The Role of Artificial Intelligence in Male Infertility: Evaluation and Treatment: A Narrative Review
Abstract
:1. Introduction
2. Methods
3. Artificial Intelligence in Reproductive Medicine: Transformative Applications and Potential Impact
4. Use of Prediction Models for Risk Factors in Infertility Using AI
4.1. Sperm Morphology Assessment
4.2. Using ANN and DL to Predict Seminal Quality
4.3. Computer- and AI-Based Algorithms for Semen Analysis
4.4. Anatomical Variations and AI: Implications for Male Infertility and Testosterone Deficiency Syndrome
5. Future Directions
6. Legal and Ethical Concerns
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
- Eisenberg, M.L.; Esteves, S.C.; Lamb, D.J.; Hotaling, J.M.; Giwercman, A.; Hwang, K.; Cheng, Y.-S. Male infertility. Nat. Rev. Dis. Primer. 2023, 9, 49. [Google Scholar] [CrossRef] [PubMed]
- Inhorn, M.C.; Patrizio, P. Infertility around the globe: New thinking on gender, reproductive technologies and global movements in the 21st century. Hum. Reprod. Update 2015, 21, 411–426. [Google Scholar] [CrossRef] [PubMed]
- Leslie, S.W.; Soon-Sutton, T.L.; Khan, M.A. Male Infertility. In StatPearls; StatPearls Publishing: St. Petersburg, FL, USA, 2023; Available online: http://www.ncbi.nlm.nih.gov/books/NBK562258/ (accessed on 17 November 2023).
- Calogero, A.E.; Cannarella, R.; Agarwal, A.; Hamoda, T.A.-A.A.-M.; Rambhatla, A.; Saleh, R.; Boitrelle, F.; Ziouziou, I.; Toprak, T.; Gul, M.; et al. The Renaissance of Male Infertility Management in the Golden Age of Andrology. World J. Mens. Health. 2023, 41, 237–254. [Google Scholar] [CrossRef]
- Cherouveim, P.; Velmahos, C.; Bormann, C.L. Artificial intelligence for sperm selection—A systematic review. Fertil. Steril. 2023, 120, 24–31. [Google Scholar] [CrossRef] [PubMed]
- Sengupta, P.; Dutta, S.; Krajewska-Kulak, E. The Disappearing Sperms: Analysis of Reports Published between 1980 and 2015. Am. J. Mens. Health 2017, 11, 1279–1304. [Google Scholar] [CrossRef] [PubMed]
- Levine, H.; Jørgensen, N.; Martino-Andrade, A.; Mendiola, J.; Weksler-Derri, D.; Jolles, M.; Pinotti, R.; Swan, S.H. Temporal trends in sperm count: A systematic review and meta-regression analysis of samples collected globally in the 20th and 21st centuries. Hum. Reprod. Update 2023, 29, 157–176. [Google Scholar] [CrossRef] [PubMed]
- Petersen, P.M.; Skakkebaek, N.E.; Vistisen, K.; Rørth, M.; Giwercman, A. Semen quality and reproductive hormones before orchiectomy in men with testicular cancer. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 1999, 17, 941–947. [Google Scholar] [CrossRef]
- Ardestani Zadeh, A.; Arab, D. COVID-19 and male reproductive system: Pathogenic features and possible mechanisms. J. Mol. Histol. 2021, 52, 869–878. [Google Scholar] [CrossRef]
- Medenica, S.; Zivanovic, D.; Batkoska, L.; Marinelli, S.; Basile, G.; Perino, A.; Cucinella, G.; Gullo, G.; Zaami, S. The Future Is Coming: Artificial Intelligence in the Treatment of Infertility Could Improve Assisted Reproduction Outcomes-The Value of Regulatory Frameworks. Diagnostics 2022, 12, 2979. [Google Scholar] [CrossRef]
- ISO/IEC TR 24028:2020; Information Technology—Artificial Intelligence—Overview of Trustworthiness in Artificial Intelligence. ISO: Geneva, Switzerland, 2020. Available online: https://www.iso.org/obp/ui/#iso:std:iso-iec:tr:24028:ed-1:v1:en (accessed on 20 December 2023).
