Advancing Reproductive and Organ Health Management through cell-free DNA Testing and Machine Learning

Abstract

Science or medicine is a never-ending quest for cure and healing. The science of Reproductive Health issues and addressing them for help is an innate human vocation. The word "advancing" in the title infers that there has been some work done in Reproductive Health, which indeed is true. The paper outlines the expansion in scope of Reproductive Health issues from the erstwhile obstetrical complications of pregnancy associated with high morbidity and mortality in young women of reproductive age to the present concerns of infertility and prevention of sequelae of pregnancy complications related to high morbidity and mortality in mothers and neonates. Efforts have also been made in organically coordinating Reproductive Health with general health to optimally achieve and maintain overall health through all stages of life.
The tool is the innovative use of cell-free DNA testing solutions that quantifies maternal, fetal, and placental cfDNA to deliver compelling clinical insights into Reproductive and Organ Health Management. Computational insights developed by ML can augment the value of cfDNA testing solutions through advanced patient selection, data interpretation, and building predictive models for clinical outcomes based on cfDNA signatures and in conjunction with other omics. The end goals are to (1) Develop a simple, accurate, and cost-effective cfDNA based test to screen all women before conception, at the first trimester pregnancy scan and at the risk assessment visit in the postpartum period irrespective of obstetrical history. (2) Improve predictive accuracy of known pregnancy complications.