What is this collection about?

Technology for women’s health (colloquially known as “Femtech”) has come to the forefront, with a large focus on reproductive health, sex differences, and women-centric solutions. Developments in diagnostic tools are occurring in traditional clinic-based instruments, such as imaging, and point-of-care home-diagnostic tools that allow users to monitor conditions themselves. New approach methodologies for research tools include engineering systems such as microphysiological systems, organoids, and computational models, that can complement and reduce animal testing. These methodologies can lead to early detection, personalised care plans, and innovations for diagnostics and treatments for women’s health applications.

In this collection, we aim to showcase cutting-edge engineering research covering conditions and situations, including (but not limited to) endometriosis, menopause and hormonal health monitoring, fertility and pregnancy monitoring, cycle tracking, ovulation, pregnancy risk, PCOS, cancers (cervical, ovarian, uterine), and conditions affecting women specifically or differently.

Why is this collection important?   

This Collection supports and amplifies research directly related to the United Nation's Sustainable Development Goal 3 and 5 (SDG 3 and SDG 5) – Good Health & Well being and Gender Equility.

Why submit to a collection?

Collections like this one help promote high-quality science. They are led by Guest Editors and In-House Editors who are experts in their fields and supported by a dedicated team of Commissioning Editors and Managing Editors at Springer Nature. Collection manuscripts typically see higher citations, downloads, and Altmetric scores, and provide a one-stop-shop on a cutting-edge topic of interest.

Who is involved?

Annalisa Occhipinti, PhD, Teesside University, United Kingdom

Annalisa Occhipinti is a Professor at Teesside University, where she works at the intersection between AI and Biology. Annalisa received her PhD in Computer Science from the University of Cambridge in 2016, and her current research topics include the integration of metabolic modelling and multi-modal deep learning techniques for biomedical and bioprocessing applications.

Samantha Zambuto, PhD, MPHS, University of Kentucky, United States

Samantha Zambuto is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University of Kentucky. Her research involves creating tissue engineered models of the uterus, vagina, placenta, and female lower urinary tract to study the menstrual cycle, pregnancy, space biology, urinary incontinence, and aging.

Communications Engineering is edited by both in-house professional editors and Editorial Board Members.

How can I submit my paper?

Visit the Collection page to find out more about this collection and submit your article.