- Zhang, C.; Lu, Y. Study on artificial intelligence: The state of the art and future prospects. J. Ind. Inf. Integr. 2021, 23, 100224. [Google Scholar] [CrossRef]
- Abduljabbar, R.; Dia, H.; Liyanage, S.; Bagloee, S.A. Applications of Artificial Intelligence in Transport: An Overview. Sustainability 2019, 11, 189. [Google Scholar] [CrossRef]
- Bahrammirzaee, A. A comparative survey of artificial intelligence applications in finance: Artificial neural networks, expert system and hybrid intelligent systems. Neural Comput. Appl. 2010, 19, 1165–1195. [Google Scholar] [CrossRef]
- Javorsky, E.; Tegmark, M.; Helfand, I. Lethal autonomous weapons. BMJ 2019, 364, l1171. [Google Scholar] [CrossRef]
- You, J.B.; McCallum, C.; Wang, Y.; Riordon, J.; Nosrati, R.; Sinton, D. Machine learning for sperm selection. Nat. Rev. Urol. 2021, 18, 387–403. [Google Scholar] [CrossRef]
- Curchoe, C.L.; Malmsten, J.; Bormann, C.; Shafiee, H.; Farias, A.F.-S.; Mendizabal, G.; Chavez-Badiola, A.; Sigaras, A.; Alshubbar, H.; Chambost, J.; et al. Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us? Fertil. Steril. 2020, 114, 934–940. [Google Scholar] [CrossRef]
- Ory, J.; Ory, J.; Tradewell, M.B.; Tradewell, M.B.; Blankstein, U.; Blankstein, U.; Lima, T.F.; Lima, T.F.; Nackeeran, S.; Nackeeran, S.; et al. Artificial Intelligence Based Machine Learning Models Predict Sperm Parameter Upgrading after Varicocele Repair: A Multi-Institutional Analysis. World J. Mens. Health 2022, 40, 618–626. [Google Scholar] [CrossRef] [PubMed]
- Kulkarni, S.; Seneviratne, N.; Baig, M.S.; Khan, A.H.A. Artificial Intelligence in Medicine: Where Are We Now? Acad. Radiol. 2020, 27, 62–70. [Google Scholar] [CrossRef] [PubMed]
- May, M. Eight ways machine learning is assisting medicine. Nat. Med. 2021, 27, 2–3. [Google Scholar] [CrossRef] [PubMed]
- Yang, S.; Zhu, F.; Ling, X.; Liu, Q.; Zhao, P. Intelligent Health Care: Applications of Deep Learning in Computational Medicine. Front. Genet. 2021, 12, 607471. Available online: https://www.frontiersin.org/articles/10.3389/fgene.2021.607471 (accessed on 27 December 2023). [CrossRef] [PubMed]
- Shahid, N.; Rappon, T.; Berta, W. Applications of artificial neural networks in health care organizational decision-making: A scoping review. PLoS ONE 2019, 14, e0212356. [Google Scholar] [CrossRef]
- Locke, S.; Bashall, A.; Al-Adely, S.; Moore, J.; Wilson, A.; Kitchen, G.B. Natural language processing in medicine: A review. Trends Anaesth. Crit. Care 2021, 38, 4–9. [Google Scholar] [CrossRef]
- Bartoov, B.; Berkovitz, A.; Eltes, F.; Kogosowski, A.; Menezo, Y.; Barak, Y. Real-Time Fine Morphology of Motile Human Sperm Cells is Associated with IVF-ICSI Outcome. J. Androl. 2002, 23, 1–8. [Google Scholar] [CrossRef] [PubMed]
- Gatimel, N.; Moreau, J.; Parinaud, J.; Léandri, R.D. Sperm morphology: Assessment, pathophysiology, clinical relevance, and state of the art in 2017. Andrology 2017, 5, 845–862. [Google Scholar] [CrossRef]
- Björndahl, L.; Kirkman Brown, J. The sixth edition of the WHO Laboratory Manual for the Examination and Processing of Human Semen: Ensuring quality and standardization in basic examination of human ejaculates. Fertil. Steril. 2022, 117, 246–251. [Google Scholar] [CrossRef]
- Bijar, A.; Benavent, A.P.; Mikaeili, M.; Khayati, R. Fully automatic identification and discrimination of sperm’s parts in microscopic images of stained human semen smear. J. Biomed. Sci. Eng. 2012, 05, 384–395. [Google Scholar] [CrossRef]
- Czubaszek, M.; Andraszek, K.; Banaszewska, D.; Walczak-Jędrzejowska, R. The effect of the staining technique on morphological and morphometric parameters of boar sperm. PLoS ONE 2019, 14, e0214243. [Google Scholar] [CrossRef] [PubMed]
