inovmedИнновационная медицина КубаниInnovative Medicine of Kuban2541-9897Scientific Research Institute – Ochapovsky Regional Clinical Hospital No. 110.35401/2541-9897-2024-9-4-14-20inovmed-932Research ArticleОРИГИНАЛЬНЫЕ СТАТЬИORIGINAL ARTICLESИскусственный интеллект в фетальной эхокардиографииArtificial Intelligence in Fetal Echocardiographyhttps://orcid.org/0000-0002-8898-9612БокерияЕ. Л.BokerijaE. L.

Бокерия Екатерина Леонидовна - д. м. н., советник директора, неонатолог, детский кардиолог, ведущий научный сотрудник отделения патологии новорожденных и недоношенных детей № 2, НМИЦ АГП им. акад. В.И. Кулакова; профессор кафедры неонатологии клинического института детского здоровья им. Н.Ф. Филатова, ПМГМУ им. И.М. Сеченова (Сеченовский Университет).

Москва

Ekaterina L Bokerija - Dr. Sci. (Med.), Adviser to the Director, Neonatologist, Pediatric Cardiologist, Leading Researcher, 2nd Department of Pathology of Newborns and Premature Babies, National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov; Professor at the Department of Neonatology, Clinical Institute of Children’s Health named after N.F. Filatov, I.M. Sechenov First Moscow State Medical University.

Moscow

https://orcid.org/0009-0002-1049-0296ЯннаеваН. Е.YannaevaN. E.

Яннаева Наталья Евгеньевна - к. м. н., врач ультразвуковой диагностики, старший научный сотрудник, НМИЦ АГП им. акад. В.И. Кулакова; доцент кафедры акушерства и гинекологии, РязГМУ им. акад. И.П. Павлова.

Москва; Рязань

Natalia E. Yannaeva - Cand. Sci. (Med.), Ultrasonographer, Senior Researcher, National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov; Associate Professor at the Department of Obstetrics and Gynecology, Ryazan State Medical University named after Academician I.P. Pavlov.

Moscow; Ryazan

https://orcid.org/0000-0002-1188-8872СенчаА. Н.SenchaA. N.

Сенча Александр Николаевич - д. м. н., заведующий отделом визуальной диагностики, профессор кафедры акушерства и гинекологии, НМИЦ АГП им. акад. В.И. Кулакова; профессор кафедры ультразвуковой диагностики, РНИМУ им. Н.И. Пирогова.

117997, Москва, ул. Академика Опарина, д. 4

Alexander N. Sencha - Dr. Sci. (Med.), Head of the Diagnostic Imaging Division, Professor at the Department of Obstetrics and Gynecology, National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov; Professor at the Diagnostic Ultrasound Department, Pirogov Russian National Research Medical University.

Moscow, 117997

senchavyatka@mail.ruhttps://orcid.org/0009-0003-8135-3405МетелкинП. В.MetelkinP. V.

Метелкин Петр Валерьевич - директор центра стратегического анализа департамента проектного управления.

Москва

Petr V. Metelkin - Director, Center for Strategic Analysis, Department of Project Management, ZashchitaInfoTrans.

Moscow

https://orcid.org/0009-0008-3284-5995ЮрченкоО. В.YurchenkoO. V.

Юрченко Оксана Валерьевна - врач ультразвуковой диагностики.

Самара

Oxana V. Yurchenko - Ultrasonographer, Volga Medical Company.

Samara

Национальный медицинский исследовательский центр акушерства, гинекологии и перинатологии им. акад. В.И. Кулакова; Первый Московский государственный медицинский университет им. И.М. Сеченова (Сеченовский Университет)РоссияNational Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov; I.M. Sechenov First Moscow State Medical UniversityRussian FederationНациональный медицинский исследовательский центр акушерства, гинекологии и перинатологии им. акад. В.И. Кулакова; Рязанский государственный медицинский университет им. акад. И.П. ПавловаРоссияNational Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov; Ryazan State Medical University named after Academician I.P. PavlovRussian FederationНациональный медицинский исследовательский центр акушерства, гинекологии и перинатологии им. акад. В.И. Кулакова; Российский национальный исследовательский медицинский университет им. Н.И. ПироговаРоссияNational Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov; Pirogov Russian National Research Medical UniversityRussian FederationЗащитаИнфоТранс, Подведомственное учреждение Министерства транспорта Российской ФедерацииРоссияZashchitaInfoTrans, Ministry of Transport of the Russian FederationRussian FederationПриволжская медицинская компанияРоссияVolga Medical CompanyRussian Federation202430122024041420Copyright © Бокерия Е.Л., Яннаева Н.Е., Сенча А.Н., Метелкин П.В., Юрченко О.В., 20242024Бокерия Е.Л., Яннаева Н.Е., Сенча А.Н., Метелкин П.В., Юрченко О.В.Bokerija E.L., Yannaeva N.E., Sencha A.N., Metelkin P.V., Yurchenko O.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.innovmedkub.ru/jour/article/view/932Актуальность

