Concordance of the risk of neonatal respiratory morbidity assessed by quantitative ultrasound lung texture analysis in fetuses of twin pregnancies

Moreno-Espinosa, Ana L.; Hawkins-Villarreal, Ameth; Burgos-Artizzu, Xavier P.; Coronado-Gutiérrez, David; Castelazo, Santiago; Lip‐Sosa, D.L.; Fuenzalida, Javiera; Gallo, Dahiana; Peña-Ramírez, Tatiana; Zuazagoitia, Paula; Muñoz, Miriam Estébanez; Parra-Cordero, Mauro; Gratacós, E.; Palacio, Montse

doi:10.1038/s41598-022-13047-x

Cited by 2 publications

(2 citation statements)

References 41 publications

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“…They predicted the incidence of neonatal respiratory distress syndrome with an accuracy of 86.5% and a specificity of 88.6%. Another study [46] applied quantusFLM to twin pregnancy groups. Xia et al [47] developed a normal fetal lung GA grading model to identify abnormal fetal lung development caused by maternal gestational diseases.…”

Section: Intelligent Measurement Of Fetal Nuchal Translucency (Nt) Th...mentioning

confidence: 99%

Application and Progress of Artificial Intelligence in Fetal Ultrasound

Xiao

Zhang

Zhu

et al. 2023

JCM

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Prenatal ultrasonography is the most crucial imaging modality during pregnancy. However, problems such as high fetal mobility, excessive maternal abdominal wall thickness, and inter-observer variability limit the development of traditional ultrasound in clinical applications. The combination of artificial intelligence (AI) and obstetric ultrasound may help optimize fetal ultrasound examination by shortening the examination time, reducing the physician’s workload, and improving diagnostic accuracy. AI has been successfully applied to automatic fetal ultrasound standard plane detection, biometric parameter measurement, and disease diagnosis to facilitate conventional imaging approaches. In this review, we attempt to thoroughly review the applications and advantages of AI in prenatal fetal ultrasound and discuss the challenges and promises of this new field.

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Section: Intelligent Measurement Of Fetal Nuchal Translucency (Nt) Th...mentioning

confidence: 99%

Application and Progress of Artificial Intelligence in Fetal Ultrasound

Xiao

Zhang

Zhu

et al. 2023

JCM

View full text Add to dashboard Cite

show abstract

“…Gender [ 4 ], birth order [ 5 ], chorionicity [ 3 ], and birthweight discordance [ 6 , 7 ] are some of the factors which may affect the risk of NMR. In twin pregnancies, there is usually disparity between twins, so the risk of respiratory morbidity may be different for each infant in each twin pair [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Neonatal Respiratory Morbidity Assessed by Quantitative Ultrasound Lung Texture Analysis in Twin Pregnancies

Moreno-Espinosa

Hawkins-Villarreal

Coronado-Gutiérrez

et al. 2022

JCM

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The objective of this study was to evaluate the performance of quantitative ultrasound of fetal lung texture analysis in predicting neonatal respiratory morbidity (NRM) in twin pregnancies. This was an ambispective study involving consecutive cases. Eligible cases included twin pregnancies between 27.0 and 38.6 weeks of gestation, for which an ultrasound image of the fetal thorax was obtained within 48 h of delivery. Images were analyzed using quantusFLM® version 3.0. The primary outcome of this study was neonatal respiratory morbidity, defined as the occurrence of either transient tachypnea of the newborn or respiratory distress syndrome. The performance of quantusFLM® in predicting NRM was analyzed by matching quantitative ultrasound analysis and clinical outcomes. This study included 166 images. Neonatal respiratory morbidity occurred in 12.7% of cases, and it was predicted by quantusFLM® analysis with an overall sensitivity of 42.9%, specificity of 95.9%, positive predictive value of 60%, and negative predictive value of 92.1%. The accuracy was 89.2%, with a positive likelihood ratio of 10.4, and a negative likelihood ratio of 0.6. The results of this study demonstrate the good prediction capability of NRM in twin pregnancies using a non-invasive lung texture analysis software. The test showed an overall good performance with high specificity, negative predictive value, and accuracy.

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Concordance of the risk of neonatal respiratory morbidity assessed by quantitative ultrasound lung texture analysis in fetuses of twin pregnancies

Cited by 2 publications

References 41 publications

Application and Progress of Artificial Intelligence in Fetal Ultrasound

Application and Progress of Artificial Intelligence in Fetal Ultrasound

Prediction of Neonatal Respiratory Morbidity Assessed by Quantitative Ultrasound Lung Texture Analysis in Twin Pregnancies

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