Pith. sign in

REVIEW 3 major objections 5 minor 53 references

Reviewed by Pith at T0; open to challenge.

T0 means a machine referee read the full paper against a public rubric. The mark states how deep the mechanical check went, never who wrote it. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · grok-4.5

Machine learning can label male fertility status from standard semen parameters at 94% accuracy on a public 85-sample dataset.

2026-07-10 07:34 UTC pith:2RIC5QJM

load-bearing objection 94% accuracy is mostly re-learning the WHO thresholds that define the labels from the same three input features; useful as a transparent LazyPredict ranking on VISEM, not as clinical fertility prediction. the 3 major comments →

arxiv 2607.08429 v1 pith:2RIC5QJM submitted 2026-07-09 cs.LG cs.AI

Predicting Male Fertility Using Machine Learning: A Semen Parameters Based Analysis with the VISEM Dataset

Shahnawaz Qureshi , Raja Khurram Shahzad , Muhammad Fozan , Emal Kawal , Syed Aziz Shah , Sattam Al-Anazi , Syed MuhammadZeeshan Iqbal This is my paper
classification cs.LG cs.AI
keywords Male infertilitySemen analysisSperm motilitySperm morphologyMachine learningFertility classificationVISEM datasetClinical decision support
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper asks whether ordinary machine-learning classifiers can turn the three classic semen numbers—sperm concentration, progressive motility, and normal morphology—into a clinically useful fertility label. Using the public VISEM collection of 85 samples that the authors re-labeled into Fertile, Sub-Fertile and Infertile bins according to WHO thresholds, they run more than forty off-the-shelf algorithms through LazyPredict. The Nearest Centroid classifier comes out on top at 94.2 percent cross-validated accuracy, with multiclass AUC scores of 0.95–1.00. The authors argue that such models can replace the subjective, observer-dependent steps of traditional semen evaluation and give andrology clinics a fast, objective decision-support tool. A sympathetic reader cares because male-factor infertility is under-diagnosed and still rests on manual microscopy that varies from lab to lab; an automated classifier that recovers WHO categories with high fidelity could standardize first-line assessment and free clinicians to focus on borderline or complex cases.

Core claim

When semen samples are labeled into three fertility classes by WHO thresholds on concentration, progressive motility and morphology, a simple Nearest Centroid classifier recovers those labels at 94.2 percent accuracy under 5-fold cross-validation and yields multiclass AUC values of 0.95, 1.00 and 0.97. The result is presented as evidence that machine learning can furnish fast, objective fertility assessments from routine semen parameters.

What carries the argument

Nearest Centroid classifier: each sample is assigned to the fertility class whose mean feature vector (centroid) in the three-dimensional space of concentration, motility and morphology is nearest; the paper shows this distance-to-centroid rule outperforms more complex models on the labeled VISEM data.

Load-bearing premise

The three fertility labels are treated as independent clinical ground truth, yet they are defined by hard thresholds on exactly the same three features the classifiers receive, so high accuracy may simply mean the models re-learned the WHO binning rules rather than discovering new fertility signals.

What would settle it

Collect a fresh set of semen samples that have true reproductive outcomes (time-to-pregnancy or live birth) independent of the WHO thresholds, re-train and re-test the same classifiers, and check whether accuracy remains near 94 percent when the labels are no longer a deterministic function of the input features.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Clinics could insert a lightweight Nearest Centroid (or equivalent) step into existing semen-analysis software to produce an immediate Fertile / Sub-Fertile / Infertile flag.
  • Borderline samples that currently produce inter-observer disagreement could be flagged automatically for second-look review.
  • The same three-parameter model could serve as a triage filter before more expensive assays such as DNA fragmentation or hormonal panels.
  • Assisted-reproduction centers could use the classifier output as a standardized input when counseling couples about IVF versus less invasive options.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Because the labels are deterministic functions of the inputs, the experiment mainly demonstrates that LazyPredict can rediscover a three-rule decision table; the same pipeline could be re-run on any clinical dataset whose labels are independent of the measured features.
  • The extreme class imbalance (only 8 Infertile samples) means that future work should report per-class precision-recall rather than overall accuracy before the method is considered ready for triage.
  • Adding continuous outcome data (time-to-pregnancy) would turn the present classification task into a genuine risk-prediction problem and would falsify or confirm the clinical utility claim.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper applies more than 40 supervised classifiers (via LazyPredict) to the VISEM dataset of 85 semen samples in order to assign each sample to one of three fertility categories (Fertile/Sub-Fertile/Infertile). Labels are obtained by applying WHO 6th-edition thresholds on progressive motility, normal morphology and sperm concentration; the same three quantities serve as the sole model features. Nearest Centroid is reported to reach 94.2 % accuracy under 5-fold cross-validation, with multiclass ROC-AUC values of 0.95/1.00/0.97, and the authors conclude that such models can furnish objective clinical decision support for andrology and ART.

