Machine learning based prediction models in male reproductive health: Development of a proof‐of‐concept model for Klinefelter Syndrome in azoospermic patients. (10th January 2022)

• Record Type: Journal Article
• Authors: Krenz, Henrike; Sansone, Andrea; Fujarski, Michael; Krallmann, Claudia; Zitzmann, Michael; Dugas, Martin; Kliesch, Sabine; Varghese, Julian; Tüttelmann, Frank; Gromoll, Jörg
• Is Part Of: Andrology. Volume 10:Number 3(2022)
• Page Start: 534
• Page End: 544
• Publication Date: 2022-01-10
• Subjects: azoospermia -- Klinefelter Syndrome -- machine learning -- prediction models -- reproductive genetics -- reproductive health; Andrology -- Periodicals; 616.65
• DOI: 10.1111/andr.13141
• Languages: English
• ISSNs: 2047-2919

Abstract

Abstract: Background: Due to the highly variable clinical phenotype, Klinefelter Syndrome is underdiagnosed. Objective: Assessment of supervised machine learning based prediction models for identification of Klinefelter Syndrome among azoospermic patients, and comparison to expert clinical evaluation. Materials and methods: Retrospective patient data (karyotype, age, height, weight, testis volume, follicle‐stimulating hormone, luteinizing hormone, testosterone, estradiol, prolactin, semen pH and semen volume) collected between January 2005 and June 2019 were retrieved from a patient data bank of a University Centre. Models were trained, validated and benchmarked based on different supervised machine learning algorithms. Models were then tested on an independent, prospectively acquired set of patient data (between July 2019 and July 2020). Benchmarking against physicians was performed in addition. Results: Based on average performance, support vector machines and CatBoost were particularly well‐suited models, with 100% sensitivity and >93% specificity on the test dataset. Compared to a group of 18 expert clinicians, the machine learning models had significantly better median sensitivity (100% vs. 87.5%, p = 0.0455) and fared comparably with regards to specificity (90% vs. 89.9%, p = 0.4795), thereby possibly improving diagnosis rate. A Klinefelter Syndrome Score Calculator based on the prediction models is available on http://klinefelter‐score‐calculator.uni‐muenster.de . Discussion: Differentiating Klinefelter Syndrome patients from azoospermic patients with normal karyotype (46, XY) is a problem that can be solved with supervised machine learning techniques, improving patient care. Conclusions: Machine learning could improve the diagnostic rate of Klinefelter Syndrome among azoospermic patients, even more for less‐experienced physicians.