A digital health approach for identifying polyendocrine metabolic ovarian syndrome using machine learning and body temperature

Background Polyendocrine Metabolic Ovarian Syndrome (PMOS), formerly known as Polycystic Ovary Syndrome (PCOS), is a prevalent endocrine disorder with high rates of undiagnosed cases globally. Accessible screening tools are needed to facilitate appropriate management and earlier intervention. As PMOS is frequently characterised by oligo-anovulation, the absence of the characteristic rise in basal body temperature typically seen in ovulatory cycles may serve as a physiological marker for the condition. Objective This study aimed to assess the feasibility of using machine learning to identify individuals with PMOS from temperature data collected by a body-worn device. Methods We used data from 387 users of a vaginal temperature monitor (OvuSenseTM) who responded to a questionnaire. The sample was restricted to individuals with at least three cycles with sufficient temperature data and whose PMOS case/control status could be determined from questions about prior clinical consultation for infertility and conditions for which they take medications. We randomly sampled three menstrual cycles for each participant and derived a set of cycle-level and user-level temperature features. Cycle-level features included cycle length and measures describing the temperature rise indicative of ovulation (e.g. temperature rise, cycle day of temperature rise start). We also constructed a reference cycle representing the typical bi-phasic cycle pattern (created using cycles from those without known fertility conditions) and used this to derive features describing how much a participant's cycles differed from this reference. The cycle-level features were aggregated into user-level features by taking the minimum, maximum, median, and range of the cycle-level features across the three selected cycles for each participant. We used 5-fold nested cross-validation to evaluate the extent that PMOS could be predicted, at the cycle and user levels, using Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF). Results The average age of participants was 31.97 years (SD=4.58), with 49.6% having a self-reported PMOS diagnosis. The models demonstrated moderate discrimination, with cycle-level AUC-ROC scores ranging from 0.64 (SD=0.02) (LR) to 0.68 (SD=0.04) (RF), and user-level scores ranging from 0.65 (SD=0.07) (LR) to 0.70 (SD=0.04) (RF). All models were reasonably calibrated, though confidence intervals were wide (e.g. RF cycle-level: calibration slope = 0.83 (95% confidence interval [CI]: 0.68, 1.00), calibration intercept = 0.02 (95% CI: -0.11, 0.14); user-level: slope = 0.88 (95% CI: 0.69, 1.15), intercept = -0.01 (95% CI: -0.22, 0.16)). Conclusions This study demonstrates the potential of using body temperature from digital health devices to identify those with PMOS. Such a passive approach to identifying PMOS could help to identify undiagnosed PMOS in those who have not actively sought a diagnosis. Further research is needed to assess its predictive performance and acceptability in a general population using more widely used digital devices.