Significance. If the reported accuracy reflected genuine out-of-sample prediction of an independent fertility endpoint, the work would be a useful demonstration that simple, interpretable classifiers can automate WHO-based semen grading. The public VISEM resource, the systematic LazyPredict screen, and the transparent reporting of confusion matrices and ROC curves are positive methodological contributions. However, because the target labels are a deterministic function of the identical three features supplied to every model, the experiment reduces to rule recovery on a tiny, imbalanced table (n=8 in the Infertile class). Consequently the numerical results, while reproducible, do not constitute evidence of clinical fertility prediction and the claimed decision-support value remains unsubstantiated.

major comments (3)
  1. §4 “Fertility Classification Criteria” and Table 1 define the three class labels solely by hard thresholds on progressive motility, morphology and concentration—the exact features later used as model inputs. No pregnancy outcome, partner data or other external fertility endpoint is employed. The learning task is therefore pure recovery of the authors’ own labeling function; the 94.2 % accuracy of Nearest Centroid (and the near-perfect AUCs) is the expected consequence of that circular construction rather than a demonstration of predictive power. The central claim that the models “predict male fertility status” and can “support clinical decision-making” is thereby undermined.
  2. The Infertile class contains only eight samples (Table 2). With 5-fold CV this yields folds that may contain zero or one positive example, rendering accuracy, F1 and AUC estimates for that class statistically unstable. The single reported misclassification (Average o Slow) further indicates that performance is driven by the two larger, well-separated classes. Any claim of robustness must be qualified by this extreme imbalance and by the absence of an independent test cohort.
  3. The paper repeatedly equates recovery of WHO bins with “objective assessments of semen quality” that can inform ART (Abstract, §6, §7). Because the bins are already the clinical standard, an ML model that merely re-implements them adds little beyond automation of a trivial decision tree. Without either (a) an external fertility endpoint or (b) a clear statement that the contribution is limited to automated rule application, the clinical-utility narrative overreaches the experimental design.
minor comments (5)
  1. Equation (1) is simply the definition of accuracy; it does not require a numbered display and is not specific to LazyPredict.
  2. Figures 3–5 are box-plots of the three features stratified by the labels that were themselves derived from those features; they therefore illustrate the labeling rule rather than independent class separability. Captions should note this dependence.
  3. The TZI formula is introduced but never used as a feature; either drop it or clarify its role.
  4. Several references appear as “unpublished” or lack DOIs (e.g., Dobrovolny et al., Saadat et al.); these should be completed or removed.
  5. Typographical inconsistencies: “E-Serivces”, “na¨ ıve”, “Rregion-based”, “lthe probability”, “Var smoothingparameter”.

Circularity Check

3 steps flagged

Fertility labels are deterministic WHO thresholds on the exact input features, so 94% accuracy is rule recovery by construction, not independent prediction.

specific steps
  1. self definitional [§4 Fertility Classification Criteria + Table 1]
    "To categorize male fertility potential, we used classification thresholds based on the WHO’s 6th edition manual for semen evaluation Organization (2021). The dataset was labeled into three fertility classes, i.e, Fertile, Sub-Fertile, and Infertile, based on progressive motility (≥40%, 20–39%, and <20%), morphology (≥4%, 2–3%, and <2%), and sperm concentration (≥15, 10–14, and <10 million/mL) ... as illustrated in the table 1."

