Abstract
Objective. To establish an early diagnostic system for hypoxic ischemic encephalopathy (HIE) in newborns based on artificial neural networks and to determine its feasibility. Methods. Based on published research as well as preliminary studies in our laboratory, multiple noninvasive indicators with high sensitivity and specificity were selected for the early diagnosis of HIE and employed in the present study, which incorporates fuzzy logic with artificial neural networks. Results. The analysis of the diagnostic results from the fuzzy neural network experiments with 140 cases of HIE showed a correct recognition rate of 100% in all training samples and a correct recognition rate of 95% in all the test samples, indicating a misdiagnosis rate of 5%. Conclusion. A preliminary model using fuzzy backpropagation neural networks based on a composite index of clinical indicators was established and its accuracy for the early diagnosis of HIE was validated. Therefore, this method provides a convenient tool for the early clinical diagnosis of HIE.
Highlights
Neonatal hypoxic ischemic encephalopathy (HIE) is a serious illness with a high incidence rate worldwide and can result in death or disability and significantly affect the quality of life
Non-invasive indicators with high levels of early sensitivity and specificity were selected and utilized in fuzzy neural network experiments based on the clinical manifestations of HIE as well as several related studies conducted in China and abroad [6–9]
The present study utilized 10 qualitative and quantitative parameters corresponding to fetal distress, neonatal asphyxia, and HIE clinical manifestations, as well as 15 indicators selected from early diagnostic indicators considered to be important based on several related studies
Summary
To establish an early diagnostic system for hypoxic ischemic encephalopathy (HIE) in newborns based on artificial neural networks and to determine its feasibility. The analysis of the diagnostic results from the fuzzy neural network experiments with 140 cases of HIE showed a correct recognition rate of 100% in all training samples and a correct recognition rate of 95% in all the test samples, indicating a misdiagnosis rate of 5%. A preliminary model using fuzzy backpropagation neural networks based on a composite index of clinical indicators was established and its accuracy for the early diagnosis of HIE was validated. This method provides a convenient tool for the early clinical diagnosis of HIE
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