Alex Blank
53 lines
3.5 KiB
TeX
53 lines
3.5 KiB
TeX
%! Author = alex
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%! Date = 3/6/25
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\section{Introduction}\label{sec:introduction}
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A 2024 report by McKinsey and the World Economic Forum\cite{mckinsey_health_institute_closing_2024} highlights
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persistent disparities in women's healthcare and health-related research, particularly in reproductive health.
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The Institute for Health Metrics and Evaluation (IHME) has identified reproductive and gynecological health issues as
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the most significant factors affecting both life span and health span globally\cite{global_burden_of_disease_collaborative_network_global_2020}.
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Despite their widespread impact, many aspects of reproductive health remain under-researched.
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Improving our ability to understand the menstrual cycle could have significant implications for fertility tracking, contraception,
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and overall reproductive health.
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The average age of pregnant women in developed countries has been increasing over the past few decades.
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This combined with the overall chance of conception sharply decreasing with age, especially after 35,
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makes it more and more important to understand and predict ovulation accurately\cite{sauer_reproduction_2015}.
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Predicting the fertile days in a women's menstrual cycle is not only relevant for family planning but also for
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natural contraception and general health monitoring, as the corresponding hormone levels have a significant impact on
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the overall health and well-being of a woman.
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Ovulation is the process in which an egg is released from the ovarian follicle, making fertilization possible.
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This process is regulated by hormonal changes, including fluctuations in luteinizing hormone (LH) and
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follicle-stimulating hormone (FSH), and is accompanied by an increase in basal body temperature (BBT)\cite{holesh_physiology_2025}
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This process is complex and yet not fully understood.
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Factors such as stress, diet, and exercise can influence the menstrual cycle and make it hard to predict ovulation.
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Several physiological signs can be used to predict ovulation.
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The most accurate method is ultrasonography, which detects changes in follicle size and rupture.
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Other methods include detecting LH and FSH in urine, measuring BBT, and observing cervical mucus,
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each with its own advantages and limitations.
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These, as well as the interplay of those factors, will be discussed in more detail in Section~\ref{sec:background}.
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Many studies have used these physiological signs to predict ovulation and fertility
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\cite{noauthor_cervicovaginal_2005, sato_novel_2024, royston_identifying_1991, luo_detection_2020,
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alexander_fertilitatsmonitoring_2014, luz_improved_2024, yu_tracking_2022, pratikno_pdf_2024}.
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However, most of these methods rely on manual data collection, requiring either daily measurements or invasive procedures.
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This not only makes them impractical but also results in small sample sizes, limiting their generalizability.
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Generalization is crucial for developing a reliable ovulation predictor, given the high variability of the menstrual cycle
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\cite{munster_length_1992, bull_real-world_2019}.
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This is particularly important for applications where prediction accuracy
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is critical, such as natural contraception or high-cost procedures like in-vitro fertilization (IVF),
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where false predictions can have severe consequences.
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This work aims to develop an ovulation predictor that is both accurate and generalizable while maintaining interpretability,
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allowing for insights into key variables and patterns influencing the prediction.
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% explain focus: improve explainability |