@article {Uthoff1469, author = {Johanna Uthoff and Samer Alabed and Pankaj Garg and Allan Lawrie and Jim M Wild and David G Kiely and Haiping Lu and Andrew J Swift}, title = {Sex bias exists in diagnosing pulmonary arterial hypertension via machine learning}, volume = {56}, number = {suppl 64}, elocation-id = {1469}, year = {2020}, doi = {10.1183/13993003.congress-2020.1469}, publisher = {European Respiratory Society}, abstract = {Objective: Pulmonary arterial hypertension (PAH) is more common in females, yet males tend to have a worse prognosis, indicating potential sex-based differences. Will such differences affect the PAH diagnosis accuracy by machine learning (ML) on cardiac magnetic resonance imaging (CMRI)?Methods: A retrospective cohort of 220 consecutive subjects with PAH (105 F; 45 M) or with no pulmonary hypertension (51 F; 19 M) were included in the study. An ML pipeline [1] was implemented using the baseline CMRI Short Axis and 4 Chamber scans for PAH diagnosis prediction utilizing (a) the full cohort and (b) only female subjects. Fisher{\textquoteright}s exact test with Bonferroni correction was used to compare the proportionate accuracy between the sexes on 150 PAH subjects.Results: No significant difference was found between the sexes using right heart catheterisation PAH diagnosis (p=0.75). In the full cohort (a), ML-predicted diagnosis demonstrated a bias towards correctly predicting PAH in males on both the Short Axis (p=0.01) and 4 Chamber (p\<0.01). The Short Axis ML-predicted diagnosis correctly classified 132 PAH subjects (88/105 F; 44/45 M). The 4 Chamber ML-predicted diagnosis accurately classified 128 PAH subjects (83/105 F; 45/45 M). In the sex-stratification ML pipeline (b), accuracy was improved on both the Short Axis (89/105, i.e. +1) and 4 Chamber (93/105, i.e. +10).Conclusions: This preliminary study found significant disparity in PAH prediction accuracy by ML between the sexes. This suggests that sex bias exists in CMRI-based PAH diagnosis via ML and sex-stratification could be benefitial in ML-based diagnosis.[1] Swift, A, et al. Eur. Heart J. Cardiovasc. Imaging 2020; jeaa001FootnotesCite this article as: European Respiratory Journal 2020; 56: Suppl. 64, 1469.This abstract was presented at the 2020 ERS International Congress, in session {\textquotedblleft}Respiratory viruses in the "pre COVID-19" era{\textquotedblright}.This is an ERS International Congress abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only).}, issn = {0903-1936}, URL = {https://erj.ersjournals.com/content/56/suppl_64/1469}, eprint = {https://erj.ersjournals.com/content}, journal = {European Respiratory Journal} }