ReviewSichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition2025
[Advances in the Application of Artificial Intelligence in the Field of Male Infertility].
Review in Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Progress in the research of artificial intelligence in andrology: a narrative review.Translational andrology and urology · 2026Review
- The Evolving Role of Artificial Intelligence in Andrological Surgery: Current Landscape and Future Direction.Journal of clinical medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, with the constant rise in the number of infertility patients, the rapid and accurate assessment of sperm quality has become a key challenge in reproductive medicine. Currently, traditional methods for sperm classification suffer from limitations in efficiency, subjectivity, and cost-effectiveness, and hence fail to meet clinical needs. With the introduction of artificial intelligence (AI), innovative approaches have been offered to address these issues. Herein, we systematically reviewed the latest progress in AI applications in the field of male infertility, focusing on the role of AI in the assessment of sperm concentration, motility, morphology, DNA fragmentation index, and the treatment of non-obstructive azoospermia. It has been reported that AI technologies, such as convolutional neural networks, demonstrate high accuracy and efficiency in assessing sperm concentration and motility. Particularly in morphological analysis, the performance of AI has been validated in multiple studies, significantly enhancing objectivity and clinical utility. However, the assessment of DNA fragmentation index remains underexplored due to the lack of support in advanced imaging technology, with only a few models showing promise. Additionally, AI significantly improves sperm detection rates in modified testicular sperm extraction through AI-driven image recognition, offering a breakthrough in the treatment of patients with non-obstructive azoospermia. We also discussed the application of natural language processing technology in patient pre-consultation and follow-up, such as automated data collection and intelligent tracking systems, which demonstrate AI's potential to optimize medical workflows. In the future, with the accumulation of high-quality datasets, algorithm optimization, and advances in imaging technology, the application of artificial intelligence is expected to enable multi-dimensional comprehensive screening and play a greater role in the diagnosis and treatment of male infertility.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.