Evidence map›Paper›PMID 35856034›Full record

ArticleiScience2022

motilitAI: A machine learning framework for automatic prediction of human sperm motility.

Sandra Ottl, Shahin Amiriparian, Maurice Gerczuk, Björn W Schuller

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
6.5field-weighted citation impact, top 3% of its field
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

16 citing papers in PubMed, 39 citations in OpenAlex.

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  7. [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
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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

4 authors at 1 institution in 2 countries.

Sandra OttlChair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany.
Shahin AmiriparianChair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany.
Maurice GerczukChair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany.
Björn W SchullerChair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany.
University of Augsburg · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this article, human semen samples from the Visem dataset are automatically assessed with machine learning methods for their quality with respect to sperm motility. Several regression models are trained to automatically predict the percentage (0-100) of progressive, non-progressive, and immotile spermatozoa. The videos are adopted for unsupervised tracking and two different feature extraction methods-in particular custom movement statistics and displacement features. We train multiple neural networks and support vector regression models on the extracted features. Best results are achieved using a linear Support Vector Regressor with an aggregated and quantized representation of individual displacement features of each sperm cell. Compared to the best submission of the Medico Multimedia for Medicine challenge, which used the same dataset and splits, the mean absolute error (MAE) could be reduced from 8.83 to 7.31. We provide the source code for our experiments on GitHub (Code available at: https://github.com/EIHW/motilitAI).

Indexed as

Artificial intelligenceBiocomputational methodBioinformaticsHealth sciencesMedicineReproductive medicine

Identifiers

PMID35856034
PMCPMC9287611
OpenAlexW4283204158

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.