Актуальность: Врожденные пороки сердца (ВПС) – это одни из наиболее распространенных пороков развития плода, которые встречаются с частотой 5–9 на 1 тыс. новорожденных. ВПС развития занимают второе место среди причин младенческой смертности и составляют 47% всех причин смерти от пороков развития.

Основным методом оценки анатомии и функции сердца является 2D УЗИ. Технологии искусственного интеллекта превосходно справляются с распознаванием изображений, это позволяет быстро сканировать и анализировать визуальную информацию, а также ускорить и упростить процесс ультразвуковой диагностики.

Во всех программах искусственного интеллекта (ИИ), применяемых в акушерстве, используются статические кадры. В нашей работе, проведенной на базе ФГБУ «НМИЦ АГП им. акад. В.И. Кулакова» в 2022–2023 гг., мы использовали видеофайлы, включающие от одного до пяти стандартных срезов сердца на каждого плода.

Цель исследования

Цель исследования: Создание набора данных для разработки ИИ-сервиса, повышающего качество диагностики ВПС плода.

Задача работы

Задача работы: Формирование алгоритма осмотра сердца плода при помощи ИИ. Полученное в результате медицинское заключение могло быть в одном из двух вариантов: «норма» – правильное строение сердца (ВПС нет); «не норма» – неправильное строение сердца (нельзя исключить наличие ВПС), рекомендована расширенная эхокардиография плода в кратчайшие сроки.

Методы

Методы: Исследование проводилось на сроке беременности 18–21 недель. Каждое исследование на одного пациента содержало видеофайлы пяти стандартных проекций сердца. Каждый срез представлен не менее 25 кадрами. Верификация была выполнена путем подтверждения/изменения диагноза врачом-экспертом, а также подтверждением диагноза после рождения.

Заключение

Заключение: В результате выполнения работ задача определения зон грудной клетки и сердца плода решена с точностью ~98%; задача классификации среза сердца на кадре решена с точностью ~82%, задача определения патологии на срезах сердца решена с точностью ~77%.

Background

Background: Congenital heart diseases (CHD) are one of the most common birth defects, occurring in 5-9 per 1000 newborns. CHD are the second leading cause of infant mortality and account for 47% of all causes of death from birth defects.

The main method for assessing the anatomy and function of the heart is 2-dimensional ultrasonography. Artificial intelligence (AI) technologies are great at recognizing images, thus facilitating quick scanning and analysis of visual information in order to speed up and simplify the diagnostic ultrasonography.

All AI software for obstetrics use static images. In our study conducted at the National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov (Moscow, Russian Federation) in 2022-2023, we used video files including 1-5 standard heart views for each fetus.

Objective

Objective: To create a data set for development of an AI tool that improves the quality of fetal CHD diagnosis and to develop an algorithm for examining the fetal heart using AI. Resulting medical reports could be either “normal” (correct structure of the heart; no sign of CHD) or “abnormal” (incorrect structure of the heart; CHD cannot be excluded; extended fetal echocardiography is recommended as soon as possible).

Materials and methods

Materials and methods: The examination was conducted at 18-21 weeks’ gestation. Each examination contained video files of 5 standard views of the heart per patient. Each view is at least 25 frames. Verification was performed by confirming/changing the diagnosis by a physician and confirming the diagnosis after birth.

Conclusions

Conclusions: As a result, the task of determining zones of the fetal chest and heart was solved with an approximate accuracy of 98%; the task of classifying the heart view on the frame was solved with an approximate accuracy of 82%, and the task of determining the disease on the heart views was solved with an approximate accuracy of 77%.