- Maree, L.; Du Plessis, S.S.; Menkveld, R.; Van Der Horst, G. Morphometric dimensions of the human sperm head depend on the staining method used. Hum. Reprod. 2010, 25, 1369–1382. [Google Scholar] [CrossRef]
- Natali, I.; Muratori, M.; Sarli, V.; Vannuccini, M.; Cipriani, S.; Niccoli, L.; Giachini, C. Scoring human sperm morphology using Testsimplets and Diff-Quik slides. Fertil. Steril. 2013, 99, 1227–1232.e2. [Google Scholar] [CrossRef]
- Butola, A.; Popova, D.; Prasad, D.K.; Ahmad, A.; Habib, A.; Tinguely, J.C.; Basnet, P.; Acharya, G.; Senthilkumaran, P.; Mehta, D.S.; et al. High spatially sensitive quantitative phase imaging assisted with deep neural network for classification of human spermatozoa under stressed condition. Sci. Rep. 2020, 10, 13118. [Google Scholar] [CrossRef] [PubMed]
- Agarwal, A.; Henkel, R.; Huang, C.C.; Lee, M.S. Automation of human semen analysis using a novel artificial intelligence optical microscopic technology. Andrologia 2019, 51, e13440. [Google Scholar] [CrossRef]
- Sahoo, A.J.; Kumar, Y. Seminal quality prediction using data mining methods. Technol. Health Care Off. J. Eur. Soc. Eng. Med. 2014, 22, 531–545. [Google Scholar] [CrossRef]
- Gil, D.; Girela, J.L.; De Juan, J.; Gomez-Torres, M.J.; Johnsson, M. Predicting seminal quality with artificial intelligence methods. Expert. Syst. Appl. 2012, 39, 12564–12573. [Google Scholar] [CrossRef]
- Bidgoli, A.A.; Komleh, H.E.; Mousavirad, S.J. Seminal quality prediction using optimized artificial neural network with genetic algorithm. In Proceedings of the 2015 9th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Turkey, 26–28 November 2015; IEEE: New York, NY, USA, 2015; pp. 695–699. [Google Scholar] [CrossRef]
- Girela, J.L.; Gil, D.; Johnsson, M.; Gomez-Torres, M.J.; De Juan, J. Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods. Biol. Reprod. 2013, 88, 99. [Google Scholar] [CrossRef] [PubMed]
- Soltanzadeh, S.; Zarandi, M.H.F.; Astanjin, M.B. A hybrid fuzzy clustering approach for fertile and unfertile analysis. In Proceedings of the 2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS), El Paso, TX, USA, 31 October–4 November 2016; IEEE: New York, NY, USA, 2016; pp. 1–6. [Google Scholar] [CrossRef]
- Candemir, C. Estimating the Semen Quality from Life Style Using Fuzzy Radial Basis Functions. Int. J. Mach. Learn. Comput. 2018, 8, 44–48. [Google Scholar] [CrossRef]
- Simfukwe, M.; Kunda, D.; Christopher, C. Comparing Naive Bayes Method and Artificial Neural Network for Semen Quality Categorization. Int. J. Innov. Sci. Eng. Technol. 2015, 2, 689. [Google Scholar]
- El-Shafeiy, E.; El-Desouky, A.; El-Ghamrawy, S. An Optimized Artificial Neural Network Approach Based on Sperm Whale Optimization Algorithm for Predicting Fertility Quality. Stud. Inform. Control. 2018, 27, 349–358. [Google Scholar] [CrossRef]
- Ma, J.; Afolabi, D.O.; Ren, J.; Zhen, A. Predicting Seminal Quality via Imbalanced Learning with Evolutionary Safe-Level Synthetic Minority Over-Sampling Technique. Cogn. Comput. 2021, 13, 833–844. [Google Scholar] [CrossRef]
- GhoshRoy, D.; Alvi, P.A.; Santosh, K.C. Explainable AI to Predict Male Fertility Using Extreme Gradient Boosting Algorithm with SMOTE. Electronics 2023, 12, 15. [Google Scholar] [CrossRef]
- Yibre, A.M.; Koçer, B. Semen quality predictive model using Feed Forwarded Neural Network trained by Learning-Based Artificial Algae Algorithm. Eng. Sci. Technol. Int. J. 2021, 24, 310–318. [Google Scholar] [CrossRef]
