Machine Learning to Predict Pre-Eclampsia and Intrauterine Growth Restriction in Pregnant Women

Gómez-Jemes, Lola; Oprescu, Andreea Madalina; Chimenea, Ángel; García‐Díaz, Lutgardo; Romero-Ternero, M. C.

doi:10.3390/electronics11193240

“…Of these, three studies used LR combined with Random Forest (RF) [51], Stochastic Gradient Decent & RF [27], and SVM models [52]. Two studies used multiple models [25,53]. One study used a combination of Auto-Contractive Map (ACM) & Activation and Competition System (ACS) [54].…”

Section: Study Characteristicsmentioning

confidence: 99%

Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis

Rescinito

¹

,

Ratti

²

,

Payedimarri

³

et al. 2023

View full text Add to dashboard Cite

Background: IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial for obtaining positive outcomes for the newborn. In recent years Artificial intelligence (AI) and machine learning (ML) techniques are being used to identify risk factors and provide early prediction of IUGR. We performed a systematic review (SR) and meta-analysis (MA) aimed to evaluate the use and performance of AI/ML models in detecting fetuses at risk of IUGR. Methods: We conducted a systematic review according to the PRISMA checklist. We searched for studies in all the principal medical databases (MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and Cochrane). To assess the quality of the studies we used the JBI and CASP tools. We performed a meta-analysis of the diagnostic test accuracy, along with the calculation of the pooled principal measures. Results: We included 20 studies reporting the use of AI/ML models for the prediction of IUGR. Out of these, 10 studies were used for the quantitative meta-analysis. The most common input variable to predict IUGR was the fetal heart rate variability (n = 8, 40%), followed by the biochemical or biological markers (n = 5, 25%), DNA profiling data (n = 2, 10%), Doppler indices (n = 3, 15%), MRI data (n = 1, 5%), and physiological, clinical, or socioeconomic data (n = 1, 5%). Overall, we found that AI/ML techniques could be effective in predicting and identifying fetuses at risk for IUGR during pregnancy with the following pooled overall diagnostic performance: sensitivity = 0.84 (95% CI 0.80–0.88), specificity = 0.87 (95% CI 0.83–0.90), positive predictive value = 0.78 (95% CI 0.68–0.86), negative predictive value = 0.91 (95% CI 0.86–0.94) and diagnostic odds ratio = 30.97 (95% CI 19.34–49.59). In detail, the RF-SVM (Random Forest–Support Vector Machine) model (with 97% accuracy) showed the best results in predicting IUGR from FHR parameters derived from CTG. Conclusions: our findings showed that AI/ML could be part of a more accurate and cost-effective screening method for IUGR and be of help in optimizing pregnancy outcomes. However, before the introduction into clinical daily practice, an appropriate algorithmic improvement and refinement is needed, and the importance of quality assessment and uniform diagnostic criteria should be further emphasized.

show abstract

“…The testing process rigorously evaluates the performance of our model, allowing us to assess the degree of generalization of our model to unseen data. This generalization performance is an important aspect of any machine learning model, as it indicates the model's ability to make accurate predictions on new data, not limited to the specific examples it was trained on [39].…”

Section: Training and Testing With Encrypted Chunksmentioning

confidence: 99%

Efficient and Privacy-Preserving Image Classification Using Homomorphic Encryption and Chunk-based Convolutional Neural Network

Jia,

Cai,

Yang

et al. 2023

Preprint

0

View full text Add to dashboard Cite

Image feature categorization has emerged as a crucial component in many domains, including computer vision, machine learning, and biometrics, in the dynamic environment of big data and cloud computing. It is extremely difficult to guarantee image data security, privacy, and computing efficiency while also lowering storage and transmission costs. This paper introduces a novel method for classifying image features that combines multilevel homomorphic encryption and image data partitioning in an integrated manner. We employ a novel partitioning strategy to reduce computational complexity, significantly reducing computational load and improving classification accuracy. In the quest for increased data security and privacy, we introduce a novel, fully homomorphic encryption approach specialized to partitioned images. To counter the inherent complexity of encryption, we devise a compound encryption strategy that exploits the full potential of homomorphic computation, with an explicit objective to curtail computational and storage overheads. Evidently superior to conventional methods, our methodology showcases pronounced benefits in computational efficiency, storage and transmission cost reduction, and robust security and privacy preservation. Hence, the methodology put forth in this paper presents a pioneering and efficacious resolution to the multifaceted challenges of image feature classification within the intricate milieu of cloud computing and big data.

show abstract

“…The testing process rigorously evaluates the performance of our model, allowing us to assess the degree of generalization of our model to unseen data. This generalization performance is an important aspect of any machine learning model, as it indicates the model's ability to make accurate predictions on new data, not limited to the specific examples it was trained on [49]. 2.…”

Section: Training and Testing With Encrypted Chunksmentioning

confidence: 99%

Efficient and privacy-preserving image classification using homomorphic encryption and chunk-based convolutional neural network

Jia,

Cai,

Yang

et al. 2023

J Cloud Comp

4

0

View full text Add to dashboard Cite

Image feature categorization has emerged as a crucial component in many domains, including computer vision, machine learning, and biometrics, in the dynamic environment of big data and cloud computing. It is extremely difficult to guarantee image data security, privacy, and computing efficiency while also lowering storage and transmission costs. This paper introduces a novel method for classifying image features that combines multilevel homomorphic encryption and image data partitioning in an integrated manner. We employ a novel partitioning strategy to reduce computational complexity, significantly reducing computational load and improving classification accuracy. In the quest for increased data security and privacy, we introduce a novel, fully homomorphic encryption approach specialized to partitioned images. To counter the inherent complexity of encryption, we devise a compound encryption strategy that exploits the full potential of homomorphic computation, with an explicit objective to curtail computational and storage overheads. Evidently superior to conventional methods, our methodology showcases pronounced benefits in computational efficiency, storage and transmission cost reduction, and robust security and privacy preservation. Hence, the methodology put forth in this paper presents a pioneering and efficacious resolution to the multifaceted challenges of image feature classification within the intricate milieu of cloud computing and big data.

show abstract

Machine Learning to Predict Pre-Eclampsia and Intrauterine Growth Restriction in Pregnant Women

Cited by 15 publications

References 32 publications

Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis

Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis

Efficient and Privacy-Preserving Image Classification Using Homomorphic Encryption and Chunk-based Convolutional Neural Network

Efficient and privacy-preserving image classification using homomorphic encryption and chunk-based convolutional neural network

Contact Info

Product

Resources

About