Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis
Abstract
:1. Introduction
- describe the most widely used AI/ML algorithms applied in IUGR diagnosis
- evaluate the performance of AI/ML models in terms of accuracy
- determine whether there is a combination of these methods that has been shown to provide better accuracy in early diagnosis.
2. Materials and Methods
2.1. Search Strategy
- MEDLINE (PubMed): “Fetal Growth Retardation [Mesh]) Machine Learning [Mesh]”, “Fetal growth restriction machine learning”, “Machine learning IUGR”, “(Artificial intelligence) AND (IUGR)”, “(Artificial Intelligence) AND (Fetal growth restriction)”, “(Artificial Intelligence) AND (Fetal growth retardation)”
- EMBASE: “‘artificial intelligence’/exp AND ‘intrauterine growth retardation’/exp EMTREE”, “‘machine learning’/exp AND ‘fetal growth retardation’/exp” AND “‘machine learning’/exp AND ‘fetal growth restriction’/exp”
- Scopus: “Fetal Growth Retardation Machine Learning”, “Fetal growth restriction machine learning”, “Machine learning IUGR”, “(Artificial intelligence) AND (IUGR)”, “(Artificial Intelligence) AND (Fetal growth restriction)” and “(Artificial Intelligence) AND (Fetal growth retardation)”
- CINAHL: “Fetal Growth Retardation Machine Learning”, “Fetal growth restriction Machine Learning”, “Machine learning IUGR”, “(Artificial intelligence) AND (IUGR)”, “(Artificial Intelligence) AND (Fetal growth restriction)”, “(Artificial Intelligence) AND (Fetal growth retardation)”
- Web of Science: “Machine learning IUGR”, “Fetal growth retardation machine learning”, “Fetal growth retardation machine learning”, “(artificial intelligence) and IUGR”, “(artificial intelligence) and (fetal growth restriction)”, “(artificial intelligence) and (fetal growth retardation)”
- Cochrane Database of Systematic Reviews: “Fetal Growth Retardation/restriction/IUGR AND Machine Learning”, “Fetal Growth Retardation/restriction/ IUGR AND Artificial intelligence”
2.2. Inclusion and Exclusion Criteria
2.3. Selection Criteria
2.4. Quality Assessments of Studies
2.5. Data Extraction
2.6. Statistical Analysis
3. Results
3.1. Study Selection
3.2. Study Characteristics
Author | Study Type | Setting | Sample (n) | Preg Time (weeks) | Methods | AI/ML Model | Outcomes | Measures (Accuracy) |
---|---|---|---|---|---|---|---|---|
Guo, Z. [50] | Obs. retr. case-control | China | 2199 | 12–28 | DNA profiling | LR | Using ML to predict FGR and BW | 79% |
Dahdoud, S. [58] | Obs. retr. case-control | USA | 80 | 18–39 | MRI | RUSBoost | Using ML to predict FGR and BW | 86% |
Lunghi, F. [45] | Obs. retr. case-control | Italy | 909 | 30–35 | FHR by CTG | SVM | Realizing an automatic system for identified FGR | 84% |
Magenes, G. [48] | Obs. retr. case-control | Italy | 100 | 30–35 | FHR by CTG | SVM | Realizing an automatic system for identified FGR | 78% |
Signorini, M. [60] | Obs. retr. case-control | Italy | 120 | 30–35 | FHR by CTG | RF (best) | Find the best classification ML model for identifying IUGR | 91% |
Crockart, I.C. [27] | Obs. prosp. case-control | USA and S. Africa | 6004 | 20–29 | FHR by CTG | Stochastic Gradient Descent, LR & RF | Using ML to predict FGR and BW | 91% |
Bahado–Singh, R. [46] | Obs. retr. case-control | USA | 80 | Delivery | Biochemical | SVM | Find the best classification ML model for identifying IUGR | 80% |
Pini, N. [47] | Obs. retr. case-control | Italy | 262 | 36–37 | FHR by CTG | RBF-SVM | Build a ML screener for late IUGR | 93% |
Magenes, G. [51] | Obs. retr. case-control | Italy | 122 | 30–35 | FHR by CTG | RF & LR | Find the best classification ML model for identifying IUGR | RF = 85%; LR = 83% |
