Artificial Intelligence-Based Approach for Automated Gonad Volume Quantification Using Magnetic Resonance Imaging in Healthy Adolescents Across Puberty.

Background/

Objectives

MRI is a non-invasive tool which can be used to assess baseline gonadal anatomy, including changes during puberty. Volumetric characterization offers valuable insights about the reproductive system and gonads, but annotation is cumbersome, and no AI tool is currently available. This study aimed to develop two open-source AI models to segment bilateral gonads at MRI scans in healthy subjects. Materials and

Methods

This study uses a longitudinal dataset consisting of 182 MRIs from 22 healthy girls (median age 13) and 266 MRIs from 44 healthy boys (median age 13) from a single institute. MRI acquisition included T2-weighted (T2W) sequence, along with fat-saturated (FS) T2W when indicated. An expert radiologist segmented gonadal anatomy, including ovarian cysts (>3 cm). Three-dimensional nnUnet models were trained for ovary, cyst, and testicle segmentation, respectively. The ovary-cyst segmentation model was applied to an external dataset with 30 adult subjects. Model performance was evaluated on the test set using the Dice similarity coefficient for ovary (DSCOV), cyst (DSCCY), and testicles (DSCTS). Subject-level total volumes for ovaries (TOV), cysts (TCV), and testicles (TTV) were computed.

Results

Ovary, cyst, and testicle segmentation models achieved DSCOV of 0.86, DSCCY of 0.69, and DSCTS of 0.90 in the in-house test set, respectively. Average mean difference with 95% confidence intervals for TOV, TCV, and TTV were 0.87 (-5.78, 7.5), -0.41 (-3.3, 2.5), and 0.19 (-1.5, 1.9) cm3, respectively.

Conclusions

The developed models show promising and reliable performance in volumetric and morphologic evaluation of gonads during puberty.