врожденные пороки сердцаискусственный интеллектфетальная эхокардиографияcongenital heart diseasesartificial intelligencefetal echocardiographyИсследование выполнено за счет гранта «Обеспечение информационного взаимодействия медицинских информационных систем ФГБУ «НМИЦ АГП им. В.И. Кулакова» Минздрава России с подсистемами ЕГИСЗ размеченных датасетов по направлению ультразвуковой скрининг второго триместра».The study was supported by the grant “Providing information exchange between medical information systems of the National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov of the Ministry of Health of the Russian Federation and subsystems of the Uniform State Health Information System’s labeled datasets in terms of second-trimester ultrasound screening”.ReferencesMcleod G, Shum K, Gupta T, et al. Echocardiography in congenital heart disease. Prog Cardiovasc Dis. 2018;61(5–6):468–475. PMID: 30445162. https://doi.org/10.1016/j.pcad.2018.11.004Mcleod G, Shum K, Gupta T, et al. Echocardiography in congenital heart disease. Prog Cardiovasc Dis. 2018;61(5–6):468– 475. PMID: 30445162. https://doi.org/10.1016/j.pcad.2018.11.004Yoon SA, Hong WH, Cho HJ. Congenital heart disease diagnosed with echocardiogram in newborns with asymptomatic cardiac murmurs: a systematic review. BMC Pediatr. 2020;20(1):322. PMID: 32605548. PMCID: PMC7325562. https://doi.org/10.1186/s12887-020-02212-8Yoon SA, Hong WH, Cho HJ. Congenital heart disease diagnosed with echocardiogram in newborns with asymptomatic cardiac murmurs: a systematic review. BMC Pediatr. 2020;20(1):322. PMID: 32605548. PMCID: PMC7325562. https://doi.org/10.1186/s12887-020-02212-8Gilboa SM, Salemi JL, Nembhard WN, Fixler DE, Correa A. Mortality resulting from congenital heart disease among children and adults in the United States, 1999 to 2006. Circulation. 2010;122(22):2254–2263. PMID: 21098447. PMCID: PMC4911018. https://doi.org/10.1161/CIRCULATIONAHA.110.947002Gilboa SM, Salemi JL, Nembhard WN, Fixler DE, Correa A. Mortality resulting from congenital heart disease among children and adults in the United States, 1999 to 2006. Circulation. 2010;122(22):2254–2263. PMID: 21098447. PMCID: PMC4911018. https://doi.org/10.1161/CIRCULATIONAHA.110.947002Под ред. Л.А. Бокерия, К.В. Шаталова. Детская кардиохирургия : Руководство для врачей. НЦССХ им. А.Н. Бакулева; 2016:24–40.Bockeria LA, Shatalov KV, eds. Pediatric Heart Surgery : A Physicians’ Guide. NCSSH im. A.N. Bakuleva; 2016:24–40. (In Russ.).Facts about Critical Congenital Heart Defects. Centers for Disease Control and Prevention. Accessed February 23, 2024. https://web.archive.org/web/20240223071414/http://www.cdc.gov/ncbddd/heartdefects/cchd-facts.htmlFacts about Critical Congenital Heart Defects. Centers for Disease Control and Prevention. Accessed February 23, 2024. https://web.archive.org/web/20240223071414/http://www.cdc.gov/ncbddd/heartdefects/cchd-facts.htmlBravo-Valenzuela NJ, Peixoto AB, Araujo Júnior E. Prenatal diagnosis of congenital heart disease: a review of current knowledge. Indian Heart J. 2018;70(1):150–164. PMID: 29455772. PMCID: PMC5903017. https://doi.org/10.1016/j.ihj.2017.12.005Bravo-Valenzuela NJ, Peixoto AB, Araujo Júnior E. Prenatal diagnosis of congenital heart disease: a review of current knowledge. Indian Heart J. 2018;70(1):150–164. PMID: 29455772. PMCID: PMC5903017. https://doi.org/10.1016/j.ihj.2017.12.005Scott M, Neal AE. Congenital heart disease. Prim Care. 2021;48(3):351–366. PMID: 34311844. https://doi.org/10.1016/j.pop.2021.04.005Scott M, Neal AE. Congenital heart disease. Prim Care. 2021;48(3):351–366. PMID: 34311844. https://doi.org/10.1016/j.pop.2021.04.005Bensemlali M, Bajolle F, Laux D, et al. Neonatal management and outcomes of prenatally diagnosed CHDs. Cardiol Young. 