The paper explores the evolving role of Artificial Intelligence (AI) in perinatal medicine and hu-man reproduction, highlighting its potential to transform clinical practices. AI technologies are being utilized to improve diagnostic accuracy, personalize treatment, and enhance patient care, particularly in areas like perinatal ultrasound, fetal heart rate monitoring, and fetal neurology. The Kurjak Antenatal Neurodevelopmental Test (KANET) exemplifies how AI can aid early detection of neurodevelopmental disorders. However, the integration of AI presents challenges such as data quality concerns, algorithmic bias, ethical concerns, and the need for robust regulatory frameworks. The authors argue that while AI offers significant opportunities, its implementation must be carefully managed to avoid over-reliance on technology and ensure equitable healthcare access. The paper concludes that the current state of AI in this field marks not an endpoint but a critical phase of growth and development, necessitating a balanced approach that combines innovation with ethical and practical considerations.

Congenital pulmonary airway malformations (CPAM) refer to an unusual lesion of the pulmonary airways which combines features of hamartoma malformation and dysplastic proliferation. CPAM includes cystic pulmonary airway malformations, bronchopulmonary sequestration, bronchogenic cysts, hybrid lesions and lobar/segmental emphysema causing respiratory distress in 20-40% of affected babies in the postnatal period. The remaining cases continue asymptomatic or develop symptoms later in life such as chest infections. Most CPAM can be detected on the 20-week antenatal ultrasound increasing the diagnostic yield if MRI is utilized. Children with symptoms early in life are managed with surgery. The management of asymptomatic CPAM is a source of controversy in the literature. CPAM is classified 0 to IV. Type 0 is very rare described as acinar aplasia or agenesis and incompatible with life. Type I the most common is primarily macrocystic with large single or multiple cysts several centimeters in size. Type II is microcystic and associated with other anomalies. Type III appears more solid or with very small cysts similar to immature lungs without bronchi. Type IV originates from the acinus and present with small cysts on the periphery of the lung lobes. Once a cystic lesion is detected in antenatal ultrasound, the location, volume, size, macrocystic or microcystic classification and blood supply should be evaluated. CPAM volume to head circumference ratio (CVR) greater than 1.6 results in fetal demise in about 80% of cases without fetal intervention. CVR < 1.6 will often not continue to grow past the 28th week of gestation. The reasons used to remove asymptomatic lesions in the first year of life include the rate of empyema, abscess, recurrent pneumonia, air leak, pneumothorax and malignancy. Almost 25% of asymptomatic children show histologic evidence of infection. CPAM have a long-term risk of malignancy. Multiple courses of antenatal betamethasone for high-risk fetal CPAM often results in favorable short-term outcomes without the need for open fetal resection.

Abstract Objectives Prenatal cardiology is a part of preventive cardiology based on fetal echocardiography and fetal interventional cardiology, which facilitates treatment of congenital heart defects (CHD) in pediatric patients and consequently in adults. Timely prenatal detection of CHD plays a pivotal role in facilitating the appropriate referral of pregnant women to facilities equipped to provide thorough perinatal care within the framework of a well-structured healthcare system. The aim of this paper is to highlight the role of left atrial strain (LAS) in prenatal evaluation of fetal heart and prediction of structural and functional disorders. Methods We conducted a comprehensive literature review searching PubMed for articles published from inception up until August 2023, including the search terms “left atrial strain”, “fetal echocardiography”, and “prenatal cardiology” combined through Boolean operators. In addition, references lists of identified articles were further reviewed for inclusion. Results Our review underscores the significance of LAS parameters in fetal echocardiography as a screening tool during specific gestational windows (starting from 11 to 14 weeks of gestation, followed by better visualization between 18 and 22 weeks of gestation). The left atrial strain technique and its parameters serve as valuable indicators, not only for identifying cardiac complications but also for predicting and guiding therapeutic interventions in cases of both cardiac and noncardiac pregnancy complications in fetuses. Evidence suggests establishment of second-trimester reference strain and strain rate values by speckle-tracking echocardiography in the healthy fetal cohort is essential for the evaluation of myocardial pathologies during pregnancy. Conclusions Finding of LAS of fetal heart is feasible and probably can have potential for clinical and prognostic implications.

BACKGROUND Following the latest trends in the development of artificial intelligence (AI), the possibility of processing an immense amount of data has created a breakthrough in the medical field. Practitioners can now utilize AI tools to advance diagnostic protocols and improve patient care. OBJECTIVE The aim of this article is to present the importance and modalities of AI in maternal-fetal medicine and obstetrics and its usefulness in daily clinical work and decision-making process. METHODS A comprehensive literature review was performed by searching PubMed for articles published from inception up until August 2023, including the search terms "artificial intelligence in obstetrics", "maternal-fetal medicine", and "machine learning" combined through Boolean operators. In addition, references lists of identified articles were further reviewed for inclusion. RESULTS According to recent research, AI has demonstrated remarkable potential in improving the accuracy and timeliness of diagnoses in maternal-fetal medicine and obstetrics, e.g., advancing perinatal ultrasound technique, monitoring fetal heart rate during labor, or predicting mode of delivery. The combination of AI and obstetric ultrasound can help optimize fetal ultrasound assessment by reducing examination time and improving diagnostic accuracy while reducing physician workload. CONCLUSION The integration of AI in maternal-fetal medicine and obstetrics has the potential to significantly improve patient outcomes, enhance healthcare efficiency, and individualized care plans. As technology evolves, AI algorithms are likely to become even more sophisticated. However, the successful implementation of AI in maternal-fetal medicine and obstetrics needs to address challenges related to interpretability and reliability.