Xu, C. [52] | Obs. retr. nested case-control | China | 810 | 12–27 | DNA profiling | SVM & LR | Find the best classification ML model for identifying IUGR | 83% |
Buscema, M. [54] | Obs. retr. case-control | Italy | 46 | Delivery | Biochemical | ACM & ACS | Find the best classification ML model for identifying IUGR | 87% |
Foltran, F. [55] | Obs. prosp. case-control | Italy | 46 | 20–32 | Biochemical | BN | Realizing an automatic system for identified FGR | 90% |
Street, M.E. [56] | Obs. retr. case-control | Italy | 48 | 20–32 | Biochemical | ANNS | Find the best classification ML model for identifying IUGR | 89% |
Ferrario, M. [57] | Obs. retr. case-control | Italy | 59 | 27–34 | FHR by CTG | LZ complexity | Realizing an automatic system for identified FGR | 91% |
Deval, R. [49] | Obs. retr. case-control | India | 214 | - | Biochemical | SVM, MLP | Using ML models to predict IUGR | SMO = 95.5%; MLP = 8.5% |
Gómez–Jemes, L. [53] | Obs. retr. case-control | Slovenia | 95 | 24–38 | Doppler indices: UA, sFIt-1, and PIGF values | Multi-models (extra-trees, RF) | Using ML models to predict pre-Eclampsia, IUGR | Extra trees = 78%, RF = 73% |
Sufriyana, H. [59] | Obs. prosp. case-control | Slovenia | 95 | 24–37 | Doppler indices: UA, sFIt–1, and PIGF values | CVR | Using ML models to predict pre-Eclampsia, IUGR | CVR = 90.6% |
Aslam, N. [61] | Obs. retr. case-control | Italy | 382 | 30–37 | FHR by CTG | SVM & RF | Using ML models to predict IUGR | RF = 97% |
Gürgen, F. [62] | Obs. retr. case-control | Turkey | 44 | <38 | Doppler indices: PI & RI of UA, MCA, DV, and AFI | SVM | Using ML models to predict IUGR | SVM = 81% |
Van, S.N. [25] | Obs. prosp. case-control | China | 75 | - | Physiological, clinical, and socioeconomic | Seven ML algorithms | Identify the latent risk clinical attributes using the ML algorithms. | 94.73% |
3.3. Performance of ML Models for IUGR Prediction: Qualitative Synthesis
3.3.1. Prediction of IUGR from Biochemical and Clinical Parameters
3.3.2. Prediction of IUGR from DNA Profiling
3.3.3. Prediction of IUGR from MRI Data
3.3.4. Prediction of IUGR from FHR Parameters by CTG
3.3.5. Prediction of IUGR from Doppler Indices
3.3.6. Detection of Latent Clinical Attributes for Children Born under IUGR Condition
3.4. Meta-Analysis
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
SR | Systematic Review |
MA | Meta Analysis |
AI | Artificial Intelligence |
ML | Machine Learning |
IUGR | Intra Uterine Growth Restriction |
SGA | Small for Gestational Age |
CTG | Cardiotocography |
FHR | Fetal Heart Rate |
RUSBoost | Random under sampling boosting |
ACM ACS | Auto Contractive Map and Activation and Competition System |
TNF-alfa | Tumor necrosis factor- Alfa |
IGF | Insulin-like growth factor |
UtA | Uterine artery |
sFIt-1 | soluble fms-like tyrosine kinase receptor-1 |
PIGF | Placental growth factor |
PI /RI | Pulsitility Index/Resistance Index |
UA | umbelical artery |
MCA/DV | middle celebral artery/ductus venosus |
AFI | amniotic fluid index |
MLP | multilayer perceptron |
CVR | classification via regression |
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Rescinito, R.; Ratti, M.; Payedimarri, A.B.; Panella, M. Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis. Healthcare 2023, 11, 1617. https://doi.org/10.3390/healthcare11111617
Rescinito R, Ratti M, Payedimarri AB, Panella M. Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis. Healthcare. 2023; 11(11):1617. https://doi.org/10.3390/healthcare11111617
Chicago/Turabian StyleRescinito, Riccardo, Matteo Ratti, Anil Babu Payedimarri, and Massimiliano Panella. 2023. "Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis" Healthcare 11, no. 11: 1617. https://doi.org/10.3390/healthcare11111617