Evidence map›Paper›PMID 40719805›Full record

ArticleJournal of assisted reproduction and genetics2025

An efficient advanced YOLOv8 framework for sperm motility detection.

Chuanjiang Li, Wenjing Xia, Ang Li, Liangshou Gao, Chongming Zhang, Erlei Zhi, Zheng Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of assisted reproduction and genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Chuanjiang LiThe College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, No. 100 Guilin Road, 200234, Shanghai, China.
Wenjing XiaThe College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, No. 100 Guilin Road, 200234, Shanghai, China.
Ang LiThe College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, No. 100 Guilin Road, 200234, Shanghai, China. liang@shnu.edu.cn.ORCID http://orcid.org/0000-0002-6232-5279
Liangshou GaoThe College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, No. 100 Guilin Road, 200234, Shanghai, China.
Chongming ZhangThe College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, No. 100 Guilin Road, 200234, Shanghai, China.
Erlei ZhiDepartment of Andrology, Center for Men's Health, Department of ART, Institute of Urology Urologic Medical Center, Shanghai General Hospital, Shanghai Key Lab of Reproductive Medicine, Shanghai Jiao Tong University, No. 85 Wujin Road, 200080, Shanghai, China.
Zheng LiDepartment of Andrology, Center for Men's Health, Department of ART, Institute of Urology Urologic Medical Center, Shanghai General Hospital, Shanghai Key Lab of Reproductive Medicine, Shanghai Jiao Tong University, No. 85 Wujin Road, 200080, Shanghai, China.

Funding

National Key Research and Development Program of China 2022YFC2702700
6 · The paper itself

Abstract

purposeSperm motility detection is crucial for reproductive health research, which is closely related to human development. Due to the small size and fast movement of sperm, coupled with the complex background, traditional image processing methods can hardly obtain accurate detection results.

methodsTo address these issues, an advanced YOLOv8-based sperm detection model is proposed, named YOLOv8-STA. First, the SPD_Conv structure is integrated into the backbone network to improve the ability for extracting fine-grained information about sperm targets. Secondly, in order to construct effective relationships between input features while preserving more original image information, the partial C2F modules in the backbone and neck networks of the YOLOv8 model are replaced with C2F-Triplet modules, which can capture interactions across dimensions, build channel, and spatial attentions with minimal parameters. Finally, a new 104

resultsExperimental results on the public EVISAN dataset show that the improved YOLOv8-STA model can increase the mAP@0.5 by 4.2% and mAP@0.5:0.95 by 5.4%, compared with the baseline YOLOv8 model.

conclusionThese experimental results verify the effectiveness and superiority of the proposed method in sperm motility detection.

Indexed as

Image Processing, Computer-AssistedSemen AnalysisSpermatozoaSperm MotilityAlgorithmsHumansMaleDeep learningMale fertilitySmall object detectionSperm motility detection

Identifiers

PMID40719805
PMCPMC12559496

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.