Table 7.
ML applications in fetal malformations research.
| Reference | ML application | Input | ML technique | Main output |
|---|---|---|---|---|
| (111) | Complication prediction | Mobile collected data | DF | Ac = 87.5% |
| (112) | Complication prediction | Computed tomography images | LDA | Ac = 95.7%, Se = 92.7% and Sp = 98.9% for craniosynostosis |
| (113) | Complication prediction | Ultrasound images | SVM | AUC = 0.89, Ac = 88.63%, Se = 95%, Sp = 82% and +LR = 5.25 for craniosynostosis |
| (114) | Complication differentiation | Stereophotogrammetry images | PCA | Clear differentiation between craniosynostosis and control patients |
| (115) | Data acquisition | Ultrasound videos | RF | Estimation of heart position, orientation, viewing plane and cardiac phase |
| (116) | Data acquisition | Electrocardiography recordings | ICA and DT | Reconstruction of fetal electrocardiogram |
| (117) | Data acquisition | Electrocardiography recordings | SDAE | Reconstruction of fetal electrocardiogram |
| (118) | Data extraction | Ultrasound videos | SVM | Detection of fetal presentation and heartbeat |
| (119) | Data extraction | Cardiotocography recordings | EMD | Extraction of fetal heart rate |
| (120) | Data extraction | Electrocardiography recordings | CNN and LSTM | Extraction of fetal heart rate |
| (121) | Data extraction | Electrocardiography recordings | CNN and LSTM | Extraction of fetal heart rate |
| (122) | Data extraction | Doppler ultrasound recordings | EMD | Extraction of fetal heart rate |
| (123) | Complication prediction | Cardiotocography recordings | CNN | AUC = 97.82%, Ac = 98.34%, Se = 98.22%, Sp = 94.87% and QI = 96.53% for fetal acidemia caused by hypoxia |
| (124) | Decision making support | Cardiotocography recordings | Infant software | Identification of fetal status |
| (125) | Decision making support | Cardiotocography recordings | PeriCALM software | Identification of fetal status |
| (126) | Decision making support | Cardiotocography recordings and ultrasound measurements | Foetos software | Identification of fetal status |
| (127) | Complication prediction | Ultrasound measurements | NN | Ac = 95% for intrauterine growth restriction |
| (128) | Complication prediction | Cardiotocography recordings | SVM | Ac = 78,26%, Se = 0.78 and Sp = 0.79 for intrauterine growth restriction |
| (129) | Complication prediction | Ultrasound images | ANN | Ac = 91-94% for intrauterine growth restriction |
| (130) | Complication prediction | Echocardiography images | FINE software | Se = 98%, Sp = 93%, +LR = 14 and -LR: 0.02 for congenital heart disease |
| (131) | Complication prediction | Echocardiography images | CON | Ac = 99.0%, Se = 75%, Sp = 99.6%, PPV = 99% and NPV = 88.5% for congenital heart disease |
| (132) | Biomarker discovery | Transcriptomics data | PCA and K-means | miR-1647, miR-3064, mirR-3533, miR-6544, miR-6590, miR-6593, miR-6602, miR-6604, miR-6639, miR-6667, miR-6706, miR-6710, miR-1650, miR-1665, miR-6542, miR-6565, miR-6619 and miR-6706 as novel biomarkers for fetal alcohol spectrum disorder |
| (133) | Complication prediction | Clinical parameters | LR | AUC = 0.880, Se = 1.00, Sp = 0.49, PPV = 0.03 and NPV = 1.00 for macrosomia |
| (134) | Complication prediction | Electronic health records | LSTM | Ac = 93.3% for small, appropriate and large for gestational age |
| (135) | Drug teratogenicity prediction | Drug databases information | t-SNE and GB | AUC = 0.8 |
ML, machine learning; DF, decision forest; LDA, linear discriminant analysis; SVM, support vector machines; PCA, principal component analysis; RF, random forest; ICA, independent component analysis; DT, decision tree; SDAE, stacked denoising autoencoder; EMD, empirical mode decomposition; CNN, convolutional neural networks; LSTM, long short-term memory; NN, neural networks; ANN, artificial neural networks; CON, compound network; LR, logistic regression; t-SNE, t-distributed stochastic neighbor embedding; GB, gradient boosting; Ac, accuracy; Se, sensitivity; Sp, specificity, AUC, area under the receiver operating characteristic curve; +LR, positive likelihood ratio; QI, quality index; -LR, negative likelihood ratio; PPV, positive predictive value; NPV, negative predictive value.