Prediction of gestational diabetes mellitus based on an analysis of amniotic fluid by capillary electrophoresis

Artificial Intelligence Methodologies and Their Application to Diabetes

In the past decade diabetes management has been transformed by the addition of continuous glucose monitoring and insulin pump data. More recently, a wide variety of functions and physiologic variables, such as heart rate, hours of sleep, number of steps walked and movement, have been available through wristbands or watches. New data, hydration, geolocation, and barometric pressure, among others, will be incorporated in the future. All these parameters, when analyzed, can be helpful for patients and doctors' decision support. Similar new scenarios have appeared in most medical fields, in such a way that in recent years, there has been an increased interest in the development and application of the methods of artificial intelligence (AI) to decision support and knowledge acquisition. Multidisciplinary research teams integrated by computer engineers and doctors are more and more frequent, mirroring the need of cooperation in this new topic. AI, as a science, can be defined as the ability to make computers do things that would require intelligence if done by humans. Increasingly, diabetes-related journals have been incorporating publications focused on AI tools applied to diabetes. In summary, diabetes management scenarios have suffered a deep transformation that forces diabetologists to incorporate skills from new areas. This recently needed knowledge includes AI tools, which have become part of the diabetes health care. The aim of this article is to explain in an easy and plane way the most used AI methodologies to promote the implication of health care providers-doctors and nurses-in this field.

Early prediction of macrosomia based on an analysis of second trimester amniotic fluid by capillary electrophoresis

AIM

To identify, using capillary electrophoresis and chemometrics, early biomarkers in human amniotic fluid of large-for-gestational-age (LGA) infants.

MATERIALS & METHODS

Second trimester amniotic fluid samples, obtained from mothers undergoing age-related amniocentesis, were analyzed by capillary electrophoresis. Electropherogram data were aligned using correlation-optimized warping. A genetic algorithm using a Bayesian evaluation function and a leave-one-out cross-validation strategy for two birth outcomes: appropriate-for-gestational-age (AGA) versus LGA infants.

RESULTS

LGA (n = 23) was differentiated from AGA (n = 86) with a sensitivity of 100% and a specificity of 98% using only two wavelets. The first wavelet is associated with albumin and the second wavelet with an unknown small molecule.

CONCLUSION

The approach developed herein allows LGA fetuses to be metabolically distinguished from AGA fetuses early in pregnancy and indicates that the birth of a LGA infant is already associated with an altered biochemical profile by the second trimester.