- Vickram, A.S.; Kamini, A.R.; Das, R.; Pathy, M.R.; Parameswari, R.; Archana, K.; Sridharan, T.B. Validation of artificial neural network models for predicting biochemical markers associated with male infertility. Syst. Biol. Reprod. Med. 2016, 62, 258–265. [Google Scholar] [CrossRef]
- Mittal, S.; Mielnik, A.; Bolyakov, A.; Schlegel, P.N.; Paduch, D. Initial experience with fluorescence activated cell sorting of spermatozoa from testis tissue: A novel method for sperm isolation after TESE. Fertil. Steril. 2016, 106, e92. [Google Scholar] [CrossRef]
- Lee, R.; Witherspoon, L.; Robinson, M.; Lee, J.H.; Duffy, S.P.; Flannigan, R.; Ma, H. Automated rare sperm identification from low-magnification microscopy images of dissociated microsurgical testicular sperm extraction samples using deep learning. Fertil. Steril. 2022, 118, 90–99. [Google Scholar] [CrossRef] [PubMed]
- Engel, K.M.; Grunewald, S.; Schiller, J.; Paasch, U. Automated semen analysis by SQA Vision® versus the manual approach-A prospective double-blind study. Andrologia 2019, 51, e13149. [Google Scholar] [CrossRef] [PubMed]
- Riegler, M.A.; Stensen, M.H.; Witczak, O.; Andersen, J.M.; A Hicks, S.; Hammer, H.L.; Delbarre, E.; Halvorsen, P.; Yazidi, A.; Holst, N.; et al. Artificial intelligence in the fertility clinic: Status, pitfalls and possibilities. Hum. Reprod. Oxf. Engl. 2021, 36, 2429–2442. [Google Scholar] [CrossRef] [PubMed]
- Tomlinson, M.J.; Naeem, A. CASA in the medical laboratory: CASA in diagnostic andrology and assisted conception. Reprod. Fertil. Dev. 2018, 30, 850–859. [Google Scholar] [CrossRef]
- Amann, R.P.; Waberski, D. Computer-assisted sperm analysis (CASA): Capabilities and potential developments. Theriogenology 2014, 81, e1–e3. [Google Scholar] [CrossRef] [PubMed]
- Finelli, R.; Leisegang, K.; Tumallapalli, S.; Henkel, R.; Agarwal, A. The validity and reliability of computer-aided semen analyzers in performing semen analysis: A systematic review. Transl. Androl. Urol. 2021, 10, 3069–3079. [Google Scholar] [CrossRef]
- Brock, C.; Nielsen, L.M.; Lelic, D.; Drewes, A.M. Pathophysiology of chronic pancreatitis. World J. Gastroenterol. 2013, 19, 7231–7240. [Google Scholar] [CrossRef]
- Hansen, T.W.; Li, Y.; Boggia, J.; Thijs, L.; Richart, T.; Staessen, J.A. Predictive Role of the Nighttime Blood Pressure. Hypertension 2011, 57, 3–10. [Google Scholar] [CrossRef]
- Kanakasabapathy, M.K.; Sadasivam, M.; Singh, A.; Preston, C.; Thirumalaraju, P.; Venkataraman, M.; Bormann, C.L.; Draz, M.S.; Petrozza, J.C.; Shafiee, H. An automated smartphone-based diagnostic assay for point-of-care semen analysis. Sci. Transl. Med. 2017, 9, eaai7863. [Google Scholar] [CrossRef]
- Sengupta, P.; Dutta, S.; Roychoudhury, S.; Vizzarri, F.; Slama, P. Revolutionizing semen analysis: Introducing Mojo AISA, the next-gen artificial intelligence microscopy. Front. Cell Dev. Biol. 2023, 11, 1203708. [Google Scholar] [CrossRef]
- Kantartzi, P.D.; Goulis, C.D.; Goulis, G.D.; Papadimas, I. Male infertility and varicocele: Myths and reality. Hippokratia 2007, 11, 99–104. [Google Scholar]
- Wright, E.J.; Young, G.P.; Goldstein, M. Reduction in testicular temperature after varicocelectomy in infertile men. Urology 1997, 50, 257–259. [Google Scholar] [CrossRef]
- Fujisawa, M.; Yoshida, S.; Kojima, K.; Kamidono, S. Biochemical changes in testicular varicocele. Arch. Androl. 1989, 22, 149–159. [Google Scholar] [CrossRef]
- Naughton, C.K.; Nangia, A.K.; Agarwal, A. Pathophysiology of varicoceles in male infertility. Hum. Reprod. Update 2001, 7, 473–481. [Google Scholar] [CrossRef]
