ISSN 1662-4009 (online)

ey0019.6 | Tülay Güran, Gary Butler | ESPEYB19

6. DSD and Gender Incongruence

Guran Tulay , Butler Gary

Preface: In the past 12 months, the search for “Differences of Sexual Development” or “disorders of sex development“ or “ambiguous genitalia” or “gonadal development” or “DSD” in PubMed yielded 625 publications. A similar search for gender incongruence revealed > 325 papers on a search between “transgender and hormones”. Among those, 16 are summarized in this chapter. The selection process has been very challengin...

ey0020.4 | Tulay Guran, Gary Butler | ESPEYB20

4. Differences of Sexual Development (DSD) and Gender Incongruence (GI)

Guran Tulay , Butler Gary

Preface: In the past 12 months, between June 1, 2022 and May 31, 2023, the search for ‘Differences of Sexual Development’ or ‘disorders of sex development’ or ‘ambiguous genitalia’ or ‘gonadal development’ or ‘DSD’ in PubMed yielded 680 publications published in English. A similar search for gender incongruence revealed more than 600 papers. Among those, 15 are summarized in this chapter. The selection process has been very cha...

ey0018.15-11 | (1) | ESPEYB18

15.11. Prediction of adult height by machine learning technique

Shmoish Michael , German Alina , Devir Nurit , Hecht Anna , Butler Gary , Niklasson Aimon , Albertsson-Wikland Kerstin , Hochberg Ze'ev

J Clin Endocrinol Metab. 2021; 16;106(7):e2700–e2710.PMID: 33606028 doi: 10.1210/clinem/dgab093This paper illustrates the power of machine learning to successfully predict adult height using growth measurements before age 6 years, without the need for bone age.Computers beat us in games of predictions, such as chess. They beat us also in the exercise of predictin...

ey0020.14-2 | Section | ESPEYB20

14.2. Prediction of adult height by machine learning technique

Shmoish Michael , German Alina , Devir Nurit , Hecht Anna , Butler Gary , Niklasson Aimon , Albertsson-Wikland Kerstin , Hochberg Ze'ev

Brief summary: Growth data from three independent longitudinal cohort studies (Gothenburg GrowUp 1974 (n 1596); Gothenburg GrowUp 1990 (n 1890); Edinburgh Growth Study (n 145)) were used to train machine learning (ML) to predict adult height (AH) based on growth measurements until the age of 6 years. Five ML algorithms were tested. A random forest model predicted best, with sex and height at age 3.4–6.0 years being the most influencing factors. The model was cross-validat...