Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study.

Background: With Surgomics, we aim for personalized prediction of the patient's surgical outcome using machine-learning (ML) on multimodal intraoperative data to extract surgomic features as surgical process characteristics. As high-quality annotations by medical experts are crucial, but still a bottleneck, we prospectively investigate active learning (AL) to reduce annotation effort and present automatic recognition of surgomic features.

Methods: To establish a process for development of surgomic features, ten video-based features related to bleeding, as highly relevant intraoperative complication, were chosen.

Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark.

Purpose: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset.

A Platform and Multisided Market for Translational, Software-Defined Medical Procedures in the Operating Room (OP 4.1): Proof-of-Concept Study.

Background: Although digital and data-based technologies are widespread in various industries in the context of Industry 4.0, the use of smart connected devices in health care is still in its infancy. Innovative solutions for the medical environment are affected by difficult access to medical device data and high barriers to market entry because of proprietary systems.

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.

Service-Oriented Medical Device Connectivity: Particular Standards for Endoscopic Surgery.

To translate recent advances in medical device interoperability research into clinical practice, standards are being developed that specify precise requirements towards the network representation of particular medical devices connecting through ISO/IEEE 11073 SDC. The present contribution supplements this protocol standard with specific models for endoscopic camera systems, light sources, insufflators, and pumps. Through industry consensus, these new standards provide modular means to describe the devices' capabilities and modes of interaction in a service-oriented medical device communication architecture.

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.

Prediction of laparoscopic procedure duration using unlabeled, multimodal sensor data.

Purpose: The course of surgical procedures is often unpredictable, making it difficult to estimate the duration of procedures beforehand. This uncertainty makes scheduling surgical procedures a difficult task. A context-aware method that analyses the workflow of an intervention online and automatically predicts the remaining duration would alleviate these problems.

Amplitude and phasing of trunk motion is critical for the efficacy of gait training aimed at reducing ambulatory loads at the knee.

The purpose of this study was to determine the contribution of changes in amplitude and phasing of medio-lateral trunk sway to a change in the knee adduction moment when walking with increased medio-lateral trunk sway. Kinematic and kinetic data of walking trials with normal and with increased trunk sway were collected for 19 healthy volunteers using a standard motion analysis system. The relationship between the change in first peak knee adduction moment (ΔKAM) and change in trunk sway amplitude (ΔSA; difference between maximum contralateral trunk lean and maximum ipsilateral trunk lean) and phasing (SP; time of heel-strike relative to time of maximum contralateral and time of maximum ipsilateral trunk lean) was determined using nonlinear regression analysis.

Automatic generation of a subject-specific model for accurate markerless motion capture and biomechanical applications.

A novel approach for the automatic generation of a subject-specific model consisting of morphological and joint location information is described. The aim is to address the need for efficient and accurate model generation for markerless motion capture (MMC) and biomechanical studies. The algorithm applied and expanded on previous work on human shapes space by embedding location information for ten joint centers in a subject-specific free-form surface.

Predicting changes in knee adduction moment due to load-altering interventions from pressure distribution at the foot in healthy subjects.

The purpose of this pilot study of healthy subjects was to determine if changes in foot pressure patterns associated with a lateral wedge can predict the changes in the knee adduction moment. We tested two hypotheses: (1) increases or decreases in the knee adduction moment and ankle eversion moment due to load-altering footwear interventions can be predicted from foot pressure distribution and (2) changes in magnitude of the knee adduction moment and ankle eversion moment due to lateral wedges can be predicted from pressure distribution at the foot during walking. Fifteen healthy adults performed walking trials in three shoes: 0 degrees , 4 degrees , and 8 degrees laterally wedged.

A framework for the functional identification of joint centers using markerless motion capture, validation for the hip joint.

The objective of the study was to develop a framework for the accurate identification of joint centers to be used for the calculation of human body kinematics and kinetics. The present work introduces a method for the functional identification of joint centers using markerless motion capture (MMC). The MMC system used 8 color VGA cameras.

Implications of increased medio-lateral trunk sway for ambulatory mechanics.

The purposes of this study was to test a mechanism to reduce the knee adduction moment by testing the hypothesis that increased medio-lateral trunk sway can reduce the knee adduction moment during ambulation in healthy subjects, and to examine the possibility that increasing medio-lateral trunk sway can produce similar potentially adverse secondary gait changes previously associated with reduced knee adduction moments in patients with knee osteoarthritis. Nineteen healthy adults performed walking trials with normal and increased medio-lateral trunk sway at a self-selected normal walking speed. Standard gait analysis was used to calculate three-dimensional lower extremity joint kinematics and kinetics.

The evolution of methods for the capture of human movement leading to markerless motion capture for biomechanical applications.

Over the centuries the evolution of methods for the capture of human movement has been motivated by the need for new information on the characteristics of normal and pathological human movement. This study was motivated in part by the need of new clinical approaches for the treatment and prevention of diseases that are influenced by subtle changes in the patterns movement. These clinical approaches require new methods to measure accurately patterns of locomotion without the risk of artificial stimulus producing unwanted artifacts that could mask the natural patterns of motion.

Quantitative modeling of Arabidopsis development.

We present an empirical model of Arabidopsis (Arabidopsis thaliana), intended as a framework for quantitative understanding of plant development. The model simulates and realistically visualizes development of aerial parts of the plant from seedling to maturity. It integrates thousands of measurements, taken from several plants at frequent time intervals.