Web- and Artificial Intelligence–Based Image Recognition For Sperm Motility Analysis: Verification Study
Bibliographic record
Abstract
BACKGROUND: Human sperm quality fluctuates over time. Therefore, it is crucial for couples preparing for natural pregnancy to monitor sperm motility. OBJECTIVE: This study verified the performance of an artificial intelligence-based image recognition and cloud computing sperm motility testing system (Bemaner, Createcare) composed of microscope and microfluidic modules and designed to adapt to different types of smartphones. METHODS: Sperm videos were captured and uploaded to the cloud with an app. Analysis of sperm motility was performed by an artificial intelligence-based image recognition algorithm then results were displayed. According to the number of motile sperm in the vision field, 47 (deidentified) videos of sperm were scored using 6 grades (0-5) by a male-fertility expert with 10 years of experience. Pearson product-moment correlation was calculated between the grades and the results (concentration of total sperm, concentration of motile sperm, and motility percentage) computed by the system. RESULTS: Good correlation was demonstrated between the grades and results computed by the system for concentration of total sperm (r=0.65, P<.001), concentration of motile sperm (r=0.84, P<.001), and motility percentage (r=0.90, P<.001). CONCLUSIONS: This smartphone-based sperm motility test (Bemaner) accurately measures motility-related parameters and could potentially be applied toward the following fields: male infertility detection, sperm quality test during preparation for pregnancy, and infertility treatment monitoring. With frequent at-home testing, more data can be collected to help make clinical decisions and to conduct epidemiological research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".