Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data
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Published:2022-09-28
Issue:11
Volume:36
Page:8568-8591
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ISSN:0930-2794
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Container-title:Surgical Endoscopy
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language:en
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Short-container-title:Surg Endosc
Author:
Wagner MartinORCID, Brandenburg Johanna M., Bodenstedt Sebastian, Schulze André, Jenke Alexander C., Stern Antonia, Daum Marie T. J., Mündermann Lars, Kolbinger Fiona R., Bhasker Nithya, Schneider Gerd, Krause-Jüttler Grit, Alwanni Hisham, Fritz-Kebede Fleur, Burgert Oliver, Wilhelm Dirk, Fallert Johannes, Nickel Felix, Maier-Hein Lena, Dugas Martin, Distler Marius, Weitz Jürgen, Müller-Stich Beat-Peter, Speidel Stefanie
Abstract
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.
Graphical abstract
Funder
Bundesministerium für Gesundheit Deutsche Forschungsgemeinschaft Else Kröner-Fresenius-Stiftung Universitätsklinikum Heidelberg
Publisher
Springer Science and Business Media LLC
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