Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time

Matthew, Jacqueline; Skelton, Emily; Zimmer, Veronika A.; Gómez, Alberto; Wheeler, Gavin; Toussaint, Nicolas; Liu, Tianrui; Budd, Samuel; Lloyd, Karen G.; Wright, Robert; Deng, Shujie; Ghavami, Navid; Sinclair, Matthew; Meng, Qingjie; Kainz, Bernhard; Schnabel, Julia A.; Rueckert, Daniel; Razavi, Reza; Simpson, John; Hajnal, Jo

doi:10.1002/pd.6059

Cited by 24 publications

(23 citation statements)

References 24 publications

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“…Matthews et al evaluated an AI system that provided feedback to the sonographer during live scanning via a traffic light system to inform the sonographer that the required anatomic views had been obtained in addition to automatically measuring fetal biometry. 105 Our review also highlights the potential of AI in low resource settings. Several studies focused on AI detection of placental location and categorization of the placenta as normal, low-lying, or placenta previa.…”

Section: Discussionmentioning

confidence: 95%

“…Six studies focused on anatomical evaluation of the fetus (Table 6). Two studies evaluated AI systems to automatically detect 13 standard anatomic planes 105,106 . In the study by Matthews et al., feedback during live scanning was provided via a traffic light system to inform the sonographer that the required anatomic views including fetal biometry had been obtained.…”

Section: Resultsmentioning

confidence: 99%

“…Two studies evaluated AI systems to automatically detect 13 standard anatomic planes. 105,106 In the study by Matthews et al, feedback during live scanning was provided via a traffic light system to inform the sonographer that the required anatomic views including fetal biometry had been obtained. Sharma et al assessed the use of AI to comprehensively analyze and quantify operator clinical workflow in a spatio-temporal context during fetal morphology ultrasound to evaluate inter and intraobserver variability.…”

Section: Number Of Patients Inclusion Criteria Description Of Artific...mentioning

confidence: 99%

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Artificial intelligence in obstetric ultrasound: A scoping review

2023

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The objective is to summarize the current use of artificial intelligence (AI) in obstetric ultrasound. PubMed, Cochrane Library, and ClinicalTrials.gov databases were searched using the following keywords “neural networks”, OR “artificial intelligence”, OR “machine learning”, OR “deep learning”, AND “obstetrics”, OR “obstetrical”, OR “fetus”, OR “foetus”, OR “fetal”, OR “foetal”, OR “pregnancy”, or “pregnant”, AND “ultrasound” from inception through May 2022. The search was limited to the English language. Studies were eligible for inclusion if they described the use of AI in obstetric ultrasound. Obstetric ultrasound was defined as the process of obtaining ultrasound images of a fetus, amniotic fluid, or placenta. AI was defined as the use of neural networks, machine learning, or deep learning methods. The authors’ search identified a total of 127 papers that fulfilled our inclusion criteria. The current uses of AI in obstetric ultrasound include first trimester pregnancy ultrasound, assessment of placenta, fetal biometry, fetal echocardiography, fetal neurosonography, assessment of fetal anatomy, and other uses including assessment of fetal lung maturity and screening for risk of adverse pregnancy outcomes. AI holds the potential to improve the ultrasound efficiency, pregnancy outcomes in low resource settings, detection of congenital malformations and prediction of adverse pregnancy outcomes.

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Section: Discussionmentioning

confidence: 95%

Section: Resultsmentioning

confidence: 99%

Section: Number Of Patients Inclusion Criteria Description Of Artific...mentioning

confidence: 99%

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Artificial intelligence in obstetric ultrasound: A scoping review

2023

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“…Recently, artificial-intelligence, machine-learning and deep-learning methods have attracted strong interest around the world. These methods have already been tested in the diagnosis and prediction of many prenatal complications, such as Down syndrome, various structural anomalies identified by ultrasound and autism spectrum disorders [11][12][13][14] . In these studies, learning from dataset patterns enabled artificial-intelligence and machine-learning methods to identify interactions between variables and outcomes that are not accessible by linear methods 14,15 .…”

Section: Introductionmentioning

confidence: 99%

Machine‐learning‐based prediction of pre‐eclampsia using first‐trimester maternal characteristics and biomarkers

