From Planning Stage Towards FAIR Data: A Practical Metadatasheet For Biomedical Scientists.

Datasets consist of measurement data and metadata. Metadata provides context, essential for understanding and (re-)using data. Various metadata standards exist for different methods, systems and contexts. However, relevant information resides at differing stages across the data-lifecycle. Often, this information is defined and standardized only at publication stage, which can lead to data loss and workload increase. In this study, we developed Metadatasheet, a metadata standard based on interviews with members of two biomedical consortia and systematic screening of data repositories. It aligns with the data-lifecycle allowing synchronous metadata recording within Microsoft Excel, a widespread data recording software. Additionally, we provide an implementation, the Metadata Workbook, that offers user-friendly features like automation, dynamic adaption, metadata integrity checks, and export options for various metadata standards. By design and due to its extensive documentation, the proposed metadata standard simplifies recording and structuring of metadata for biomedical scientists, promoting practicality and convenience in data management. This framework can accelerate scientific progress by enhancing collaboration and knowledge transfer throughout the intermediate steps of data creation.

Reading the Mind through the Nose: Mentalizing Skills Predict Olfactory Performance.

A growing body of literature suggests a close link between olfaction and social expertise. The current study examines whether mentalizing skills are related to olfactory discrimination performance. In order to assess their mentalizing ability, 21 women and 20 men completed the "Reading the Mind in the Eyes" test (RMET). Here, the participants have to infer other persons' mental state from photographs of eye regions. Odor discrimination was assessed using the "Düsseldorf Odour Discrimination Test" (DODT). The DODT consists of 15 items, each containing mixtures of four monomolecular substances. Within each item, two bottles contain the same mixing ratio, while the third contains the reversed mixing ratio. The participants had to identify the deviating odor. Women's expertise in mentalizing (RMET score) is strongly related to olfactory discrimination performance (DODT score): The better women are in mentalizing, the better their performance in olfactory discrimination (rho = 0.572, p = 0.042, Bonferroni-corrected). Men's mentalizing capability was unrelated to olfactory discrimination (rho = −0.117, p > 0.999, Bonferroni-corrected). The current results suggest that the social skill of mentalizing might underly the link between olfaction and social integration, at least in women, and are discussed with regard to the social nature of human olfaction.

A review of computer-based simulators for ultrasound training.

SUMMARY STATEMENT: Computer-based simulators for ultrasound training are a topic of recent interest. During the last 15 years, many different systems and methods have been proposed. This article provides an overview and classification of systems in this domain and a discussion of their advantages. Systems are classified and discussed according to the image simulation method, user interactions and medical applications. Computer simulation of ultrasound has one key advantage over traditional training. It enables novel training concepts, for example, through advanced visualization, case databases, and automatically generated feedback. Qualitative evaluations have mainly shown positive learning effects. However, few quantitative evaluations have been performed and long-term effects have to be examined.

Statistical modeling and recognition of surgical workflow.

In this paper, we contribute to the development of context-aware operating rooms by introducing a novel approach to modeling and monitoring the workflow of surgical interventions. We first propose a new representation of interventions in terms of multidimensional time-series formed by synchronized signals acquired over time. We then introduce methods based on Dynamic Time Warping and Hidden Markov Models to analyze and process this data. This results in workflow models combining low-level signals with high-level information such as predefined phases, which can be used to detect actions and trigger an event. Two methods are presented to train these models, using either fully or partially labeled training surgeries. Results are given based on tool usage recordings from sixteen laparoscopic cholecystectomies performed by several surgeons.

Modeling and segmentation of surgical workflow from laparoscopic video.

Modeling and analyzing surgeries based on signals that are obtained automatically from the operating room (OR) is a field of recent interest. It can be valuable for analyzing and understanding surgical workflow, for skills evaluation and developing context-aware ORs. In minimally invasive surgery, laparoscopic video is easy to record but it is challenging to extract meaningful information from it. We propose a method that uses additional information about tool usage to perform a dimensionality reduction on image features. Using Canonical Correlation Analysis (CCA) a projection of a high-dimensional image feature space to a low dimensional space is obtained such that semantic information is extracted from the video. To model a surgery based on the signals in the reduced feature space two different statistical models are compared. The capability of segmenting a new surgery into phases only based on the video is evaluated. Dynamic Time Warping which strongly depends on the temporal order in combination with CCA shows the best results.

Modeling and online recognition of surgical phases using Hidden Markov Models.

The amount of signals that can be recorded during a surgery, like tracking data or state of instruments, is constantly growing. These signals can be used to better understand surgical workflow and to build surgical assist systems that are aware of the current state of a surgery. This is a crucial issue for designing future systems that provide context-sensitive information and user interfaces. In this paper, Hidden Markov Models (HMM) are used to model a laparoscopic cholecystectomy. Seventeen signals, representing tool usage, from twelve surgeries are used to train the model. The use of a model merging approach is proposed to build the HMM topology and compared to other methods of initializing a HMM. The merging method allows building a model at a very fine level of detail that also reveals the workflow of a surgery in a human-understandable way. Results for detecting the current phase of a surgery and for predicting the remaining time of the procedure are presented.