Development of e-health network for in-home pregnancy surveillance based on artificial intelligence

Kazantsev, Alexander; Ponomareva, Julia; Kazantsev, Pavel Aleksandrovich; Digilov, Rafael M.; Huang, Ping

doi:10.1109/bhi.2012.6211511

Cited by 25 publications

(18 citation statements)

References 3 publications

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“…The approximate Shannon entropy of the normalized power spectral density of a very-low-frequency fetal heart rhythm signal, the aposterior FHR entropy, was used for sonographic signal quality assurance. It was demonstrated that the aposterior FHR entropy is ideal for detecting signal loss [1].…”

Section: B a New Approach To Ultrasound Monitoring Of Pregnancymentioning

confidence: 99%

“…In 2012 the project team participated in the International Conference of the IEEE-EMBS on Biomedical Informatics (BHI 2012) presenting a paper on the principle of in-home pregnancy surveillance using personal smart monitors [1]. Experts have acknowledged the novelty of the approach: information technology for large-scale pervasive, ubiquitous pregnancy care using personal web monitors, artificial intelligence, and cloud computing.…”

Section: A Innovative Architecturementioning

confidence: 99%

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Development and validation of an AI-enabled mHealth technology for in-home pregnancy management

Kazantsev

Ponomareva

Kazantsev

2014

2014 International Conference on Information Science, Electronics and Electrical Engineering

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The solution of perinatal morbidity and mortality problem lies in the development of an information technology for large-scale eHealth-enabled management of pregnancy, making use of personal mobile web monitors, cloud computing and artificial intelligence. The solution leads to halving the indices of perinatal morbidity and mortality, as well as the costs of pregnancy care at the same time. Lowering the costs of care in turn ensures the investment attractiveness of the eHealthcare services, and makes them affordable to low budget care providers. The advances of the technology are substantiated by the results already obtained. The concept of large-scale eHealth pregnancy management, making use of mobile/ wearable personal monitors, artificial intelligence and cloud computing is first proposed.

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Section: B a New Approach To Ultrasound Monitoring Of Pregnancymentioning

confidence: 99%

Section: A Innovative Architecturementioning

confidence: 99%

Development and validation of an AI-enabled mHealth technology for in-home pregnancy management

Kazantsev

Ponomareva

Kazantsev

2014

2014 International Conference on Information Science, Electronics and Electrical Engineering

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“…The best results were obtained by Iraji et al [51] using the Portuguese database showing an accuracy of 99.5%. There have also been some attempts to translate this into clinical practice via the development of software such as Infant, PeriCALM [52,53], and Foetos [54] or the development of mobile/website applications [55,56] to provide additional support in the interpretation of CTG signals and therefore to improve the assessment of fetal status. However, there is no evidence on whether these systems really improve the prediction of fetal distress or acidemia compared to visual CTG interpretation alone, and reports about their clinical performance were not found.…”

Section: For Fetal Diagnosismentioning

confidence: 99%

Machine Learning in Fetal Cardiology: What to Expect

et al. 2020

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In fetal cardiology, imaging (especially echocardiography) has demonstrated to help in the diagnosis and monitoring of fetuses with a compromised cardiovascular system potentially associated with several fetal conditions. Different ultrasound approaches are currently used to evaluate fetal cardiac structure and function, including conventional 2-D imaging and M-mode and tissue Doppler imaging among others. However, assessment of the fetal heart is still challenging mainly due to involuntary movements of the fetus, the small size of the heart, and the lack of expertise in fetal echocardiography of some sonographers. Therefore, the use of new technologies to improve the primary acquired images, to help extract measurements, or to aid in the diagnosis of cardiac abnormalities is of great importance for optimal assessment of the fetal heart. Machine leaning (ML) is a computer science discipline focused on teaching a computer to perform tasks with specific goals without explicitly programming the rules on how to perform this task. In this re-view we provide a brief overview on the potential of ML techniques to improve the evaluation of fetal cardiac function by optimizing image acquisition and quantification/segmentation, as well as aid in improving the prenatal diagnoses of fetal cardiac remodeling and abnormalities.

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“…Early diagnosis and prevention of antenatal diseases are on the agenda now. Large-scale fetal monitoring could help if it were more pervasive [2,3]. However, there is the lack of ehealth technologies enabling low-cost remote services for broad-scale management of pregnancy.…”

Section: Introductionmentioning

confidence: 97%