hopefully final commit

thesis/sections/related_work.tex

@@ -17,8 +17,8 @@ In~\citeyear{wallach_prediction_1980}, \citeauthor{wallach_prediction_1980} iden
 including salivary ferning and viscosity, serum levels of progesterone and estrogen,
 and urinary luteinizing hormone (LH) concentrations~\cite{wallach_prediction_1980}.
 These indicators showed strong correlation with ovulation timing as measured via transvaginal ultrasound.
-\citeauthor{noauthor_monitoring_1987} later expanded on this work by focusing specifically on LH and estradiol,
-confirming that LH surges reliably indicate an imminent ovulation event.
+\citeauthor{vermesh_monitoring_1987} later expanded on this work by focusing specifically on LH and estradiol,
+confirming that LH surges reliably indicate an imminent ovulation event~\cite{vermesh_monitoring_1987}.
 
 Despite their diagnostic value, many of these biomarkers are difficult to measure continuously and reliably in everyday settings,
 limiting their practicality for real-time or large-scale applications.
@@ -36,7 +36,7 @@ However, several studies have raised concerns about its reliability as a predict
 high susceptibility to external confounders, recommending caution in its clinical or research use~\cite{bauman_basal_1981}.
 Similarly, \citeauthor{moghissi_accuracy_1976} emphasized its limited accuracy, particularly in cycles with irregularities~\cite{moghissi_accuracy_1976}.
 
-However, such conclusions were largely based on the standard BBT method, which relies on a single-point measurement taken
+Such conclusions were largely based on the standard BBT method, which relies on a single-point measurement taken
 immediately upon waking—typically reflecting the body's lowest resting temperature.
 In contrast, this study, along with several recent works, leverages continuous or high-resolution temperature data collected during sleep or throughout the day.
 This richer signal provides a more robust foundation for detecting ovulatory patterns and addresses many of the limitations historically associated with BBT-based methods.
@@ -78,10 +78,27 @@ For menstruation prediction, the model detected 70.70\% of menstruation days in
 
 These results indicate that the model performed well in regular cycles but struggled with irregularity, particularly in detecting the fertile window.
 
-Complementing academic efforts, several commercial products have adopted temperature-based tracking,
-such as \textit{Ava}~\cite{sl_ava_nodate}, \textit{Daysy}~\cite{electronics_zykluscomputer_nodate}, \textit{Trackle}~\cite{noauthor_trackle_nodate}
-or \textit{Natural Cycles}~\cite{noauthor_natural_nodate}.
-However, these products typically rely on proprietary algorithms, and no peer-reviewed publications are available detailing their methodology or performance.
+In~\citeyear{kilungeja_machine_2025},~\citeauthor{kilungeja_machine_2025} trained a set of classification models
+to predict the cycle phase a day based on skin temperature, electrodermal activity, interbeat interval and heart rate.
+The measurements were automatic and did not require manual participant input~\cite{kilungeja_machine_2025}.
+The classification was either into three or four targets: period, ovulation, luteal phase, and follicular phase for the four-class models.
+Their dataset included 65 cycles across 18 subjects.
+They trained a decision tree, random forest ensemble, logistic regression and support vector machine to enable
+an architectural comparison.
+Results show an edge for the random forest model with a reported 87\% accuracy and AUC-ROC (area under the receiver operating characteristic curve)
+of 0.96 for the three class approach.
+The four class approach significantly reduced accuracy to 68\% and AUC-ROC of 0.77.
+There was no separation into cycle groups and all cycles were in a regular group, with a mean lengths of 28 days (SD: 1.65).
+
+There are some additional studies based on commercial products, such as \emph{Oura Ring}\cite{thigpen_oura_2025}
+or \emph{Natural Cycles}\cite{bull_real-world_2019} that work with temperature data as a base.
+However, they all focus on retrospective ovulation-detection and often complement this with advice to remain
+abstinent during the first cycle phase until the ovulation was reliably detected.
+This way they can offer a contraceptive product option, without needing to create a predictive model or algorithm.
+
+Complementing academic efforts, several other commercial products have also adopted temperature-based tracking,
+such as \textit{Ava}~\cite{sl_ava_nodate}, \textit{Daysy}~\cite{electronics_zykluscomputer_nodate} or \textit{Trackle}~\cite{noauthor_trackle_nodate}.
+However, these products rely on proprietary algorithms, and no peer-reviewed publications are available detailing their methodology or performance.
 This lack of transparency limits their scientific evaluation and comparability.
 In contrast, the present study provides an open and data-driven approach to ovulation prediction based on continuous temperature data, aiming to contribute reproducible evidence to the field.