    The target variable Y (Fertile/Sub-Fertile/Infertile) is defined as a deterministic piecewise function of the three input features X that are later fed to the classifiers. Training any model to predict Y from X is therefore equivalent to recovering the authors' own thresholding rule; the reported 94.2% accuracy and AUCs measure fidelity of that recovery, not an independent clinical prediction of fertility status.

  2. self definitional [Abstract + §5 Results (model performance)]
    "Among the more than 40 algorithms tested, the Nearest Centroid classifier achieved an accuracy of 94.2% ... This study illustrates that machine learning models can provide fast, accurate, and objective assessments of semen quality, potentially supporting clinical decision-making in andrology..."

    The abstract and results present the high accuracy as evidence that ML can assess fertility and support clinical decisions. Because the labels are constructed from the features themselves (see prior step), the accuracy figure is tautological and cannot support the clinical-utility claim; it only shows that a centroid classifier can approximate the WHO binning function already applied by the authors.

  3. fitted input called prediction [§4 Machine Learning Models’ Evaluation + LazyPredict Benchmarking]
    "After pre-processing and feature engineering, the dataset was used to train and assess multiple classification models using the LazyPredict framework. ... Models that achieved over 90% accuracy demonstrated a significant improvement compared to the ZeroR baseline..."

    The features are the same quantities used to create the labels; any model that can learn simple thresholds (Nearest Centroid, SVM, QDA, etc.) will necessarily achieve high accuracy on this constructed task. Calling the result a 'prediction' of fertility status after fitting on the definitional features is statistically forced rather than an out-of-sample clinical forecast.

full rationale

The paper's central claim is that ML (esp. Nearest Centroid at 94.2% CV accuracy, high multiclass ROC-AUC) can classify male fertility status from semen parameters and thereby support clinical decision-making. However, Section 4 and Table 1 define the three target classes solely by hard thresholds applied to progressive motility, normal morphology, and sperm concentration—the identical three features supplied to every classifier. No external fertility endpoint (pregnancy, live birth, partner data, DNA fragmentation as label, etc.) is used. Consequently the supervised task reduces to recovering the authors' own labeling function on an 85-row table. High accuracy, perfect separation of the middle class, and success of a simple centroid model are expected by construction rather than evidence of clinical prediction. This is a pure self-definitional circularity; the derivation chain collapses at the label-creation step. No self-citation chain or ansatz smuggling is involved; the circularity is definitional and load-bearing for the headline result.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The entire claim rests on treating WHO 6th-edition cut-offs as ground-truth fertility labels and on the assumption that recovering those labels with off-the-shelf classifiers constitutes a clinically useful fertility predictor. No free parameters are fitted beyond the default LazyPredict settings; no new physical entities are invented.

axioms (2)
  • domain assumption WHO 6th-edition thresholds on progressive motility, morphology and concentration correctly partition men into Fertile / Sub-Fertile / Infertile classes that are clinically meaningful.
    Invoked in §4 and Table 1 to create the three-class target; without this axiom the accuracy numbers have no fertility interpretation.
  • domain assumption The 85-sample VISEM cohort is sufficiently representative for five-fold cross-validation estimates to generalize.
    Stated implicitly by reporting 94.2% CV accuracy as evidence of robustness despite only 8 Infertile cases.

pith-pipeline@v1.1.0-grok45 · 17474 in / 2294 out tokens · 36429 ms · 2026-07-10T07:34:55.961812+00:00 · methodology

