Fully On-Smartphone: Efficient Speed-Up of Sperm Tracking Algorithm for Point-of-Care Semen Analysis
Bibliographic record
Abstract
OBJECTIVE: To develop a lowcost, accurate, and fully-on-smartphone system for pointofcare semen analysis and to address the computational bottlenecks in dense sperm tracking on resourceconstrained devices. METHODS: A portable optical attachment enabling microscopic video acquisition through consumer-grade smartphone cameras was developed. Initial deployment of the Joint Probabilistic Data Association Filter (JPDAF) on legacy mobile hardware revealed critical computational bottlenecks, where high-density semen samples ($\approx$ 80 million sperm/mL) induced exponential processing delays exceeding 3 hours due to exhaustive enumeration of feasible joint events in highdensity local regions. To overcome this limitation, the Global-Local Integrated JPDAF (GLIJPDAF) was proposed, featuring a twostage clustering strategy: (1) global clustering via a nondiagonal matrix partitioning algorithm to split the validation matrix into sparse submatrices isolating homogeneous association regions; (2) local clustering with a dynamic kmeans++ algorithm (adaptive k based on local non-zero density) to cap enumeration complexity. RESULTS: Validation across four smartphone platforms using 90 patient samples (50-250 million sperm/mL) demonstrated that GLIJPDAF achieved a mean concentration error of 0.84 million/mL and a mean motility error of 0.74%. Processing times per 3s video remained under 120s on all devices. CONCLUSION: Integrating GLIJPDAF into a smartphone platform enables rapid, accurate pointofcare semen analysis in highdensity samples on resourcelimited devices. SIGNIFICANCE: This accessible pointofcare solution has the potential to broaden male infertility screening in lowresource settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".