PubMed · 14989031
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
M Scimia. 2003. [Thinprep imaging system].. https://pubmed.ncbi.nlm.nih.gov/14989031/
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
OBJECTIVE: It was the goal of this study to develop and test an automated method for measuring functional residual capacity (FRC) by an oxygen washin/washout in intensive care settings. Such a method is required to work with conventional ventilator breathing systems and to use only medical grade sensors. METHODS: The oxygen setting on a standard intensive care ventilator is changed by at least 10%. Ventilatory pressure and flow are measured by the built-in sensors of the intensive care ventilator. Oxygen concentration is measured by a diverting medical oxygen analyzer. In order to overcome the known problem that synchrony between flow and concentration measurement is corrupted by the change of gas viscosity and by the cyclic change of airway pressure, a physical/mathematical model of the pneumatic circuit of the analyzer was developed. With this model, the change of sample flow is calculated continuously. Thus, synchrony between flow and gas concentration measurement is restored. This allows the determination of volumetric gas fluxes as needed for the FRC measurement. The setup was tested in the laboratory with a lung simulator. Simulated lung compliance, breathing frequency and tidal volume were varied. Results. The mean difference between measured and simulated FRC (range 1.7 to 5 L) was less than 1% at tidal volumes greater than 400 mL. This difference ranged from -5% to 8%, depending on simulated lung compliance and ventilator setting. The variability of consecutive measurements was about 2.5%. CONCLUSIONS: A method has been developed for reliable measurement of the FRC with an oxygen washin/washout technique. This method is sufficiently easy to use to suit for application in intensive care units. It does not require any action by the operator except a manual change of inspired oxygen concentration. Accuracy and sensitivity of the method have been proven sufficient to meet clinical and scientific requirements. Future clinical studies will reveal the applicability of the chosen procedure under clinical conditions.
OBJECTIVE: Medical assessment of penetrating injuries is a difficult and knowledge-intensive task, and rapid determination of the extent of internal injuries is vital for triage and for determining the appropriate treatment. Physical examination and computed tomographic (CT) imaging data must be combined with detailed anatomic, physiologic, and biomechanical knowledge to assess the injured subject. We are developing a methodology to automate reasoning about penetrating injuries using canonical knowledge combined with specific subject image data. METHODS AND MATERIAL: In our approach, we build a three-dimensional geometric model of a subject from segmented images. We link regions in this model to entities in two knowledge sources: (1) a comprehensive ontology of anatomy containing organ identities, adjacencies, and other information useful for anatomic reasoning and (2) an ontology of regional perfusion containing formal definitions of arterial anatomy and corresponding regions of perfusion. We created computer reasoning services ("problem solvers") that use the ontologies to evaluate the geometric model of the subject and deduce the consequences of penetrating injuries. RESULTS: We developed and tested our methods using data from the Visible Human. Our problem solvers can determine the organs that are injured given particular trajectories of projectiles, whether vital structures--such as a coronary artery--are injured, and they can predict the propagation of injury ensuing after vital structures are injured. CONCLUSION: We have demonstrated the capability of using ontologies with medical images to support computer reasoning about injury based on those images. Our methodology demonstrates an approach to creating intelligent computer applications that reason with image data, and it may have value in helping practitioners in the assessment of penetrating injury.
Explore the source record for details and available documents.