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Predicting semen analysis parameters from testicular ultrasonography images using deep learning algorithms: an innovative approach to male infertility diagnosis

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Full Text / Tam Metin (3.159Mb)

Erişim

info:eu-repo/semantics/embargoedAccess

Tarih

2025

Yazar

Sağır, Lütfullah
Kaba, Esat
Hüner Yiğit, Merve
Taşçı, Filiz
Uzun, Hakkı

Üst veri

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Künye

Sagir, L., Kaba, E., Huner Yigit, M., Tasci, F., & Uzun, H. (2025). Predicting Semen Analysis Parameters from Testicular Ultrasonography Images Using Deep Learning Algorithms: An Innovative Approach to Male Infertility Diagnosis. Journal of Clinical Medicine, 14(2), 516. https://doi.org/10.3390/jcm14020516

Özet

Objectives: Semen analysis is universally regarded as the gold standard for diagnosing male infertility, while ultrasonography plays a vital role as a complementary diagnostic tool. This study aims to assess the effectiveness of artificial intelligence (AI)-driven deep learning algorithms in predicting semen analysis parameters based on testicular ultrasonography images. Materials and Methods: This study included male patients aged 18–54 who sought evaluation for infertility at the Urology Outpatient Clinic of our hospital between February 2022 and April 2023. All patients underwent comprehensive assessments, including blood hormone profiling, semen analysis, and scrotal ultrasonography, with each procedure being performed by the same operator. Longitudinal-axis images of both testes were obtained and subsequently segmented. Based on the semen analysis results, the patients were categorized into groups according to sperm concentration, progressive motility, and morphology. Following the initial classification, each semen parameter was further subdivided into “low” and “normal” categories. The testicular images from both the right and left sides of all patients were organized into corresponding folders based on their associated laboratory parameters. Three distinct datasets were created from the segmented images, which were then augmented. The datasets were randomly partitioned into an 80% training set and a 20% test set. Finally, the images were classified using the VGG-16 deep learning architecture. Results: The area under the curve (AUC) values for the classification of sperm concentration (oligospermia versus normal), progressive motility (asthenozoospermia versus normal), and morphology (teratozoospermia versus normal) were 0.76, 0.89, and 0.86, respectively. Conclusions: In our study, we successfully predicted semen analysis parameters using data derived from testicular ultrasonography images through deep learning algorithms, representing an innovative application of artificial intelligence. Given the limited published research in this area, our study makes a significant contribution to the field and provides a foundation for future validation studies.

Kaynak

Journal of Clinical Medicine

Cilt

14

Sayı

2

Bağlantı

https://doi.org/10.3390/jcm14020516
https://hdl.handle.net/11436/10021

Koleksiyonlar

  • Scopus İndeksli Yayınlar Koleksiyonu [5931]
  • TF, Cerrahi Tıp Bilimleri Bölümü Koleksiyonu [1216]
  • TF, Dahili Tıp Bilimleri Bölümü Koleksiyonu [1559]
  • TF, Temel Tıp Bilimleri Bölümü Koleksiyonu [691]



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