ISEP - DM – Engenharia de Inteligência Artificial
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- An explainable and privacy-preserving machine learning pipeline for early detection of endometriosis leveraging liquid biopsy and minimally-invasive CPublication . MANESSE, CIRO MIGUEL POÇAS FERREIRA; Martinho, Diogo Emanuel Pereira; Conceição, Luís Manuel SilvaEndometriosis a!ects approximately one in ten women of reproductive age, yet it is diagnosed, on average, seven to eight years after the onset of symptoms, and in some studies up to twelve. This diagnostic delay is associated with prolonged symptoms, uncertainty, repeated healthcare contacts and delayed therapeutic intervention, largely because a definitive diagnosis still depends on an invasive surgical procedure, namely laparoscopy. Recent progress in Machine Learning, together with the growing availability of clinical and molecular data, o!ers a realistic opportunity to shorten this interval. This dissertation investigates whether a non-invasive, explainable and privacy-preserving Machine Learning pipeline can support an earlier clinical suspicion of Endometriosis from micro-RNA (miRNA) measured in a blood sample, that is, a liquid biopsy. Three requirements are addressed: the test must be non-invasive; its decisions must be explainable, so that a clinician can examine and verify them; and the training procedure must protect sensitive patient data. To the best of the author’s knowledge, no previous work brings these three properties together for Endometriosis detection. Working exclusively with real, public data, a leakage-safe pipeline was built for the serummiRNA cohort GSE279435 (127 samples; 67 Endometriosis, 60 benign controls). A central observation in understanding the data is that the measurements are left-censored at the assay’s limit of detection, so that a missing value is itself informative. Additionally, every preprocessing step was fitted strictly inside each cross-validation fold to avoid optimistic bias. Five classifiers were compared under nested, repeated, stratified cross-validation. The two strongest (Random Forest and LightGBM) reached an area under the ROC curve of 0.77– 0.78 with balanced sensitivity and specificity, a performance comparable to that of the previously published eleven-miRNA model evaluated on the same cohort. Explainability was provided with SHAP, which makes each prediction inspectable both globally and at the level of an individual patient, allowing the model to be examined rather than trusted blindly. Five of the ten most influential miRNAs coincide with the published diagnostic panel, which grounds the model’s reasoning in established biology; the remaining five are plausible additional candidates, consistent with the di!erential-expression analysis, that would warrant follow-up in larger studies. On privacy, this is, to the best of the author’s knowledge, the first work to apply both Di!erential Privacy and Federated Learning to non-invasive miRNA-based Endometriosis detection. In a simulated federated setting, created by partitioning the public cohort into four virtual sites, a Federated Learning implementation built with NVIDIA FLARE trained a shared model without moving any raw data and recovered approximately 95% of the accuracy of a model trained on the pooled data, well above what any single site achieved in isolation. Di!erential Privacy, in contrast, reduced accuracy to little above chance at the privacy levels that would meaningfully protect patients, illustrating how costly strong, sample-level privacy guarantees become when the cohort is this small (n = 127). A cross-platform exploratory test on an independent plasma cohort indicated that the general signal — that circulating miRNA carries an Endometriosis-related signal — holds across biofluids, even though the specific serum signature does not transfer directly to plasma. The contribution of this work is therefore not a higher headline accuracy but a pipeline that, using real public data, reaches the performance of the previously published model while adding two properties that model did not have: explanations a clinician can recognise, and training that never centralises patient data. The proposed pipeline is presented not as a medical device, but as a reproducible and ethically framed foundation for the larger, multiinstitutional validation studies needed to support earlier clinical suspicion of Endometriosis.