30,200 research outputs found
Toward a Knowledge-Driven Context-Aware System for Surgical Assistance
Complex surgeries complications are increasing, thus making an efficient surgical assistance is a real need. In this work, an ontology-based context-aware system was developed for surgical training/assistance during Thoracentesis by using image processing and semantic technologies. We evaluated the Thoracentesis ontology and implemented a paradigmatic test scenario to check the efficacy of the system by recognizing contextual information, e.g. the presence of surgical instruments on the table. The framework was able to retrieve contextual information about current surgical activity along with information on the need or presence of a surgical instrument
Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy
Objective: Surgical data science is evolving into a research field that aims
to observe everything occurring within and around the treatment process to
provide situation-aware data-driven assistance. In the context of endoscopic
video analysis, the accurate classification of organs in the field of view of
the camera proffers a technical challenge. Herein, we propose a new approach to
anatomical structure classification and image tagging that features an
intrinsic measure of confidence to estimate its own performance with high
reliability and which can be applied to both RGB and multispectral imaging (MI)
data. Methods: Organ recognition is performed using a superpixel classification
strategy based on textural and reflectance information. Classification
confidence is estimated by analyzing the dispersion of class probabilities.
Assessment of the proposed technology is performed through a comprehensive in
vivo study with seven pigs. Results: When applied to image tagging, mean
accuracy in our experiments increased from 65% (RGB) and 80% (MI) to 90% (RGB)
and 96% (MI) with the confidence measure. Conclusion: Results showed that the
confidence measure had a significant influence on the classification accuracy,
and MI data are better suited for anatomical structure labeling than RGB data.
Significance: This work significantly enhances the state of art in automatic
labeling of endoscopic videos by introducing the use of the confidence metric,
and by being the first study to use MI data for in vivo laparoscopic tissue
classification. The data of our experiments will be released as the first in
vivo MI dataset upon publication of this paper.Comment: 7 pages, 6 images, 2 table
Inductive learning of the surgical workflow model through video annotations
partially_open5siSurgical workflow modeling is becoming
increasingly useful to train surgical residents for complex
surgical procedures. Rule-based surgical workflows have shown
to be useful to create context-aware systems. However, manually
constructing production rules is a time-intensive and laborious
task. With the expansion of new technologies, large video
archive can be created and annotated exploiting and storing the
expert’s knowledge. This paper presents a prototypical study of
automatic generation of production rules, in the Horn-clause,
using the First Order Inductive Learner (FOIL) algorithm
applied to annotated surgical videos of Thoracentesis procedure
and of its feasibility to use in context-aware system framework.
The algorithm was able to learn 18 rules for surgical workflow
model with 0.88 precision, and 0.94 F1 score on the standard
video annotation data, representing entities of the surgical
workflow, which was used to retrieve contextual information on
Thoracentesis workflow for its application to surgical training.openNakawala, HIRENKUMAR CHANDRAKANT; DE MOMI, Elena; Pescatori, Erica Laura; Morelli, Anna; Ferrigno, GiancarloNakawala, HIRENKUMAR CHANDRAKANT; DE MOMI, Elena; Pescatori, Erica Laura; Morelli, Anna; Ferrigno, Giancarl
Development of an intelligent surgical training system for Thoracentesis
Surgical training improves patient care, helps to reduce surgical risks, increases surgeon’s confidence, and thus enhances overall patient safety. Current surgical training systems are more focused on developing technical skills, e.g. dexterity, of the surgeons while lacking the aspects of context-awareness and intra-operative real-time guidance. Context-aware intelligent training systems interpret the current surgical situation and help surgeons to train on surgical tasks. As a prototypical scenario, we chose Thoracentesis procedure in this work. We designed the context-aware software framework using the surgical process model encompassing ontology and production rules, based on the procedure descriptions obtained through textbooks and interviews, and ontology-based and marker-based object recognition, where the system tracked and recognised surgical instruments and materials in surgeon’s hands and recognised surgical instruments on the surgical stand. The ontology was validated using annotated surgical videos, where the system identified “Anaesthesia” and “Aspiration” phase with 100% relative frequency and “Penetration” phase with 65% relative frequency. The system tracked surgical swab and 50 mL syringe with approximately 88.23% and 100% accuracy in surgeon’s hands and recognised surgical instruments with approximately 90% accuracy on the surgical stand. Surgical workflow training with the proposed system showed equivalent results as the traditional mentor-based training regime, thus this work is a step forward a new tool for context awareness and decision-making during surgical training
Il costo dell’intervento chirurgico in laparoscopia con l’Activity Based Costing
The purpose of this research is to analyze the role of the Management Control System (MCS) and of the Management Accounting System (MAS) in healthcare (HC) organizations. It aims at studying if and how managerial considerations affect the clinical culture. Results are based on the findings of a research developed within 12 Local Health Authorities (Aziende Sanitarie Locali LHAs) and 4 Teaching Hospital (Aziende Ospedaliere Universitarie THs) in Italian Tuscany Region and address the possibility to develop an alternative model from those of accountingization or legitimation proposed in literature to understand the role of these systems in healthcare. Results highlight that the economic language may assume a great importance in clinicians’ decision making and penetrates into clinical culture. Most important factor affecting results is the development of an alliance between controllers and clinicians, based trust and collaboration. The paper is a contribution to the literature about the role of MCS and MAS in healthcare and it is developed within the schemes traced by Habermas and refined by Laughlin and by Broadbent and Laughlin. The original value stands on the individuation of a model where the “integrative interactive” management model is able to penetrate clinical discourse and the conditions at which it can be developed.Management Accounting Change, Management Control Change, Healthcare Accounting, Professional organization, accountinization, legitimation, Habermas
User-centered visual analysis using a hybrid reasoning architecture for intensive care units
One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care
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