disclose.

Figures

Fig. 1
Fig. 1
Concept of Surgomics. a In surgical data science, pre-, intra- and postoperative data are integrated to predict morbidity, mortality and long-term outcome. b Surgomics focuses on the intraoperative setting that comprises data sources like the surgical video or anesthesiological vital sign monitoring. c Surgomic features can be extracted from suitable data sources in an automated fashion, for example using machine learning or other data science methods
Fig. 2
Fig. 2
Categories of surgomic features. A team of multidisciplinary experts defined eight feature categories to classify surgomic features
Fig. 3
Fig. 3
Rating of surgomic feature categories. Ratings are displayed in each subplot per feature category for clinical relevance regarding morbidity and mortality, clinical relevance regarding long-term (oncological) outcome and technical feasibility. Colors depict ratings of surgeons and scientists, respectively. The only significant difference between surgeons and scientists was in the category “surgical skill and quality of performance” regarding the relevance for morbidity and mortality (p = 0.002)
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