0 comments
read the original abstract

Male infertility is a significant yet often underdiagnosed aspect of reproductive health, with semen analysis serving as the cornerstone of clinical evaluation. To address this problem, this study investigates the use of machine learning algorithms to classify male fertility status based on key semen parameters, i.e., sperm concentration, motility, and morphology, using the VISEM dataset. This dataset includes semen samples from 85 participants, classified into three categories, i.e., Fertile, Sub-Fertile, and Infertile, according to the World Health Organization's criteria. After pre-processing and feature engineering, the dataset was used to train and assess multiple classification models using the LazyPredict framework. Among the more than 40 algorithms tested, the Nearest Centroid classifier achieved an accuracy of 94.2%, outperforming other models such as Support Vector Machines and Quadratic Discriminant Analysis. The model's robustness was validated using 5-fold cross-validation and multiclass ROC-AUC analysis. This study illustrates that machine learning models can provide fast, accurate, and objective assessments of semen quality, potentially supporting clinical decision-making in andrology and assisted reproductive technologies. These findings emphasize the growing potential of machine learning to enhance fertility diagnostics and inform patient-specific treatment strategies.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

53 extracted references · 53 canonical work pages · 1 internal anchor

  1. [1]

    The World Journal of Men's Health , volume =

    Sperm morphology assessment in the era of intracytoplasmic sperm injection: reliable results require focus on standardization, quality control, and training , author =. The World Journal of Men's Health , volume =. 2022 , publisher =

    work page 2022

  2. [2]

    The Lancet , volume =

    Relation between semen quality and fertility: a population-based study of 430 first-pregnancy planners , author =. The Lancet , volume =. 1998 , publisher =

    work page 1998

  3. [3]

    American Journal of Obstetrics and Gynecology , volume =

    Rate of transport of spermia in human uterus and tubes , author =. American Journal of Obstetrics and Gynecology , volume =. 1944 , publisher =

    work page 1944

  4. [4]

    British medical journal , volume =

    Evidence for decreasing quality of semen during past 50 years , author =. British medical journal , volume =. 1992 , publisher =

    work page 1992

  5. [5]

    Journal of clinical epidemiology , volume =

    A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models , author =. Journal of clinical epidemiology , volume =. 2019 , publisher =

    work page 2019

  6. [6]

    2023 , publisher =

    The Ethics of Testing for Adult-Onset Conditions in Pediatric and Prenatal Genetics , author =. 2023 , publisher =

    work page 2023

  7. [7]

    Human reproduction update , volume =

    World Health Organization reference values for human semen characteristics , author =. Human reproduction update , volume =. 2010 , publisher =

    work page 2010

  8. [8]

    Nature Reviews Urology , year =

    Advances in sperm analysis: techniques, discoveries and applications , author =. Nature Reviews Urology , year =

    work page

  9. [9]

    Reproductive sciences , pages =

    Current insights and latest updates in sperm motility and associated applications in assisted reproduction , author =. Reproductive sciences , pages =. 2022 , publisher =

    work page 2022

  10. [10]

    Pattern Analysis and Applications , volume =

    A regression model based on the nearest centroid neighborhood , author =. Pattern Analysis and Applications , volume =. 2018 , publisher =

    work page 2018

  11. [11]

    Linear and Quadratic Discriminant Analysis: Tutorial

    Linear and quadratic discriminant analysis: Tutorial , author =. arXiv preprint arXiv:1906.02590 , year =

    work page internal anchor Pith review Pith/arXiv arXiv 1906

  12. [12]

    Fertility and sterility , volume =

    Sperm morphology as diagnosed by strict criteria: probing the impact of teratozoospermia on fertilization rate and pregnancy outcome in a large in vitro fertilization population , author =. Fertility and sterility , volume =. 1994 , publisher =

    work page 1994

  13. [13]

    Proceedings of the 10th ACM Multimedia Systems Conference , pages =

    Visem: A multimodal video dataset of human spermatozoa , author =. Proceedings of the 10th ACM Multimedia Systems Conference , pages =. 2019 , publisher =

    work page 2019

  14. [14]

    Scientific reports , volume =

    Machine learning-based analysis of sperm videos and participant data for male fertility prediction , author =. Scientific reports , volume =. 2019 , publisher =

    work page 2019

  15. [15]

    Molecular reproduction and development , volume =

    Methodological considerations for examining the relationship between sperm morphology and motility , author =. Molecular reproduction and development , volume =. 2020 , publisher =

    work page 2020

  16. [16]

    9TH International Conference on Information and Communications Technology(ICT) Applications, Ilorin, Kwara , pages =

