Neural network prediction of nonstress test results: how often should we preform nonstress tests?

Article Abstract:

Nonstress tests performed on pregnant women to measure fetal heart rate patterns may not accurately predict fetal heart rates when conducted at intervals more than two days apart. Researchers conducted daily nonstress tests on 100 healthy women 36 weeks pregnant or more. Neural networks using pattern recognition and repetitive exposure to fetal heart rate values were designed to predict fetal heart rates. Trained neural networks could predict variations in fetal heart rate, but not elements of resting fetal heart rate. Neural networks could not be trained when nonstress tests were performed more than two days apart. Fetal heart rate variations were more predictable than fetal heart rate accelerations. Multiple regression formulas and neural networks may not be useful in predicting significant fetal heart rate changes.

Methods, Computer networks, Neural networks, Fetal heart rate monitoring, Electronic fetal monitoring

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Comparison of visual and computerized interpretation of nonstress test results in a randomized controlled trial

Article Abstract:

Using computer programs to analyze fetal test results may result in fewer examinations, less time testing and may consequently save money. Fetal heart rate monitoring is one example of such a test, which was traditionally interpreted by doctors.

Evaluation, Information management, Computer-aided medical diagnosis, Computer aided medical diagnosis, Fetal monitoring

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Cannabinoid receptors and their role in the regulation of the serotonin transporter in human placenta

Article Abstract:

The placenta contains receptors for tetrahydrocannabinol, which is the active ingredient in marijuana. These receptors also play a role in serotonin transport, so marijuana use during pregnancy may affect serotonin levels.

Physiological aspects, Identification and classification, Placenta, Cell receptors, Cannabinoids

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