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  • 1
    Electronic Resource
    Electronic Resource
    Comparison of A-mode ultrasound and computed tomography: detection of secretion in maxillary and frontal sinuses in ventilated patients (1996)
    Springer
    Intensive care medicine 22 (1996), S. 1265-1268 
    Details
    ISSN: 1432-1238
    Keywords: Key words A-mode ultrasonography ; Computed tomography ; Maxillary sinusitis ; Frontal sinusitis ; Mechanically ventilated patients
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine
    Notes: Abstract Objective: To study the accuracy of A-mode ultrasonography (A-MU) in detecting secretion in maxillary and frontal sinuses in critically ill, intubated patients undergoing mechanical ventilation. Design: Open study in mechanically ventilated, comatose patients. Setting: Medical-surgical intensive care unit in the General Hospital of Rovigo. Patients: 50 consecutive, mechanically ventilated, critically ill patients. All patients were in a coma and needed cerebral computed tomography (CT) for a diagnosis. Measurements and results: The A-MU technique gave 100 images of maxillary and frontal sinuses. The images were read blindly and classified into five categories: definitely normal, definitely abnormal, probably normal, questionable, and probably abnormal. CT findings were considered to be the „gold standard”. The specificity of echo images varied from 72 to 98% and the sensitivity from 63 to 86% for maxillary sinuses. For frontal sinuses, the specificity varied from 96 to 99% and the sensitivity from 14 to 57%. The area under the receiver-operating characteristic curve was found to be 0.89 and 0.76 for maxillary and frontal sinuses, respectively. Conclusions: The A-MU technique is an accurate tool for detecting secretion in the maxillary sinuses in intubated patients. More investigations are necessary in order to evaluate its usefulness in the frontal sinuses.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01709347
    Library Location Call Number Volume/Issue/Year Availability
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    Paper (German National Licenses)
    Fulltext
  • 2
    Electronic Resource
    Electronic Resource
    Comparison of A-mode ultrasound and computed tomography: Detection of secretion in maxillary and frontal sinuses in ventilated patients (1996)
    Springer
    Intensive care medicine 22 (1996), S. 1265-1268 
    Details
    ISSN: 1432-1238
    Keywords: A-mode ultrasonography ; Computed tomography ; Maxillary sinusitis ; Frontal sinusitis ; Mechanically ventilated patients
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine
    Notes: Abstract Objective To study the accuracy of A-mode ultrasonography (A-MU) in detecting secretion in maxillary and frontal sinuses in critically ill, intubated patients undergoing mechanical ventilation. Design Open study in mechanically ventilated, comatose patients. Setting Medical-surgical intensive care unit in the General Hospital of Rovigo. Patients 50 consecutive, mechanically ventilated, critically ill patients. All patients were in a coma and needed cerebral computed tomography (CT) for a diagnosis. Measurements and results The A-MU technique gave 100 images of maxillary and frontal sinuses. The images were read blindly and classified into five categories: definitely normal, definitely abnormal, probably normal, questionable, and probably abnormal. CT findings were considered to be the “gold standard”. The specificity of echo images varied from 72 to 98% and the sensitivity from 63 to 86% for maxillary sinuses. For frontal sinuses, the specificity varied from 96 to 99% and the sensitivity from 14 to 57%. The area under the receiver-operating characteristic curve was found to be 0.89 and 0.76 for maxillary and frontal sinuses, respectively. Conclusions The A-MU technique is an accurate tool for detecting secretion in the maxillary sinuses in intubated patients. More investigations are necessary in order to evaluate its usefulness in the frontal sinuses.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01709347
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 3
    Electronic Resource
    Electronic Resource
    A neural network classifier for OCR using structural descriptions (1995)
    Springer
    Machine vision and applications 8 (1995), S. 336-342 
    Details
    ISSN: 1432-1769
    Keywords: OCR ; Hand-printing ; Shape description ; Neural classifier ; Classification reliability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We present a method for character recognition especially designed for the case in which the shapes of characters belonging to the same class vary greatly, as it happens with unconstrained hand-printed characters and omnifont printed characters. The most distinctive feature of the method is the use of a special kind of structural description of character shape in connection with a neural network classifier. An original technique is used to achieve the best trade-off between reject and misclassification rates. Experimental results on databases of both hand-printed and printed characters are illustrated.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01211495
    Library Location Call Number Volume/Issue/Year Availability
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    Paper (German National Licenses)
    Fulltext
  • 4
    Electronic Resource
    Electronic Resource
    Reliability Parameters to Improve Combination Strategies in Multi-Expert Systems (1999)
    Springer
    Pattern analysis and applications 2 (1999), S. 205-214 
    Details
    ISSN: 1433-755X
    Keywords: Key words: Classification reliability; Combining rules; Multi-expert systems; Neural nets; OCR; Statistical classifiers
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract: Recognition systems based on a combination of different experts have been widely investigated in the recent past. General criteria for improving the performance of such systems are based on estimating the reliability associated with the decision of each expert, so as to suitably weight its response in the combination phase. According to the methods proposed to-date, when the expert assigns a sample to a class, the reliability of such a decision is estimated on the basis of the recognition rate obtained by the expert on the chosen class during the training phase. As a consequence, the same reliability value is associated with every decision attributing a sample to a same class, even though it seems reasonable to take into account its dependence on the quality of the specific sample. We propose a method for estimating the reliability of each single recognition act of an expert on the basis of information directly derived from its output. In this way, the reliability value of a decision is more properly estimated, thus allowing a more precise weighting during the combination phase. The definition of the reliability parameters for widely used classification paradigms is discussed, together with the combining rules employing them for weighting the expert opinions. The results obtained by combining four experts in order to recognise handwritten numerals from a standard character database are presented. Comparison with classical combining rules is also reported, and the advantages of the proposed approach outlined.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s100440050029
    Library Location Call Number Volume/Issue/Year Availability
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    Paper (German National Licenses)
    Fulltext
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