    A Prediction Model for the Likelihood of infertility in women , author =. 9TH International Conference on Information and Communications Technology(ICT) Applications, Ilorin, Kwara , pages =

    work page

  17. [17]

    Fertility and sterility , volume =

    Sperm mitochondrial DNA depletion in men with asthenospermia , author =. Fertility and sterility , volume =. 2004 , publisher =

    work page 2004

  18. [18]

    Scientific Reports , volume =

    A new model for determining risk of male infertility from serum hormone levels, without semen analysis , author =. Scientific Reports , volume =. 2024 , publisher =

    work page 2024

  19. [19]

    Human Reproduction , volume =

    Computer-assisted semen analysis systems: comparison with routine evaluation and prognostic value in male fertility and assisted reproduction , author =. Human Reproduction , volume =. 1995 , publisher =

    work page 1995

  20. [20]

    Journal of human reproductive sciences , volume =

    Trends of male factor infertility, an important cause of infertility: A review of literature , author =. Journal of human reproductive sciences , volume =. 2015 , publisher =

    work page 2015

  21. [21]

    9th International Conference on Big Data Analytics (ICBDA) , pages =

    An AI Framework for Predicting the Winner of the Grammys , author =. 9th International Conference on Big Data Analytics (ICBDA) , pages =. 2024 , organization =

    work page 2024

  22. [22]

    StatPearls [internet] , year =

    Male infertility , author =. StatPearls [internet] , year =

    work page

  23. [23]

    The Journal of Clinical Endocrinology & Metabolism , volume =

    Approach to the patient with oligozoospermia , author =. The Journal of Clinical Endocrinology & Metabolism , volume =. 2013 , publisher =

    work page 2013

  24. [24]

    Oxidants, Antioxidants and Impact of the Oxidative Status in Male Reproduction , pages =

    Oxidative stress and sperm dysfunction: An insight into dynamics of semen proteome , author =. Oxidants, Antioxidants and Impact of the Oxidative Status in Male Reproduction , pages =. 2019 , publisher =

    work page 2019

  25. [25]

    1999 , publisher =

    WHO laboratory manual for the examination of human semen and sperm-cervical mucus interaction , author =. 1999 , publisher =

    work page 1999

  26. [26]

    Acta Informatica Medica , volume =

    A review of machine learning approaches in assisted reproductive technologies , author =. Acta Informatica Medica , volume =. 2019 , publisher =

    work page 2019

  27. [27]

    Clinical biochemistry , volume =

    Fertility and infertility: Definition and epidemiology , author =. Clinical biochemistry , volume =. 2018 , publisher =

    work page 2018

  28. [28]

    Biodemography and Social Biology , volume =

    Biomarkers for demographic research: sperm counts and other male infertility biomarkers , author =. Biodemography and Social Biology , volume =. 2020 , publisher =

    work page 2020

  29. [29]

    2021 , edition =

    WHO laboratory manual for the examination and processing of human semen , author =. 2021 , edition =

    work page 2021

  30. [30]

    Computers in biology and medicine , volume =

    Private naive bayes classification of personal biomedical data: application in cancer data analysis , author =. Computers in biology and medicine , volume =. 2019 , publisher =

    work page 2019

  31. [31]

    Nature Reviews Urology , volume =

    Machine learning for sperm selection , author =. Nature Reviews Urology , volume =. 2021 , publisher =

    work page 2021

  32. [32]

    Asian Journal of Andrology , volume =

    Influence of sperm morphology on pregnancy outcome and offspring in in vitro fertilization and intracytoplasmic sperm injection: a matched case-control study , author =. Asian Journal of Andrology , volume =. 2021 , publisher =

    work page 2021

  33. [33]

    and Nakazawa, A

    Adinugroho, S. and Nakazawa, A. , title =. 2023 , journal =

    work page 2023

  34. [34]

    and Benes, J

    Dobrovolny, M. and Benes, J. and Langer, J. and Krejcar, O. and Selamat, A. , title =. 2023 , journal =

    work page 2023

  35. [35]