2017;27(2):344–353. PMID: 27225605. https://doi.org/10.1017/S1047951116000639Bensemlali M, Bajolle F, Laux D, et al. Neonatal management and outcomes of prenatally diagnosed CHDs. Cardiol Young. 2017;27(2):344–353. PMID: 27225605. https://doi.org/10.1017/S1047951116000639Salomon LJ, Alfirevic Z, Berghella V, et al; ISUOG Clinical Standards Committee. Practice guidelines for performance of the routine mid-trimester fetal ultrasound scan. Ultrasound Obstet Gynecol. 2011;37(1):116–126. PMID: 20842655. https://doi.org/10.1002/uog.8831Salomon LJ, Alfirevic Z, Berghella V, et al; ISUOG Clinical Standards Committee. Practice guidelines for performance of the routine mid-trimester fetal ultrasound scan. Ultrasound Obstet Gynecol. 2011;37(1):116–126. PMID: 20842655. https://doi.org/10.1002/uog.8831Bak GS, Shaffer BL, Madriago E, et al. Detection of fetal cardiac anomalies: cost-effectiveness of increased number of cardiac views. Ultrasound Obstet Gynecol. 2020;55(6):758–767. PMID: 31945242. https://doi.org/10.1002/uog.21977Bak GS, Shaffer BL, Madriago E, et al. Detection of fetal cardiac anomalies: cost-effectiveness of increased number of cardiac views. Ultrasound Obstet Gynecol. 2020;55(6):758–767. PMID: 31945242. https://doi.org/10.1002/uog.21977Carvalho JS, Axt-Fliedner R, Chaoui R, et al. ISUOG practice guidelines (updated): fetal cardiac screening. Ultrasound Obstet Gynecol. 2023;61(6):788–803. PMID: 37267096. https://doi.org/10.1002/uog.26224Carvalho JS, Axt-Fliedner R, Chaoui R, et al. ISUOG practice guidelines (updated): fetal cardiac screening. Ultrasound Obstet Gynecol. 2023;61(6):788–803. PMID: 37267096. https://doi.org/10.1002/uog.26224Donofrio MT, Moon-Grady AJ, Hornberger LK, et al; American Heart Association Adults With Congenital Heart Disease Joint Committee of the Council on Cardiovascular Disease in the Young and Council on Clinical Cardiology, Council on Cardiovascular Surgery and Anesthesia, and Council on Cardiovascular and Stroke Nursing. Diagnosis and treatment of fetal cardiac disease: a scientific statement from the American Heart Association. Circulation. 2014;129(21):2183–2242. Published correction appears in Circulation. 2014;129(21):e512. PMID: 24763516. https://doi.org/10.1161/01.cir.0000437597.44550.5dDonofrio MT, Moon-Grady AJ, Hornberger LK, et al; American Heart Association Adults With Congenital Heart Disease Joint Committee of the Council on Cardiovascular Disease in the Young and Council on Clinical Cardiology, Council on Cardiovascular Surgery and Anesthesia, and Council on Cardiovascular and Stroke Nursing. Diagnosis and treatment of fetal cardiac disease: a scientific statement from the American Heart Association. Circulation. 2014;129(21):2183–2242. Published correction appears in Circulation. 2014;129(21):e512. PMID: 24763516. https://doi.org/10.1161/01.cir.0000437597.44550.5dPuri K, Allen HD, Qureshi AM. Congenital heart disease. Pediatr Rev. 2017;38(10):471–486. PMID: 28972050. https://doi.org/10.1542/pir.2017-0032Puri K, Allen HD, Qureshi AM. Congenital heart disease. Pediatr Rev. 2017;38(10):471–486. PMID: 28972050. https://doi.org/10.1542/pir.2017-0032Sklansky M, DeVore GR. Fetal cardiac screening: what are we (and our guidelines) doing wrong?. J Ultrasound Med. 2016;35(4):679–681. PMID: 26969599. https://doi.org/10.7863/ultra.15.07021Sklansky M, DeVore GR. Fetal cardiac screening: what are we (and our