- Perruzza, D.; Bernabò, N.; Rapino, C.; Valbonetti, L.; Falanga, I.; Russo, V.; Mauro, A.; Berardinelli, P.; Stuppia, L.; Maccarrone, M.; et al. Artificial Neural Network to Predict Varicocele Impact on Male Fertility through Testicular Endocannabinoid Gene Expression Profiles. BioMed Res. Int. 2018, 2018, 3591086. [Google Scholar] [CrossRef] [PubMed]
- Di Guardo, F.; Vloeberghs, V.; Bardhi, E.; Blockeel, C.; Verheyen, G.; Tournaye, H.; Drakopoulos, P. Low Testosterone and Semen Parameters in Male Partners of Infertile Couples Undergoing IVF with a Total Sperm Count Greater than 5 Million. J. Clin. Med. 2020, 9, 3824. [Google Scholar] [CrossRef] [PubMed]
- Novaes, M.T.; de Carvalho, O.L.F.; Ferreira, P.H.G.; Tiraboschi, T.L.N.; Silva, C.S.; Zambrano, J.C.; Gomes, C.M.; Miranda, E.d.P.; Júnior, O.A.d.C.; Júnior, J.d.B. Prediction of secondary testosterone deficiency using machine learning: A comparative analysis of ensemble and base classifiers, probability calibration, and sampling strategies in a slightly imbalanced dataset. Inform. Med. Unlocked 2021, 23, 100538. [Google Scholar] [CrossRef]
- Diaz, P.; Dullea, A.; Chu, K.Y.; Zizzo, J.; Loloi, J.; Reddy, R.; Campbell, K.; Li, P.S.; Ramasamy, R. Future of Male Infertility Evaluation and Treatment: Brief Review of Emerging Technology. Urology 2022, 169, 9–16. [Google Scholar] [CrossRef] [PubMed]
- Elzanaty, S.; Malm, J. Comparison of semen parameters in samples collected by masturbation at a clinic and at home. Fertil. Steril. 2008, 89, 1718–1722. [Google Scholar] [CrossRef] [PubMed]
- Agarwal, A.; Selvam, M.K.P.; Sharma, R.; Master, K.; Sharma, A.; Gupta, S.; Henkel, R. Home sperm testing device versus laboratory sperm quality analyzer: Comparison of motile sperm concentration. Fertil. Steril. 2018, 110, 1277–1284. [Google Scholar] [CrossRef]
- Lustgarten Guahmich, N.; Borini, E.; Zaninovic, N. Improving outcomes of assisted reproductive technologies using artificial intelligence for sperm selection. Fertil. Steril. 2023, 120, 729–734. [Google Scholar] [CrossRef] [PubMed]
- Majzoub, A.; Arafa, M.; Khalafalla, K.; AlSaid, S.; Burjaq, H.; Albader, M.; Al-Marzooqi, T.; Esteves, S.C.; Elbardisi, H. Predictive model to estimate the chances of successful sperm retrieval by testicular sperm aspiration in patients with nonobstructive azoospermia. Fertil. Steril. 2021, 115, 373–381. [Google Scholar] [CrossRef] [PubMed]
- You, J.B.; Wang, Y.; McCallum, C.; Tarlan, F.; Hannam, T.; Lagunov, A.; Jarvi, K.; Sinton, D. Live sperm trap microarray for high throughput imaging and analysis. Lab. Chip. 2019, 19, 815–824. [Google Scholar] [CrossRef]
- Feliciani, G.; Mellini, L.; Carnevale, A.; Sarnelli, A.; Menghi, E.; Piccinini, F.; Scarpi, E.; Loi, E.; Galeotti, R.; Giganti, M.; et al. The potential role of MR based radiomic biomarkers in the characterization of focal testicular lesions. Sci. Rep. 2021, 11, 3456. [Google Scholar] [CrossRef] [PubMed]
- La Vignera, S.; Crafa, A.; Condorelli, R.A.; Barbagallo, F.; Mongioì, L.M.; Cannarella, R.; Compagnone, M.; Aversa, A.; Calogero, A.E. Ultrasound aspects of symptomatic versus asymptomatic forms of male accessory gland inflammation. Andrology 2021, 9, 1422–1428. [Google Scholar] [CrossRef]
- Vergallo, G.M.; Marinelli, E.; Luca NM di Zaami, S. Gamete Donation: Are Children Entitled to Know Their Genetic Origins? A Comparison of Opposing Views. The Italian State of Affairs. Eur. J. Health Law 2018, 25, 322–337. [Google Scholar] [CrossRef]
- Rolfes, V.; Bittner, U.; Gerhards, H.; Krüssel, J.-S.; Fehm, T.; Ranisch, R.; Fangerau, H. Artificial Intelligence in Reproductive Medicine—An Ethical Perspective. Geburtshilfe Und Frauenheilkd. 2023, 83, 106–115. [Google Scholar] [CrossRef]
Author | Year | Country | Sample Size | Study Design | Artificial Intelligence Technique | Results/Main Conclusion |
