EP09.64: Artificial intelligence in evaluation of the fetal heart

Creation of a data set for the development of an AI service that improves the quality of diagnosis of fetal heart disease. Formation of an algorithm for examining the fetal heart using AI, the result of which should be one of the conclusions: “norm” - the correct structure of the heart / abnormal heart (CHD?). The study per patient should contain video files of 5 of fetal heart standard views. AI conclusion: normal structure/abnormal heart structure. Verification of the conclusion is confirmation of the diagnosis after birth. The total number of studies - 2016, the number of personnel - 659324. The number of frames from the CHD - 27886. The results showed that the AI algorithm can improve the accuracy of ultrasound imaging in the diagnosis of fetal heart pathology and has good application value. The use of AI will improve the quality of ultrasound examinations of the fetal heart due to its standardization and will increase the accuracy of pathology detection in general obstetric practice or in less experienced hands.

Authors

Institutions

Publication Details

Journal
Ultrasound in Obstetrics and Gynecology
Published
2024-09-01
DOI
https://doi.org/10.1002/uog.28445
Primary Topic
Congenital Heart Disease Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

EP09.64: Artificial intelligence in evaluation of the fetal heart

Elena P. Fedotkina, Alexander N. Sencha, N. E. Yannaeva, Ekaterina Leonidovna Bokeria
Ultrasound in Obstetrics and Gynecology
Congenital Heart Disease Studies
article

EP09.64: Artificial intelligence in evaluation of the fetal heart

Elena P. Fedotkina, Alexander N. Sencha, N. E. Yannaeva, Ekaterina Leonidovna Bokeria
article en

Abstract

Creation of a data set for the development of an AI service that improves the quality of diagnosis of fetal heart disease. Formation of an algorithm for examining the fetal heart using AI, the result of which should be one of the conclusions: “norm” - the correct structure of the heart / abnormal heart (CHD?). The study per patient should contain video files of 5 of fetal heart standard views. AI conclusion: normal structure/abnormal heart structure. Verification of the conclusion is confirmation of the diagnosis after birth. The total number of studies - 2016, the number of personnel - 659324. The number of frames from the CHD - 27886. The results showed that the AI algorithm can improve the accuracy of ultrasound imaging in the diagnosis of fetal heart pathology and has good application value. The use of AI will improve the quality of ultrasound examinations of the fetal heart due to its standardization and will increase the accuracy of pathology detection in general obstetric practice or in less experienced hands.

Ultrasound in Obstetrics and GynecologyVol. 64(S1)
National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I.Kulakov of the Ministry of Healthcare of the Russian Federation (RU)
Gender equality
Openalex Percentile: Top 77%
Congenital Heart Disease Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.