Lars Mündermann

TL;DR: 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 as mentioned in this paper.

TL;DR: The Heidelberg Colorectal (HeiCo) dataset as discussed by the authors is the first publicly available data set enabling comprehensive benchmarking of medical instrument detection and segmentation algorithms with a specific emphasis on method robustness and generalization capabilities.

TL;DR: This paper proposes and compares methods, based on convolutional neural networks, for continuously predicting the duration of laparoscopic interventions based on unlabeled data, such as from endoscopic image and surgical device streams, and shows that a method incorporating both vision and device data performs better than methods based only on vision, while methods only based on tool usage and surgical devices data perform poorly.

TL;DR: The Heidelberg Colorectal (HeiCo) data set is introduced - the first publicly available data set enabling comprehensive benchmarking of medical instrument detection and segmentation algorithms with a specific emphasis on method robustness and generalization capabilities.

TL;DR: An extensive review of current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to surgical data science, namely technical infrastructure for data acquisition, storage and access in the presence of regulatory constraints.