Ansbacher-Feldman

Syngelaki

Meiri³

et al. 2022

Ultrasound in Obstet & Gyne

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Objective To evaluate the accuracy of predicting the risk of developing pre‐eclampsia (PE) according to first‐trimester maternal demographic characteristics, medical history and biomarkers using artificial‐intelligence and machine‐learning methods. Methods The data were derived from prospective non‐interventional screening for PE at 11–13 weeks' gestation at two maternity hospitals in the UK. The data were divided into three subsets. The first set, including 30 437 subjects, was used to develop the training process, the second set of 10 000 subjects was utilized to optimize the machine‐learning hyperparameters and the third set of 20 352 subjects was coded and used for model validation. An artificial neural network was used to predict from the demographic characteristics and medical history the prior risk that was then combined with biomarker values to determine the risk of PE and preterm PE with delivery at < 37 weeks' gestation. An additional network was trained without including race as input. Biomarkers included uterine artery pulsatility index (UtA‐PI), mean arterial blood pressure (MAP), placental growth factor (PlGF) and pregnancy‐associated plasma protein‐A. All markers were entered using raw values without conversion into standardized multiples of the median. The prediction accuracy was estimated using the area under the receiver‐operating‐characteristics curve (AUC). We further computed the detection rate at 10%, 20% and 40% false‐positive rates (FPR). The impact of taking aspirin was also added. Shapley values were calculated to evaluate the contribution of each parameter to the prediction of risk. We used a non‐parametric test to compare the expected AUC with the one obtained when we randomly scrambled the labels and kept the predictions. For the general prediction, we performed 10 000 permutations of the labels. When the AUC was higher than the one obtained in all 10 000 permutations, we reported a P‐value of < 0.0001. For the race‐specific analysis, we performed 1000 permutations. When the AUC was higher than the AUC in permutations, we reported a P‐value of < 0.001. Results The detection rate for preterm PE vs no PE, at a 10% FPR, was 53.3% when screening by maternal factors only, and the corresponding AUC was 0.816; these increased to 75.3% and 0.909, respectively, with the addition of biomarkers into the model. Information on race was important for the prediction accuracy; when race was not used to train the model, at a 10% FPR, the detection rate of preterm PE vs no PE decreased to 34.5–45.5% (for different races) when screening by maternal factors only and to 55.0–62.1% when biomarkers were added. The major predictors of PE were high MAP and UtA‐PI, and low PlGF. The accuracy of prediction of all PE cases was lower than that for preterm PE. Aspirin use was recommended for cases who were at high risk of preterm PE. The AUC of all PE vs no PE was 0.770 when screening by maternal factors and 0.817 when the biomarkers were added; the respective detection rates, at a 10% FPR, were 41.3% and...

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“…[85][86][87] A step further is artificial intelligence which has already been used to facilitate anatomical assessment and pattern recognition. 88 Technology is quickly reshaping the world of ultrasound but advances in genetic testing are allowing clinicians to take prenatal testing a step further. Undeniably, in just about one decade, the available genetic tests have changed, evolved and multiplied and it is now standard practice to offer chromosomal microarray and exome-sequencing in fetuses with abnormal ultrasound findings.…”

Section: Summary and Implications Of The Main Findingsmentioning

confidence: 99%

Genetics meets ultrasound

Bardi¹

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Chapter 1 General introduction PART I Chapter 2 Prenatal diagnosis and pregnancy outcome of major structural anomalies detectable in the first-trimester: a population-based cohort study in the Netherlands Chapter 3 Effect of prenatal screening on trends in perinatal mortality associated with congenital anomalies before and after the introduction of prenatal screening: a population-based study in the Northern Netherlands PART II Chapter 4 Early detection of structural abnormalities in a primary care setting in The Netherlands Chapter 5 Is there still a role for Nuchal Translucency measurement in the changing paradigm of first trimester screening? Chapter 6 Additional value of advanced ultrasonography in pregnancies with two inconclusive cell-free DNA draws PART III Chapter 7 Timing of diagnosis of fetal structural abnormalities after the introduction of universal cell-free DNA in the absence of first-trimester anatomical screening Chapter 8 Psychological outcomes, knowledge and preferences of pregnant women on first-trimester screening for fetal structural abnormalities: a prospective cohort study Chapter 9 Organ-specific learning curves of sonographers performing firsttrimester anatomical screening and impact of score-based evaluation on ultrasound image quality Chapter 10 General Discussion

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Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time

Cited by 24 publications

References 24 publications

Artificial intelligence in obstetric ultrasound: A scoping review

Artificial intelligence in obstetric ultrasound: A scoping review

Machine‐learning‐based prediction of pre‐eclampsia using first‐trimester maternal characteristics and biomarkers

Genetics meets ultrasound

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