Conclusion The German urological landscape has changed since the outbreak of COVID-19 with a significant shift of high priority surgeries but also continuation of elective surgical treatments. While screening and staff protection is employed heterogeneously, the number of infected German urologists stays low.

For every eight newborns delivered by primary CS one more than expected with vaginal delivery is hospitalized. It is highly relevant to recognize that each week of gestational age reduces the risk of respiratory symptoms, especially if primary CS is performed. The higher rate of respiratory morbidity and neonatal admission following CS should be clearly recognized in counselling of pregnant women.

Laparoscopic surgery has evolved as a key technique for cancer diagnosis and therapy. While characterization of the tissue perfusion is crucial in various procedures, such as partial nephrectomy, doing so by means of visual inspection remains highly challenging. Spectral imaging takes advantage of the fact that different tissue components have unique optical properties to recover relevant information on tissue function such as ischemia. However, clinical success stories for advancing laparoscopic surgery with spectral imaging are lacking to date. To address this bottleneck, we developed the first laparoscopic real-time multispectral imaging (MSI) system featuring a compact and lightweight multispectral camera and the possibility to complement the conventional RGB (Red, Green, and Blue) surgical view of the patient with functional information at a video rate of 25 Hz. To account for the high inter-patient variability of human tissue, we phrase the problem of ischemia detection as an out-of-distribution (OoD) detection problem that does not rely on data from any other patient. Using an ensemble of invertible neural networks (INNs) as a core component, our algorithm computes the likelihood of ischemia based on a short (several seconds) video sequence acquired at the beginning of each surgery. A first-in-human trial performed on 10 patients undergoing partial nephrectomy demonstrates the feasibility of our approach for fully-automatic live ischemia monitoring during laparoscopic surgery. Compared to the clinical state-of-the-art approach based on indocyanine green (ICG) fluorescence, the proposed MSI-based method does not require the injection of a contrast agent and is repeatable if the wrong segment has been clamped. Spectral imaging combined with advanced deep learning-based analysis tools could thus evolve as an important tool for fast, efficient, reliable and safe functional imaging in minimally invasive surgery.

Purpose Live intra-operative functional imaging has multiple potential clinical applications, such as localization of ischemia, assessment of organ transplantation success and perfusion monitoring. Recent research has shown that live monitoring of functional tissue properties, such as tissue oxygenation and blood volume fraction, is possible using multispectral imaging in laparoscopic surgery. While the illuminant spectrum is typically kept constant in laparoscopic surgery and can thus be estimated from preoperative calibration images, a key challenge in open surgery originates from the dynamic changes of lighting conditions. Methods The present paper addresses this challenge with a novel approach to light source calibration based on specular highlight analysis. It involves the acquisition of low-exposure time images serving as a basis for recovering the illuminant spectrum from pixels that contain a dominant specular reflectance component. Results Comprehensive in silico and in vivo experiments with a range of different light sources demonstrate that our approach enables an accurate and robust recovery of the illuminant spectrum in the field of view of the camera, which results in reduced errors with respect to the estimation of functional tissue properties. Our approach further outperforms state-of-the-art methods proposed in the field of computer vision. Conclusion Our results suggest that low-exposure multispectral images are well suited for light source calibration via specular highlight analysis. This work thus provides an important first step toward live functional imaging in open surgery.

Laparoscopic surgery has evolved as a key technique for cancer diagnosis and therapy. While characterization of the tissue perfusion is crucial in various procedures, such as partial nephrectomy, doing so by means of visual inspection remains highly challenging. We developed a laparoscopic real-time multispectral imaging system featuring a compact and lightweight multispectral camera and the possibility to complement the conventional surgical view of the patient with functional information at a video rate of 25 Hz. To enable contrast agent–free ischemia monitoring during laparoscopic partial nephrectomy, we phrase the problem of ischemia detection as an out-of-distribution detection problem that does not rely on data from any other patient and uses an ensemble of invertible neural networks at its core. An in-human trial demonstrates the feasibility of our approach and highlights the potential of spectral imaging combined with advanced deep learning–based analysis tools for fast, efficient, reliable, and safe functional laparoscopic imaging.