ArticleReproductive medicine and biology
A new deep-learning model using YOLOv3 to support sperm selection during intracytoplasmic sperm injection procedure.
Article in Reproductive medicine and biology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Implementation readiness of artificial intelligence inFrontiers in endocrinology · 2026Review
- An artificial intelligence real-time rare sperm detection system for intraoperative microsurgical testicular sperm extraction.Human reproduction open · 2026Article
- An efficient advanced YOLOv8 framework for sperm motility detection.Journal of assisted reproduction and genetics · 2025Article
- [Advances in the Application of Artificial Intelligence in the Field of Male Infertility].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2025Review
- Artificial Intelligence in Andrology: A New Frontier in Male Infertility Diagnosis and Treatment.Current urology reports · 2025Review
- Review
- Multidimensional morphological analysis of live sperm based on multiple-target tracking.Computational and structural biotechnology journal · 2024Article
- A narrative review of sperm selection technology for assisted reproduction techniques.Translational andrology and urology · 2024Review
- [Application of Artificial Intelligence in Sperm Quality Analysis and Sperm Screening].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2024Review
- Sperm YOLOv8E-TrackEVD: A Novel Approach for Sperm Detection and Tracking.Sensors (Basel, Switzerland) · 2024Article
- Testing the generalizability and effectiveness of deep learning models among clinics: sperm detection as a pilot study.Reproductive biology and endocrinology : RB&E · 2024Article
- Current Updates on Involvement of Artificial Intelligence and Machine Learning in Semen Analysis.Medicina (Kaunas, Lithuania) · 2024Review
- A new deep-learning model using YOLOv3 to support sperm selection during intracytoplasmic sperm injection procedure.Reproductive medicine and biologyArticle
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Authors and funding
13 authors.
Funding
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Abstract
Purpose: To create and evaluate a machine-learning model for YOLOv3 that can simultaneously perform morphological evaluation and tracking in a short time, which can be adapted to video data under an inverted microscope. Methods: Japanese patients who underwent intracytoplasmic sperm injection at the Jikei University School of Medicine and Keiai Reproductive and Endosurgical Clinic from January 2019 to March 2020 were included. An AI model that simultaneously performs morphological assessment and tracking was created and its performance was evaluated. Results: For morphological assessment, the sensitivity and positive predictive value (PPV) of this model for abnormal sperm were 0.881 and 0.853, respectively. The sensitivity and PPV for normal sperm were 0.794 and 0.689, respectively. For tracking performance, among the 51 objects, 40 (78.4%) were mostly tracked, 11 (21.6%) were partially tracked, and 0 (0%) were mostly lost. Conclusions: This study showed that evaluating sperm morphology while tracking in a single model is possible by training YOLO v3. This model could acquire time-series data of one sperm, which will assist in acquiring and annotating sperm image data.
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