부산대학교 도서관

Introduction: An artificial intelligence (AI) electrocardiogram (ECG) algorithm has been demonstrated to identify a patient’s birth designated sex with 90.4% accuracy with an AUC = 0.97. However, the algorithm has not been evaluated in transgender and gender diverse individuals. We aimed to determine how the algorithm output varies among transgender individuals seen at the Mayo Clinic Transgender and Intersex Specialty Care Clinic (TISCC) who are undergoing gender-affirming hormonal treatment (GAHT).Methods: We applied the algorithm (which assessed the probability of “male” between 0 and 1, with < 0.5 considered as “female” and > 0.5 as “male”) to ECGs obtained from patients in the TISCC before and after the initiation of GAHT. GAHT frequently involves the use of testosterone in transgender men, and estrogen and anti-androgen agents in transgender women. Patient characteristics, including the date of the initiation of GAHT, were collected through chart review.Results: Among transgender women, the AI model probability of “male” decreased from 0.84± 0.25 (12/86 classified as males) to 0.59±0.36 (68/173 classified as male) with GAHT (p < 7.8e-10). Among transgender men, the AI model probability of “male” increased from 0.16 ± 0.28 (7/47 classified as male) to 0.41 ±0.38 (22/53 classified as male) with GAHT (p < 2.4e-4).Conclusion: After GAHT initiation, the Mayo Clinic AI ECG algorithm assigns a higher probability of being male among transgender men and a lower probability of being male among transgender women. These findings warrant further investigation into the electrocardiographic differences across genders and the effects of GAHT on the human heart.