    Nguyen, V. D. and Ngo, T. T. H. and Duong, L. M. and Tran, M. H. , title =. 2023 , booktitle =

    work page 2023

  36. [36]

    and Amiriparian, S

    Ottl, S. and Amiriparian, S. and Gerczuk, M. and Schuller, B. W. , title =. 2021 , journal =

    work page 2021

  37. [37]

    and Sepehri, M

    Saadat, H. and Sepehri, M. M. and Borna, M.-R. and Maleki, B. , title =. 2023 , journal =

    work page 2023

  38. [38]

    and Hicks, S

    Thambawita, V. and Hicks, S. A. and Storås, A. M. and Nguyen, T. and Andersen, J. M. and Witczak, O. and Haugen, T. B. and Hammer, H. L. and Halvorsen, P. and Riegler, M. A. , title =. 2023 , journal =

    work page 2023

  39. [39]

    and Belaid, A

    Tiab, I. and Belaid, A. and Yahiaoui, S. and Belazzougui, D. and Hafsi, N. and Iguer-Ouada, M. , title =. 2023 , journal =

    work page 2023

  40. [40]

    and Raudonis, V

    Valiuškaitė, V. and Raudonis, V. and Maskeliūnas, R. and Damaševičius, R. and Krilavičius, T. , title =. 2023 , journal =

    work page 2023

  41. [41]

    iscience , volume =

    motilitAI: A machine learning framework for automatic prediction of human sperm motility , author =. iscience , volume =. 2022 , publisher =

    work page 2022

  42. [42]

    Ronneberger and P.Fischer and T

    O. Ronneberger and P.Fischer and T. Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI). 2015

    work page 2015

  43. [43]

    and Masson, Puneet and VerMilyea, Matthew and Kam, Moshe , title =

    Choi, Ji-won and Alkhoury, Ludvik and Urbano, Leonardo F. and Masson, Puneet and VerMilyea, Matthew and Kam, Moshe , title =. Scientific Reports , year =. doi:10.1038/s41598-022-20943-9 , url =

    work page doi:10.1038/s41598-022-20943-9

  44. [44]

    Reproductive Health , year =

    Gbagbo, Fred Yao and Ameyaw, Edward Kwabena and Yaya, Sanni , title =. Reproductive Health , year =. doi:10.1186/s12978-024-01924-9 , url =

    work page doi:10.1186/s12978-024-01924-9

  45. [45]

    Reproductive Health , year =

    Darand, Mina and Salimi, Zahra and Ghorbani, Moloud and Sadeghi, Narges and Babaie, Syavash and Hosseinzadeh, Mahdieh , title =. Reproductive Health , year =. doi:10.1186/s12978-023-01664-2 , url =

    work page doi:10.1186/s12978-023-01664-2

  46. [46]

    Scientific Reports , volume =

    Deep learning-based video analysis of human spermatozoa for automated motility assessment , author =. Scientific Reports , volume =. 2021 , publisher =

    work page 2021

  47. [47]

    2018 , publisher =

    Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning , author =. 2018 , publisher =

    work page 2018

  48. [48]

    Neural computation , volume =

    Approximate statistical tests for comparing supervised classification learning algorithms , author =. Neural computation , volume =. 1998 , publisher =

    work page 1998

  49. [49]

    Machine learning , volume =

    Support-vector networks , author =. Machine learning , volume =. 1995 , publisher =

    work page 1995

  50. [50]

    AA , volume =

    The optimality of naive Bayes , author =. AA , volume =

    work page

  51. [51]

    Machine learning , volume =

    Random forests , author =. Machine learning , volume =. 2001 , publisher =

    work page 2001

  52. [52]

    Andrology , publisher =

    Human asthenozoospermia: Update on genetic causes, patient management, and clinical strategies , author =. Andrology , publisher =

    work page

  53. [53]

    1996 , issn =

    Methods of Digital Video Microscopy for Colloidal Studies , journal =. 1996 , issn =. doi:https://doi.org/10.1006/jcis.1996.0217 , author =

    work page doi:10.1006/jcis.1996.0217 1996