AI IN MATERNAL HEALTHCARE BY DR SHASHWAT JANI.pptx

AI IN MATERNAL HEALTHCARE BY DR SHASHWAT JANI

1. Role of A.I. in Maternal Healthcare Dr. Shashwat Jani M. S. ( Obs – Gyn ), F.I.A.O.G., F.I.C.O.G. Diploma in Advance Laparoscopy . Consultant Assistant Professor , Smt. N.H.L. Municipal Medical College. S.V.P. Hospital , Ahmedabad. Mobile : [phone]. E-mail : [email protected]

2. What is AI? Artificial intelligence (AI) is a type of digital computer system that parallels the way the human brain processes information. AI is organized in a similar way that neurons in the brain are arranged, with their multiple neural nodes, and so are referred to as neural networks. The rise of AI has led to the subsequent development of ARTIFICIAL NEURAL NETWORKS ( ANN), which consist of a dependable mathematical system that can interpret multifactorial data.

3. These neurons are connected via multiple synapses and send the data to each other back and forth , and by doing so , come up with the most probable answer. Making these multiple connections enables computers to mimic cognitive functions, such as the reasoning process, to identify the most probable answer to a problem. This complex algorithm AI software is now utilized in medicine to analyze large amounts of data, which can assist in disease prevention, diagnosing, and monitoring patients. Overall , AI can aid practitioners in decision-making and will help clinicians to make more self-assured decisions

4. The definition of artificial intelligence provided in the Merriam-Webster dictionary is . . . The capability of a machine to imitate intelligent human behavior…!!! 08-Sep-24 Dr Shashwat Jani 99099 44160 4

5. AI in Women’s Health Early Detection and Diagnosis Personalized Treatment Plans Enhanced Pregnancy Monitoring Improving Ultrasound Accuracy Predictive Analytics for Complications Tele health and Accessibility Prediction of Cancer

6. Application of AI in Obstetrics Fetal Heart Rate Monitoring and Pregnancy Surveillance Prediction of Gestational Diabetes Mellitus Prediction of Preterm Labour & Miscarriage Prediction of Preeclampsia Parturition Prediction Of PPH Prediction of Post partum depression

7. Focus Areas On Role Of AI In Complimenting Maternal Health. 08-Sep-24 Dr Shashwat Jani 99099 44160 7

8. Fetal Heart Monitoring And Pregnancy Surveillance Cardiotocography (CTG ) was an early development in the field of obstetrics. CTG is the most important device for evaluating fetal well-being through measurements of the fetal heart rate and uterine contractions. Despite its clinical importance, it is difficult to ensure objectivity between interpretations , and above all, regular observations are necessary in order to avoid a long gap between detection of suspicious patterns and intervention.

9. To overcome limitations in the interpretation of CTG by humans, AI using modern computer systems has been applied to CTG interpretation, and many experiments are underway. AI systems are not influenced by human limitations such as fatigue, distraction, bias, poor communication, cognitive overload, or fear of doing harm.

10. AI can give a qualitative and quantitative overview of . . . Baseline FHR Variability Acceleration Deceleration Uterine contraction intensity, and FHR pattern changes It helps to monitor the FHR rate during labor via analysing cardiotocographs and estimating possible outcomes.

11. This technology would help to . . . Decrease the discrepancies between different obstetricians interpreting intrapartum monitoring. Get a more reliable and replicable output for each analysis. Ultimately reduce the perinatal and maternal complications and morbidity. Perinatal asphyxia is a significant problem worldwide, and by creating an efficient way to monitor FHR, it would improve care and decrease poor outcomes.

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13. In the above study, AI system read the information at a similar level as the experts in the field and was also able to detect errors. This is a large trial currently evaluating the ability of AI interpretation of CTG during labor to assist practitioners in deciding the best management on an individual basis .

14. WHAT IS NEXT IN FETAL MONITORING ??? Technical development and research of in-home pregnancy surveillance continue with these priorities : Short term variability analysis using pattern recognition of the Doppler signal. AI interpretation of FHR variability and syndrome decoding. AI generation of warnings and alerts addressed to the patient and/or family. Messaging and real time communication to the attending physician and consultants . AI generation of alarms addressed to relatives and obstetric brigade or ambulance .

15. Gestational Diabetes Mellitus Polak and Mendyk created a study to evaluate the use of an AI calculator to screen for GDM that would be more cost-efficient and less inconvenient for the patient than current guidelines.

16. • It is an online calculator that a physician and patient can use for screening . • The calculator uses risk factors, such as High blood pressure, Hyperlipidemia, Smoking, Weight, Low-fat diet, and Ethnicity Despite the AI having lower-efficacy than the standard screening test at present, the current ANN model on the website will continue to progress and learn as it continues to be exposed to more cases, with the finality of eventually helping to lower health costs.

17. Preterm Labor Machine learning, particularly deep learning, achieved good to excellent prediction of perinatal outcome in asymptomatic pregnant women with short cervical length in the second trimester. Currently , the short cervical length is one of the strongest risk factor for prematurity.

18. Singh et al. studied the combination of AI and amniotic fluid (AF) proteomics and metabolomics , in conjunction or independently with imaging, demographic, and clinical factors, to predict perinatal outcomes in asymptomatic women with short cervix length.

19. A study done by Idowu et al. 2018 emphasized the importance of using AI technology to decrease expenses generated by inaccurate detection of preterm labor leading to unnecessary hospitalizations and procedures, and in the meantime, expedite treatment in those who are in true labor to prevent hazardous consequences for the baby and the mother. In this study, they used electro-hysterography (EHG) signals and used three distinct machine learning algorithms to classify these signals to help them identify true labor and accurately diagnose preterm labor. Accuracy of 97% in predicting preterm labor .

20. Under Research….!!! The day is not far away in the future, when an ultrasound probe placed on a mother's abdomen will not only measure basic parameters, but also provide related diagnoses and further treatment directions.

21. Prediction Of Preeclampsia A gradient-boosted tree model was performed with a positive predictive value of 88%, a negative predictive value of 89%, a sensitivity of 66%, a specificity of 97%, an overall accuracy of 89%.

22. Prediction Of PPH Dr Sarah McDonald wearing Oli , the AI-enabled sensor device named after her son. 08-Sep-24 Dr Shashwat Jani 99099 44160 22

23. Enhanced Pregnancy Monitoring 08-Sep-24 Dr Shashwat Jani 99099 44160 23

24. Ethical Consideration & Human Touch 08-Sep-24 Dr Shashwat Jani 99099 44160 24

25. The Future of Prenatal Care : Beyond the Nine Months While the current focus of PregnoTech and GeneMama is predominantly on the nine months of pregnancy, the potential of AI in maternal care stretches far beyond that. Imagine a world where AI continues to guide new mothers through the postnatal phase, offering insights on breastfeeding, baby sleep patterns, and even early childhood development. 08-Sep-24 Dr Shashwat Jani 99099 44160 25

26. With the surge of AI in healthcare, prenatal care is undergoing a paradigm shift. Instead of one-size-fits-all advice, AI algorithms sift through individual health data, genetic information, and even lifestyle habits to offer bespoke prenatal care recommendations. Think of it as having a personal pregnancy consultant, available 24/7 , who knows you inside out…!!! 08-Sep-24 Dr Shashwat Jani 99099 44160 26

27. Thank You AI …!!! 08-Sep-24 Dr Shashwat Jani 99099 44160 27

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