Evidence map›Paper›PMID 41531647›Full record

ArticleSmart medicine2026

An Artificial Intelligence-Based Computer Vision Model for Human Sperm Concentration, Motility, and Kinematics Analysis.

Sahar Shahali, David Mortimer, Moira K O'Bryan, Robert McLachlan, Deirdre Zander-Fox, Klaus Ackermann, Gulfam Ahmad, Adrian Neild, Reza Nosrati

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Smart medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

9 authors.

Sahar ShahaliDepartment of Mechanical and Aerospace Engineering Monash University Clayton Australia.
David MortimerOozoa Biomedical Inc West Vancouver British Columbia Canada.ORCID https://orcid.org/0000-0002-0638-2893
Moira K O'BryanSchool of BioSciences Bio21 Molecular Science and Biotechnology Institute University of Melbourne Parkville Australia.
Robert McLachlanMonash IVF Group Cremorne Australia.
Deirdre Zander-FoxMonash IVF Group Cremorne Australia.
Klaus AckermannSoDa Labs and Department of Econometrics and Business Statistics Monash Business School Clayton Australia.ORCID https://orcid.org/0000-0001-7693-8538
Gulfam AhmadAndrology Royal Children's Hospital Melbourne Australia.
Adrian NeildDepartment of Mechanical and Aerospace Engineering Monash University Clayton Australia.
Reza NosratiDepartment of Mechanical and Aerospace Engineering Monash University Clayton Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate assessment of sperm concentration and motility is critical for the diagnosis and management of male infertility. However, current methods, manual hemocytometer counting and commercial computer-aided sperm analysis (CASA) systems, are limited by labor intensity, human error, and variable performance under diverse sample conditions. Here, we present an artificial intelligence (AI)-driven computer vision tool for high-resolution, quantitative analysis of sperm motility and concentration. In a prospective study of 26 semen samples (22 patients, 4 donors), we benchmarked the AI model against manual tracking (using Fiji software) and a commercial CASA system (Hamilton Thorne IVOS II). Our method computed concentration and motility parameters, including straight-line velocity (VSL), curvilinear velocity (VCL), average path velocity (VAP), linearity (LIN), amplitude of lateral head displacement (ALH

Indexed as

andrologyartificial intelligencecomputer visionsemen analysissperm motility

Identifiers

PMID41531647
PMCPMC12794671

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.