Combined indocyanine green and quantitative perfusion assessment with hyperspectral imaging during colorectal resections

Anastomotic insufficiencies still represent one of the most severe complications in colorectal surgery. Since tissue perfusion highly affects anastomotic healing, its objective assessment is an unmet clinical need. Indocyanine green-based fluorescence angiography (ICG-FA) and hyperspectral imaging (HSI) have received great interest in recent years but surgeons have to decide between both techniques. For the first time, two data processing pipelines capable of reconstructing an ICG-FA correlating signal from hyperspectral data were developed. Results were technically evaluated and compared to ground truth data obtained during colorectal resections. In 87% of 46 data sets, the reconstructed images resembled the ground truth data. The combined applicability of ICG-FA and HSI within one imaging system might provide supportive and complementary information about tissue vascularization, shorten surgery time, and reduce perioperative mortality

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

Frequency based assessment of surgical activities

In hospitals the duration of surgeries plays a decisive role in many areas, such as patient safety or financial aspects. By utilizing accurate automated online prediction efficient surgical patient care and effective resource management can be attained. In this work several surgical activities during an intervention were examined for their potential to forecast the remaining intervention time. The method used was based on analysing in the frequency domain of time series which represented the status of surgical activities during an intervention. A nonparametric estimation of power spectral density was calculated for single surgical tasks during an intervention. The power spectral densities (PSD) of different surgical activities were compared in a leave-one-out cross validation of forty surgical workflow recordings of lumbar discectomies. The results showed that the activity irrigate with a mean prediction error of 26 min 23 s is best-suited for determining the remainder of the intervention. To construct a scheduling support for a wider range of surgery types the actions conducted by the surgeon’s right and left hand would eminently be more suitable; the error of the action right hand was 41 min 39 s, yet. In conclusion sophistication into the presented frequency based method might support time and resource management in a general manner