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. 2024 Jul 31;14(1):17079.
doi: 10.1038/s41598-024-67910-0.

A new model for determining risk of male infertility from serum hormone levels, without semen analysis

Hideyuki Kobayashi  1 , Masato Uetani  2 , Fumito Yamabe  2 , Yozo Mitsui  2 , Koichi Nakajima  2 , Koichi Nagao  2
Affiliations

A new model for determining risk of male infertility from serum hormone levels, without semen analysis

Hideyuki Kobayashi et al. Sci Rep. .

Abstract

We investigated a screening method using only serum hormone levels and AI (artificial intelligence) predictive analysis. Among 3662 patients, numbers for NOA (non-obstructive azoospermia), OA (obstructive azoospermia), cryptozoospermia, oligozoospermia and/or asthenozoospermia, normal, and ejaculation disorder were 448, 210, 46, 1619, 1333, and 6, respectively. "Normal" was defined as semen findings normal according to the WHO (World Health Organization) Manual for Human Semen Testing of 2021. We extracted age, LH (luteinizing hormone), FSH (follicle stimulating hormone), PRL (prolactin), testosterone, E2 (estradiol), and T (testosterone)/E2 from medical records. A total motility sperm count of 9.408 × 106 (1.4 ml × 16 × 106/ml × 42%) was defined as the lower limit of normal. The Prediction One-based AI model had an AUC (area under the curve) of 74.42%. For the AutoML Tables-based model, AUC ROC (receiver operating characteristic) was 74.2% and AUC PR (precision-recall) 77.2%. In a ranking of feature importance from 1st to 3rd, FSH came a clear 1st. T/E2 and LH ranked 2nd and 3rd for both Prediction One and AutoML Tables. Using data from 2021 and 2022 to verify the Prediction One-based AI model, the predicted and actual results for NOA were 100% matched in both years.

Keywords: Artificial intelligence; Hormonal evaluation; Machine learning; Male infertility; Semen analysis.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
AI predictive analysis model using Prediction One.
Figure 2
Figure 2
AI predictive analysis model using AutoML Tables.
Figure 3
Figure 3
Validation of AI prediction model generated by Prediction One using data from 2021 and 2022.
See this image and copyright information in PMC

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