American journal of rhinology · 2006
Use of an electronic nose to diagnose bacterial sinusitis.
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Thaler Erica R, Hanson C William
Аннотация
BACKGROUND: Having previously established that an electronic nose (enose) can distinguish among bacteria samples, between cerebrospinal fluid leak and serum, and can identify patients with ventilator-associated pneumonia, we hypothesized that bacterial sinusitis could be diagnosed by sampling exhaled gas with an enose.
METHODS: Using a nasal continuous positive airway pressure mask, we sampled gas exhaled through the nose of patients with sinusitis and compared them with controls. Data were first projected onto the principal components and then classified by support vector machine (SVM), a machine learning algorithm for pattern recognition.
RESULTS: SVM analysis showed good discrimination using three approaches. First, 11 samples were used to create a training set that was used to predict whether individual samples from each set were a member of the control or infected sets. The enose was correct 98.4% of the time. Second, one-half of the samples from each of the same 11 control and infected groups were used to construct a training set, which was used to predict infection in the remaining samples. The enose was correct 82% of the time. Finally, 68 samples (34 positive and 34 controls) were analyzed using a leave-one-out scheme for creating training sets and testing sets. This method, designed to reflect the generalization property of the SVM classifier, scored a classification rate of 72%.
CONCLUSION: Using the enose to sample nasal exhalation from patients with suspected sinusitis, we were able to predict correctly the diagnosis of sinusitis in at least 72% of the samples. The next step will be to do forward prediction using this model.
METHODS: Using a nasal continuous positive airway pressure mask, we sampled gas exhaled through the nose of patients with sinusitis and compared them with controls. Data were first projected onto the principal components and then classified by support vector machine (SVM), a machine learning algorithm for pattern recognition.
RESULTS: SVM analysis showed good discrimination using three approaches. First, 11 samples were used to create a training set that was used to predict whether individual samples from each set were a member of the control or infected sets. The enose was correct 98.4% of the time. Second, one-half of the samples from each of the same 11 control and infected groups were used to construct a training set, which was used to predict infection in the remaining samples. The enose was correct 82% of the time. Finally, 68 samples (34 positive and 34 controls) were analyzed using a leave-one-out scheme for creating training sets and testing sets. This method, designed to reflect the generalization property of the SVM classifier, scored a classification rate of 72%.
CONCLUSION: Using the enose to sample nasal exhalation from patients with suspected sinusitis, we were able to predict correctly the diagnosis of sinusitis in at least 72% of the samples. The next step will be to do forward prediction using this model.