guidelines) doing wrong?. J Ultrasound Med. 2016;35(4):679–681. PMID: 26969599. https://doi.org/10.7863/ultra.15.07021Sun HY, Proudfoot JA, McCandless RT. Prenatal detection of critical cardiac outflow tract anomalies remains suboptimal despite revised obstetrical imaging guidelines. Congenit Heart Dis. 2018;13(5):748–756. PMID: 30022603. PMCID: PMC7953202. https://doi.org/10.1111/chd.12648Sun HY, Proudfoot JA, McCandless RT. Prenatal detection of critical cardiac outflow tract anomalies remains suboptimal despite revised obstetrical imaging guidelines. Congenit Heart Dis. 2018;13(5):748–756. PMID: 30022603. PMCID: PMC7953202. https://doi.org/10.1111/chd.12648Sobhaninia Z, Rafiei S, Emami A, et al. Fetal ultrasound image segmentation for measuring biometric parameters using multi-task deep learning. Annu Int Conf IEEE Eng Med Biol Soc. 2019;2019:6545–6548. PMID: 31947341. https://doi.org/10.1109/EMBC.2019.8856981Sobhaninia Z, Rafiei S, Emami A, et al. Fetal ultrasound image segmentation for measuring biometric parameters using multi-task deep learning. Annu Int Conf IEEE Eng Med Biol Soc. 2019;2019:6545–6548. PMID: 31947341. https://doi.org/10.1109/EMBC.2019.8856981Kuehn BM. Cardiac imaging on the cusp of an artificial intelligence revolution. Circulation. 2020;141(15):1266– 1267. PMID: 32282247. https://doi.org/10.1161/CIRCULATIONAHA.120.046760Kuehn BM. Cardiac imaging on the cusp of an artificial intelligence revolution. Circulation. 2020;141(15):1266– 1267. PMID: 32282247. https://doi.org/10.1161/CIRCULATIONAHA.120.046760Hannun AY, Rajpurkar P, Haghpanahi M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 2019;25(1):65–69. Published correction appears in Nat Med. 2019;25(3):530. PMID: 30617320. PMCID: PMC6784839. https://doi.org/10.1038/s41591-018-0268-3Hannun AY, Rajpurkar P, Haghpanahi M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 2019;25(1):65–69. Published correction appears in Nat Med. 2019;25(3):530. PMID: 30617320. PMCID: PMC6784839. https://doi.org/10.1038/s41591-018-0268-3Brandt V, Emrich T, Schoepf UJ, et al. Ischemia and outcome prediction by cardiac CT based machine learning. Int J Cardiovasc Imaging. 2020;36(12):2429–2439. PMID: 32623625. https://doi.org/10.1007/s10554-020-01929-yBrandt V, Emrich T, Schoepf UJ, et al. Ischemia and outcome prediction by cardiac CT based machine learning. Int J Cardiovasc Imaging. 2020;36(12):2429–2439. PMID: 32623625. https://doi.org/10.1007/s10554-020-01929-ySlomka PJ, Miller RJ, Isgum I, Dey D. Application and translation of artificial intelligence to cardiovascular imaging in nuclear medicine and noncontrast CT. Semin Nucl Med. 2020;50(4):357–366. PMID: 32540032. https://doi.org/10.1053/j.semnuclmed.2020.03.004Slomka PJ, Miller RJ, Isgum I, Dey D. Application and translation of artificial intelligence to cardiovascular imaging in nuclear medicine and noncontrast CT. Semin Nucl Med. 2020;50(4):357–366. PMID: 32540032. https://doi.org/10.1053/j.semnuclmed.2020.03.004Arnaout R, Curran L, Chinn E, Zhao Y, Moon-Grady A. Deep-learning models improve on community-level diagnosis for common congenital heart disease lesions. arXiv. Deposited September 19, 2018. https://doi.org/10.48550/arXiv.1809.06993Arnaout R, Curran L, Chinn E, Zhao Y, Moon-Grady A. Deep-learning models improve on community-level diagnosis for common congenital heart disease lesions. arXiv. Deposited September 19, 2018. https://doi.org/10.48550/arXiv.1809.06993Arnaout R. Toward a clearer picture of health. Nat Med. 2019;25(1):12. PMID: 30613101. https://doi.org/10.1038/s41591-018-0318-xArnaout R. Toward a clearer picture of health. Nat Med. 2019;25(1):12. PMID: 30613101. https://doi.org/10.1038/s41591-018-0318-xOuyang D, He B, Ghorbani A, et al. Video-based AI for beat-to-beat assessment of cardiac function. Nature. 