• Author
    Jose Segura-Bermudez
  • Discovery PI

    Dr. Andrew L. Da Lio

  • Project Co-Author

    Dr. George H. Rudkin

  • Abstract Title

    The Role of Artificial Intelligence in Ideal Nipple-Areolar Complex Position: Validating the Literature and Exploring Body Habitus Differences

  • Discovery AOC Petal or Dual Degree Program

    Basic, Clinical, & Translational Research

  • Abstract

    Background: Nipple-areolar complex (NAC) position is essential in male chest aesthetics and surgical planning. Anthropometric studies have attempted to define ideal NAC positioning but are limited by population size and BMI. Artificial intelligence (AI) offers a novel perspective, particularly across body types. We aimed to evaluate NAC positioning and chest proportions of AI-generated male chests across BMI categories and compare them with published standards.

    Methods: An openly accessible AI image generator created 60 frontal-view "aesthetically ideal" male chests stratified into underweight, normal, overweight, and obese BMI groups. Measurements of sternum to nipple horizontal distance (SN), internipple distance (IND), sternal notch to nipple distance (NN), and sternal notch to xiphoid distance (NX) were analyzed using ImageJ, Excel, and R software.

    Results: Significant differences in measurement ratios emerged across BMI groups (p < 0.001). For IND: NN, the obese cohort had the highest value (1.52 ± 0.17) compared to overweight (1.34 ± 0.12), normal (1.28 ± 0.08), and underweight (1.27 ± 0.08) groups, with a positive trend following body habitus (R² = 0.97). The obese cohort also had the highest NN: NX (1.51 ± 0.15) and IND: SN (2.02 ± 0.04) ratios. Compared to previously published ideals, all groups significantly deviated (p < 0.001) positively from Beer’s (1.12), Beckenstein’s (1.06), and Shulman’s (1.12) ratios. Deviation from Yue’s (1.23) and Atiyeh's (1.39) ratios differed in the obese (p < 0.001, p < 0.01), overweight (p < 0.05, p = 0.14), normal (p < 0.05, p < 0.001), and underweight (p = 0.13, p < 0.001) cohorts.

    Conclusion: AI-generated "ideal" male chests challenge established NAC positioning, particularly at higher BMIs. AI placed the NAC significantly more lateral than previously described. Validation against diverse population data is needed to distinguish between potential AI bias. Overall, AI should be utilized as a supplementary resource, offering new perspectives in achieving optimal individualized male chest aesthetic outcomes.

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