Evidence map›Paper›PMID 35562203›Full record

ArticleFertility and sterility2022

Automated rare sperm identification from low-magnification microscopy images of dissociated microsurgical testicular sperm extraction samples using deep learning.

Ryan Lee, Luke Witherspoon, Meghan Robinson, Jeong Hyun Lee, Simon P Duffy, Ryan Flannigan, Hongshen Ma

Abstract read
PubMed Publisher
In one paragraph

Article in Fertility and sterility, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
–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

21 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Review
  6. Review
  7. Fresh versus frozen micro-TESE sperm and outcomes.Asian journal of andrology · 2025
    Review
  8. [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 · 2025
    Review
  9. Review
  10. Article
  11. Review
  12. Review
  13. Review
  14. Article
  15. [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 · 2024
    Review
  16. Article
  17. Article
  18. Review
  19. Review
  20. Male infertility.Nature reviews. Disease primers · 2023
    Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Ryan LeeDepartment of Mechanical Engineering, University of British Columbia, Vancouver, British Columbia, Canada; Centre for Blood Research, University of British Columbia, Vancouver, British Columbia, Canada; Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
Luke WitherspoonDepartment of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada; Vancouver Prostate Centre, Vancouver General Hospital, Vancouver, British Columbia, Canada; Department of Urology, The Ottawa Hospital, Ottawa, Ontario, Canada.
Meghan RobinsonDepartment of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada; Vancouver Prostate Centre, Vancouver General Hospital, Vancouver, British Columbia, Canada.
Jeong Hyun LeeDepartment of Mechanical Engineering, University of British Columbia, Vancouver, British Columbia, Canada; Centre for Blood Research, University of British Columbia, Vancouver, British Columbia, Canada.
Simon P DuffyDepartment of Mechanical Engineering, University of British Columbia, Vancouver, British Columbia, Canada; Centre for Blood Research, University of British Columbia, Vancouver, British Columbia, Canada.
Ryan FlanniganDepartment of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada; Vancouver Prostate Centre, Vancouver General Hospital, Vancouver, British Columbia, Canada; Department of Urology, Weill Cornell Medicine, New York, New York. Electronic address: ryan.flannigan@ubc.ca.
Hongshen MaDepartment of Mechanical Engineering, University of British Columbia, Vancouver, British Columbia, Canada; Centre for Blood Research, University of British Columbia, Vancouver, British Columbia, Canada; Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada; Vancouver Prostate Centre, Vancouver General Hospital, Vancouver, British Columbia, Canada; School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada. Electronic address: hongma@mech.ubc.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo develop a machine learning algorithm to detect rare human sperm in semen and microsurgical testicular sperm extraction (microTESE) samples using bright-field (BF) microscopy for nonobstructive azoospermia patients.

designSpermatozoa were collected from fertile men. Testis biopsies were collected from microTESE samples determined to be clinically negative for sperm. A convolutional neural network based on the U-Net architecture was trained using 35,761 BF image patches with fluorescent ground truth image pairs to segment sperm. The algorithm was validated using 7,663 image patches. The algorithm was tested using 7,663 image patches containing abundant sperm, as well as 7,985 image patches containing rare sperm.

settingIn vitro fertilization center and university laboratories. PATIENT(S): Normospermic and nonobstructive azoospermia patients. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Precision (positive predictive value [PPV]), recall (sensitivity), and F1-score of detected sperm locations. RESULT(S): For sperm-only samples, our algorithm achieved 91% PPV, 95.8% sensitivity, and 93.3% F1-score at ×10 magnification. For dissociated microTESE samples doped with an abundant quantity of sperm, our algorithm achieved 84.0% PPV, 72.7% sensitivity, and 77.9% F1-score. For dissociated microTESE samples doped with rare sperm, our algorithm achieved 84.4% PPV, 86.1% sensitivity, and 85.2% F1-score. CONCLUSION(S): Rare sperm can be detected in patients' testis biopsy samples for potential subsequent use in in vitro fertilization-intracytoplasmic sperm injection. A machine learning algorithm can use BF images at ×10 magnification to accurately detect sperm locations using automated imaging.

Indexed as

AzoospermiaDeep LearningOligospermiaHumansMaleMicroscopySpermatozoaSperm RetrievalTestisInfertilitymachine learningmicroTESEnonobstructive azoospermiasperm

Identifiers

PMID35562203

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.