The Mayo Clinic study shows that artificial intelligence provides successful results in the delivery of pregnant women

Madre con bebé.

ROCHESTER, Minnesota — Mayo Clinic researchers discovered that using artificial intelligence algorithms to analyze patterns of change in women during labor can help identify if vaginal delivery will be successful and positive results will be obtained both in the mother and en el bebé. The results of this investigation were published in PLOS ONE.

«Este es el primer paso en el uso de algorithmos que ofrezcan a médicos y parteras una guía contundente para tomar decisiones cruciales durante el trabajo de parto. Once the algorithms are validated with my research, we believe that they will function in real time, which means that all new data entered on the work of a pregnant woman will automatically recalculate the risk of adverse results. “Esto puede ayudar a reducir tanto la tasa de nacimientos por cesárea como las complicaciones en las madres y en los reciencidos”, says Dr. Abimbola Famuyide, gynecologist de Mayo Clinic y autor experto del estudio.

Future mothers understand the importance of periodically examining the uterus to measure the progress of labor. Es un paso fundamental, puesto que ayuda a los obstetricas a predecir la possibility de que el parto vaginal ocurra en un periodo specific de tiempo. The problem is that the dilatation of the uterine cervix during labor varies from one person to another and there are many important factors that can determine the evolution of labor.

In the study, the researchers used the database of data Consortium on Safe Labor (Consorcio sobre parto seguro) del Instituto Nacional para la Salud Infantil y el Desarrollo Humano Eunice Kennedy a fin de crear un modelo de prediction y examinaron más de 700 clinical y obstétricos en 66,586 partos, desde el momento del ingreso al ingreso al y el trabajo del parto.

El modelo de predicción del riesgo consistió en los datos conocidos al momento del ingreso al ingreso al ingreso para el parto, como las characteristics basales de la paciente, la evaluación clínica más reciente, así como del progresso cumulativeo del trabajo del parto since el ingreso. The researchers explained that the models can offer an alternative to the classic histories of labor and promote that clinical decisions are personalized through the basal characteristics and labor of each patient.

«Se personaliza absolutely para la persona que está giving a light», says el Dr. Famuyide. He adds that it is also an instrument very useful for midwives and doctors who work remotely because it allows them to consider the time needed to transport patients from rural or remote areas.

“The ability of the artificial intelligence algorithm to predict individual risks during childbirth will not only reduce the adverse outcomes in childbirth, but also the costs of medical care in the United States for maternal morbidity, which are estimated at over 30 million de dollars americanos,» added el Dr. Bijan Borah, Director Científico Robert D. y Patricia E. Kern para Servicios de Salud e Investigación de Resultados.

The validation studies to evaluate the results of these models once they are implemented in the delivery units are ongoing. This study was conducted in collaboration with cientificos del Centro Robert D. y Patricia E. Kern para la Ciencia de la Atención Médica. Los autores no declararon ningun posible conflicto de interes.


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Contact for the media:

Sharon Theimer, Comunicaciones de Mayo Clinic,

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