Abstract:This study reviews recent advances on the application of artificial intelligence for the early diagnosis of various maternal-fetal conditions such as preterm birth and abnormal fetal growth. It is found in this study that various machine learning methods have been successfully employed for different kinds of data capture with regard to early diagnosis of maternal-fetal conditions. With the more popular use of artificial intelligence, ethical issues should also be considered accordingly.
“…At present, the application of radiomics for CUS is relatively limited, as it is in the initial stage and is mostly focused on the separation and cognition of brain structures. 19,20 This study was based on multiplanar gray-scale ultrasound images of the brain to construct a radiomics model for predicting premature infants' WMI, evaluate its diagnostic efficacy, use the model to dynamically monitor the recovery status of WMI, and explore its relationship with neurodevelopment to provide a new method for early and accurate diagnosis and prognosis judgment of premature infants with WMI.…”
ObjectivesTo develop and evaluate a multiplanar radiomics model based on cranial ultrasound (CUS) to predict white matter injury (WMI) in premature infants and explore its correlation with neurodevelopment.MethodsWe retrospectively reviewed 267 premature infants. The radiomics features were extracted from five standard sections of CUS. The Spearman's correlation coefficient combined with the least absolute shrinkage and selection operator (LASSO) was applied to select features and build radiomics signature, and a multiplanar radiomics model was constructed based on the radiomics signature of five planes. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC). Infants with WMI were re‐examined by ultrasound at 2 and 4 weeks after birth, and the recovery degree of WMI was evaluated using multiplanar radiomics. The relationship between WMI and the recovery degree and neurodevelopment was analyzed.ResultsThe AUC of the multiplanar radiomics in the training and validation sets were 0.94 and 0.91, respectively. The neurodevelopmental function scores in infants with WMI were significantly lower than those in healthy preterm infants and full‐term newborns (P < .001). There were statistically significant differences in the neurodevelopmental function scores of infants between the 2‐ and 4‐week lesion disappearance and 4‐week lesion persistence (P < .001).ConclusionsThe multiplanar radiomics model showed a good performance in predicting the WMI of premature infants. It can not only provide objective and accurate results but also dynamically monitor the degree of recovery of WMI to predict the prognosis of premature infants.
“…At present, the application of radiomics for CUS is relatively limited, as it is in the initial stage and is mostly focused on the separation and cognition of brain structures. 19,20 This study was based on multiplanar gray-scale ultrasound images of the brain to construct a radiomics model for predicting premature infants' WMI, evaluate its diagnostic efficacy, use the model to dynamically monitor the recovery status of WMI, and explore its relationship with neurodevelopment to provide a new method for early and accurate diagnosis and prognosis judgment of premature infants with WMI.…”
ObjectivesTo develop and evaluate a multiplanar radiomics model based on cranial ultrasound (CUS) to predict white matter injury (WMI) in premature infants and explore its correlation with neurodevelopment.MethodsWe retrospectively reviewed 267 premature infants. The radiomics features were extracted from five standard sections of CUS. The Spearman's correlation coefficient combined with the least absolute shrinkage and selection operator (LASSO) was applied to select features and build radiomics signature, and a multiplanar radiomics model was constructed based on the radiomics signature of five planes. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC). Infants with WMI were re‐examined by ultrasound at 2 and 4 weeks after birth, and the recovery degree of WMI was evaluated using multiplanar radiomics. The relationship between WMI and the recovery degree and neurodevelopment was analyzed.ResultsThe AUC of the multiplanar radiomics in the training and validation sets were 0.94 and 0.91, respectively. The neurodevelopmental function scores in infants with WMI were significantly lower than those in healthy preterm infants and full‐term newborns (P < .001). There were statistically significant differences in the neurodevelopmental function scores of infants between the 2‐ and 4‐week lesion disappearance and 4‐week lesion persistence (P < .001).ConclusionsThe multiplanar radiomics model showed a good performance in predicting the WMI of premature infants. It can not only provide objective and accurate results but also dynamically monitor the degree of recovery of WMI to predict the prognosis of premature infants.
“… 37 Furthermore, AI can contribute to continuous monitoring by carefully observing maternal health parameters, such as blood pressure and glucose levels, and providing real-time alerts for any deviations. 38 , 39 These applications highlight the vast potential of AI in feto-maternal health, where it promises to enhance prenatal care and address the complex challenges associated with maternal and fetal health. Figure 1 This figure illustrates the systematic processing of the data records through various AI models.…”
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.
“…New approaches in computer science and statistics are required to identify actionable insights within these clinical conditions ( de Marvao et al, 2020 ). Machine learning is a branch of artificial intelligence that uses computer algorithms to identify patterns within large raw datasets, acquire knowledge and apply this to different tasks ( Ahn and Lee, 2022 ; Dhombres, 2022 ). A single machine learning model could analyse more data than a clinician would encounter over the duration of the individual's career.…”
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