2020;580(7802):252–256. PMID: 32269341. PMCID: PMC8979576. https://doi.org/10.1038/s41586-020-2145-8Ouyang D, He B, Ghorbani A, et al. Video-based AI for beat-to-beat assessment of cardiac function. Nature. 2020;580(7802):252–256. PMID: 32269341. PMCID: PMC8979576. https://doi.org/10.1038/s41586-020-2145-8Yeo L, Romero R. Fetal Intelligent Navigation Echocardiography (FINE): a novel method for rapid, simple, and automatic examination of the fetal heart. Ultrasound Obstet Gynecol. 2013;42(3):268–284. PMID: 24000158. PMCID: PMC9651141. https://doi.org/10.1002/uog.12563Yeo L, Romero R. Fetal Intelligent Navigation Echocardiography (FINE): a novel method for rapid, simple, and automatic examination of the fetal heart. Ultrasound Obstet Gynecol. 2013;42(3):268–284. PMID: 24000158. PMCID: PMC9651141. https://doi.org/10.1002/uog.12563Shin HC, Roth HR, Gao M, et al. Deep Convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging. 2016;35(5):1285–1298. PMID: 26886976. PMCID: PMC4890616. https://doi.org/10.1109/TMI.2016.2528162Shin HC, Roth HR, Gao M, et al. Deep Convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging. 2016;35(5):1285–1298. PMID: 26886976. PMCID: PMC4890616. https://doi.org/10.1109/TMI.2016.2528162Torrents-Barrena J, Piella G, Masoller N, et al. Segmentation and classification in MRI and US fetal imaging: recent trends and future prospects. Med Image Anal. 2019;51:61–88. PMID: 30390513. https://doi.org/10.1016/j.media.2018.10.003Torrents-Barrena J, Piella G, Masoller N, et al. Segmentation and classification in MRI and US fetal imaging: recent trends and future prospects. Med Image Anal. 2019;51:61–88. PMID: 30390513. https://doi.org/10.1016/j.media.2018.10.003Zhang B, Liu H, Luo H, Li K. Automatic quality assessment for 2D fetal sonographic standard plane based on multitask learning. Medicine (Baltimore). 2021;100(4):e24427. PMID: 33530242. PMCID: PMC7850658. https://doi.org/10.1097/MD.0000000000024427Zhang B, Liu H, Luo H, Li K. Automatic quality assessment for 2D fetal sonographic standard plane based on multitask learning. Medicine (Baltimore). 2021;100(4):e24427. PMID: 33530242. PMCID: PMC7850658. https://doi.org/10.1097/MD.0000000000024427Nurmaini S, Rachmatullah MN, Sapitri AI, et al. Deep learning-based computer-aided fetal echocardiography: application to heart standard view segmentation for congenital heart defects detection. Sensors (Basel). 2021;21(23):8007. PMID: 34884008. PMCID: PMC8659935. https://doi.org/10.3390/s21238007Nurmaini S, Rachmatullah MN, Sapitri AI, et al. Deep learning-based computer-aided fetal echocardiography: application to heart standard view segmentation for congenital heart defects detection. Sensors (Basel). 2021;21(23):8007. PMID: 34884008. PMCID: PMC8659935. https://doi.org/10.3390/s21238007Wu H, Wu B, Lai F, et al. Application of artificial intelligence in anatomical structure recognition of standard section of fetal heart. Comput Math Methods Med. 2023;2023:5650378. PMID: 36733613. PMCID: PMC9889146. https://doi.org/10.1155/2023/5650378Wu H, Wu B, Lai F, et al. Application of artificial intelligence in anatomical structure recognition of standard section of fetal heart. Comput Math Methods Med. 2023;2023:5650378. PMID: 36733613. PMCID: PMC9889146. https://doi.org/10.1155/2023/5650378

The authors declare that there are no conflicts of interest present.