In routine medical practice, fetal fibronectin (fFN) and phosphorylated insulin-like growth factor-binding protein-1 (phIGFBP-1) are used in the negative prediction of PTB [51,52]. Nevertheless, the positive predictive value of these methods is very low. Moreover, there are ongoing studies on several other biomarkers which might help predict PTB [39,53,54]. First of all, inflammatory markers are analyzed, such as the proinflammatory interleukins of cytokines found in the maternal serum [53] or in cervical secretions [55]. The study by Laudanski et al. showed that the levels of IGFBP-1, Eotaxin-1, BLC, BDNF, and MIP-1d measured in the serum might serve as predictive indicators for preterm labour. These biomarkers might distinguish between actual and false cases of PTB [53]. In another study, Laudanski et al. demonstrated a predictive value of MIP-3b/CCL19 serum levels [54]. The correlation of currently known biomarkers and CBR expression might help improve PTB prediction and, subsequently, neonatal outcomes. Using artificial intelligence methods, such as machine learning, might help refine the prognostic value of the existing clinical risk factors of PTB, especially in combination with biomarker analysis [56,57]. Moreover, Villar et al. proposed the phenotypic classification of PTB [58],where identifying and classifying patients according to their distinct phenotypes could improve the management of PTB.