FEMaLe: The use of machine learning for early diagnosis of endometriosis based on patient self-reported data—Study protocol of a multicenter trial

Balogh, Dora B. [Balogh, Dóra Bianka (Molekuláris biológia), author] Department of Obsterics and Gynecology (SU / FM / C); Hudelist, Gernot; Bļizņuks, Dmitrijs; Raghothama, Jayanth; Becker, Christian M.; Horace, Roman; Krentel, Harald; Horne, Andrew W.; Bourdel, Nicolas; Marki, Gabriella; Tomassetti, Carla; Kirk, Ulrik Bak; Acs, Nandor [Ács, Nándor (Szülészet-nőgyógy...), author] Department of Obsterics and Gynecology (SU / FM / C); Bokor, Attila ✉ [Bokor, Attila (szülészet, nőgyóg...), author] Department of Obsterics and Gynecology (SU / FM / C)

Fundings:

• Finding Endometriosis using Machine Learning (FEMaLe) - European Union’s Horizon 2020(101017562) Funder: EU