Our Work on SexEst Cited in Recent Research

I recently came across a new paper in the Journal of Forensic Sciences that gave me a bit of a surprise—in a good way.

Ferrell, M. J., Schultz, J. J., & Adams, D. M. (2025). The application of decision trees for estimating osteological sex from common measurements of the skull. Journal of Forensic Sciences, 00, 1–14. https://doi.org/10.1111/1556-4029.70031
View full text (Wiley)

The study explores the use of decision tree models for sex estimation from cranial metrics, reporting accuracies as high as 95%. The authors mention our open-source web application, SexEst, as part of a broader context of machine learning tools in the field.

You can find our work here:

Constantinou, C., & Nikita, E. (2022). SexEst: an open access web application for metric skeletal sex estimation. International Journal of Osteoarchaeology, 32(4), 832–844. https://doi.org/10.1002/oa.3109

Back in 2021–2022, when this project began, I never expected to be able to build machine learning models or a web application—but I just grinded through, especially on the web app part. That was a real struggle for me, using Streamlit and a Docker container, after first being introduced to it via another project.

So I’m totally humbled to see other researchers citing that early work. I had only one formal ML class, one formal HTML class, and I took Andrew Ng’s course online. But most importantly, I had expert guidance from Efthymia Nikita. Without her, I wouldn’t have made it through this project. So the fact that this work “travelled” and continues to show up in new research really feels great.

If you’re curious about SexEst, you can still explore it here or check out the original paper.