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

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.

A novel endoimaging system for endoscopic 3D reconstruction in bladder cancer patients.

INTRODUCTION: The methods employed to document cystoscopic findings in bladder cancer patients lack accuracy and are subject to observer variability. We propose a novel endoimaging system and an online documentation platform to provide post-procedural 3D bladder reconstructions for improved diagnosis, management and follow-up. MATERIAL AND METHODS: The RaVeNNA4pi consortium is comprised of five industrial partners, two university hospitals and two technical institutes. These are grouped into hardware, software and clinical partners according to their professional expertise. The envisaged endoimaging system consists of an innovative cystoscope that generates 3D bladder reconstructions allowing users to remotely access a cloud-based centralized database to visualize individualized 3D bladder models from previous cystoscopies archived in DICOM format. RESULTS: Preliminary investigations successfully tracked the endoscope's rotational and translational movements. The structure-from-motion pipeline was tested in a bladder phantom and satisfactorily demonstrated 3D reconstructions of the processing sequence. AI-based semantic image segmentation achieved a 0.67 dice-score-coefficient over all classes. An online-platform allows physicians and patients to digitally visualize endoscopic findings by navigating a 3D bladder model. CONCLUSIONS: Our work demonstrates the current developments of a novel endoimaging system equipped with the potential to generate 3D bladder reconstructions from cystoscopy videos and AI-assisted automated detection of bladder tumors.

Surgical data science - from concepts toward clinical translation.

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.

An augmented reality framework for soft tissue surgery.

Augmented reality for soft tissue laparoscopic surgery is a growing topic of interest in the medical community and has potential application in intra-operative planning and image guidance. Delivery of such systems to the operating room remains complex with theoretical challenges related to tissue deformation and the practical limitations of imaging equipment. Current research in this area generally only solves part of the registration pipeline or relies on fiducials, manual model alignment or assumes that tissue is static. This paper proposes a novel augmented reality framework for intra-operative planning: the approach co-registers pre-operative CT with stereo laparoscopic images using cone beam CT and fluoroscopy as bridging modalities. It does not require fiducials or manual alignment and compensates for tissue deformation from insufflation and respiration while allowing the laparoscope to be navigated. The paper's theoretical and practical contributions are validated using simulated, phantom, ex vivo, in vivo and non medical data.

A high resolution bladder wall map: feasibility study.

The aim of this work is to provide the surgeon-urologist with a system for automatic 2D and 3D-reconstruction of the bladder wall to help him within the treatment of bladder cancer as well as planning and documentation of the interventions. Within this small pilot-framework a fast feasibility study was made to clear if it is generally possible to build a bladder wall model using a special endoscope with an embedded laser-based distance measurement, an optical navigation system and modern image stitching techniques. Some experiments with a realistic bladder phantom have shown that this initial concept is generally acceptable and can be used with some extensions to build a system which can provide an automatic bladder wall reconstruction in real time to be used within a surgical intervention.