Real‐time identification of fetal anomalies on ultrasound using artificial intelligence: what's next?

Drukker, Lior

doi:10.1002/uog.24869

Cited by 6 publications

(8 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Not only can INDIAMAN-20 be used in training personnel involved in obstetric screening ultrasound, but the textual sonographic descriptors illustrated in Table 1 may also be transformed easily into binary variables in order to be incorporated into artificial intelligence and deep learning tools that are currently being developed to aid prenatal ultrasound diagnosis [7][8][9] .…”

Section: Discussionmentioning

confidence: 99%

INDIAMAN‐20 (INstant DIAgnosis of 20 Major ANomalies) protocol: application of IOTA diagnostic strategy to fetal anomalies

Paladini

Franzè

Morena

et al. 2023

Ultrasound in Obstet & Gyne

View full text Add to dashboard Cite

What are the novel findings of this work?This study identifies sonographic descriptors for a series of 20 major fetal malformations that enable their instant diagnosis. What are the clinical implications of this work?The possibility to obtain an instant diagnosis of a major fetal anomaly through the use of sonographic descriptors may facilitate the training of residents and may also be used in algorithms for artificial intelligence-aided ultrasound.

show abstract

Section: Discussionmentioning

confidence: 99%

INDIAMAN‐20 (INstant DIAgnosis of 20 Major ANomalies) protocol: application of IOTA diagnostic strategy to fetal anomalies

Paladini

Franzè

Morena

et al. 2023

Ultrasound in Obstet & Gyne

View full text Add to dashboard Cite

show abstract

“…A structured training program in obstetric ultrasound, taking into account the local context and available cadres at the frontline, may be a constructive strategy 27,31 . With the advent of artificial intelligence, there is hope for future commercial products with the ability to support practitioners to undertake and interpret complex ultrasound procedures with high precision 32,33 . Such modern clinical decision support tools would be of great benefit in high-burden settings in which the number of highly skilled fetal medicine specialists falls far short of demand.…”

Section: Discussionmentioning

confidence: 99%

Standardization and quality control of Doppler and fetal biometric ultrasound measurements in low‐income setting

Ali

Byamugisha

Kawooya

et al. 2023

Ultrasound in Obstet & Gyne

View full text Add to dashboard Cite

What are the novel findings of this work?This is the first study in a low-resource setting to demonstrate that the quality of pulsed-wave Doppler and fetal biometry ultrasound measurements can be assessed reliably using freely available objective evaluation tools. What are the clinical implications of this work?Well-trained healthcare providers in underserved regions can perform Doppler and fetal biometry scans with consistency. The impact of in-service retraining and clinical audits on the quality of scans should be evaluated in future studies.

show abstract

“…AI, employing neural networks extracts and measures biometric parameters through segmentation techniques using ultrasound image planes. 68 These values are then fed into a neural network for detecting fetal anomalies. Various learning algorithms, particularly Feedforward neural networks based on backpropagation, have been thoroughly analyzed and compared.…”

Section: Ai For Fetal Monitoringmentioning

confidence: 99%

A Theoretical Exploration of Artificial Intelligence’s Impact on Feto-Maternal Health from Conception to Delivery

Yaseen,

Rather

2024

IJWH

View full text Add to dashboard Cite

The implementation of Artificial Intelligence (AI) in healthcare is enhancing diagnostic accuracy in clinical setups. The use of AI in healthcare is steadily increasing with advancing technology, extending beyond disease diagnosis to encompass roles in fetomaternal health. AI harnesses Machine Learning (ML), Natural Language Processing (NLP), Artificial Neural Networks (ANN), and computer vision to analyze data and draw conclusions. Considering maternal health, ML analyzes vast datasets to predict maternal and fetal health outcomes, while NLP interprets medical texts and patient records to assist in diagnosis and treatment decisions. ANN models identify patterns in complex feto-maternal medical data, aiding in risk assessment and intervention planning whereas, computer vision enables the analysis of medical images for early detection of feto-maternal complications. AI facilitates early pregnancy detection, genetic screening, and continuous monitoring of maternal health parameters, providing real-time alerts for deviations, while also playing a crucial role in the early detection of fetal abnormalities through enhanced ultrasound imaging, contributing to informed decision-making. This review investigates into the application of AI, particularly through predictive models, in addressing the monitoring of feto-maternal health. Additionally, it examines potential future directions and challenges associated with these applications.

show abstract

Real‐time identification of fetal anomalies on ultrasound using artificial intelligence: what's next?

Abstract: Linked article: This Editorial comments on the article by Lin et al. Click https://obgyn.onlinelibrary.wiley.com/doi/full/10.1002/uog.24843 to view the article.

Cited by 6 publications

References 20 publications

INDIAMAN‐20 (INstant DIAgnosis of 20 Major ANomalies) protocol: application of IOTA diagnostic strategy to fetal anomalies

INDIAMAN‐20 (INstant DIAgnosis of 20 Major ANomalies) protocol: application of IOTA diagnostic strategy to fetal anomalies

Standardization and quality control of Doppler and fetal biometric ultrasound measurements in low‐income setting

A Theoretical Exploration of Artificial Intelligence’s Impact on Feto-Maternal Health from Conception to Delivery

Contact Info

Product

Resources

About