---|---|---|---|---|---|---|
Bartoov et al. [24] | 2001 | France | 100 participants | Prospective cohort | Motile Sperm Organelle Morphology Examination (MSOME) | Positively associated with ICSI fertilization rate (AUC—88%) |
Bijar et al. [27] | 2012 | Iran | N/A | Laboratory-based experimental study | Algorithm involved acquiring stained sperm smear images, applying Bayesian classification for segmentation, and utilizing an iterative method based on structural similarity index and local entropy estimation to identify points on sperm’s tail. | Accuracy of sperm’s head, acrosome, nucleus, and midpiece computed at 94.3%, 92.4%, 95.1%, and 90.2%, respectively. |
Butola et al. [31] | 2020 | India | Phase maps of 10,163 sperm cells | Laboratory-based experimental study | Partially spatial coherent digital holographic method for quantitively phase imaging to study sperm cells under stress conditions. Phase maps were reconstructed and then fed into seven feedforward DNNs. | When validated against a test dataset, DNN provided an average sensitivity, specificity, and accuracy of 85.5%, 94.8%, and 85.6%, respectively. Useful for improving ICSI procedure in ARTs |
Agarwal et al. [32] | 2019 | USA | 131 clinical semen samples | Laboratory-based experimental study | Development of LensHooke X1 pro—an artificial intelligence optical microscopic-based technology meant to quantitively assess sperm concentration, motility, and seminal pH | High degree of correlation in concentration and motility between LensHook X1 Pro and manual methods. |
Author | Year | Country | Sample Size | Study Design | Artificial Neural Networks/Deep Learning Modalities | Accuracy/Results in Comparison to Published Methods |
---|---|---|---|---|---|---|
Gil et al. [34] | 2012 | USA | 100 volunteers | Cross-sectional study | DT, MLP, and SVMs to evaluate performance in the prediction of seminal quality | Prediction accuracy values of 86% for seminal quality parameters, useful in predicting seminal profile of an individual |
Bidgaoli et al. [35] | 2015 | USA | n/a | Laboratory experiment | MLP, SVM, NB, and DT | 93.86% accuracy |
Girela et al. [36] | 2013 | Spain | 123 volunteers | Prospective study | MLP | 90% and 82% accuracies were achieved for sperm concentration and sperm motility, respectively |
Soltanzadeh et al. [37] | 2016 | Tehran | n/a | Laboratory experiment | NB, logistic regression, and fuzzy C-means | AUC of 0.779 |
Candemir [38] | 2018 | USA | n/a | Laboratory experiment | MLP, SVP, and DT | 90% accuracy |
Simfukwe et al. [39] | 2015 | Zambia | 100 volunteers | Laboratory experiment | NB | 97% accuracy |
El-Shafeiy et al. [40] | 2018 | Egypt | n/a | Laboratory experiment | Sperm Whale Optimization Algorithm | 99.6% accuracy |
Ma et al. [41] | 2021 | China | n/a | Laboratory experiment | Evolutionary safe-level synthetic minority over-sampling technique | 97.2% accuracy |
GhoshRoy et al. [42] | 2022 | India | n/a | Laboratory experiment | SVM, adaptive boosting, conventional extreme gradient boost, and random forest | AUC of 0.98 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Venishetty, N.; Alkassis, M.; Raheem, O. The Role of Artificial Intelligence in Male Infertility: Evaluation and Treatment: A Narrative Review. Uro 2024, 4, 23-35. https://doi.org/10.3390/uro4020003
Venishetty N, Alkassis M, Raheem O. The Role of Artificial Intelligence in Male Infertility: Evaluation and Treatment: A Narrative Review. Uro. 2024; 4(2):23-35. https://doi.org/10.3390/uro4020003
Chicago/Turabian StyleVenishetty, Nikit, Marwan Alkassis, and Omer Raheem. 2024. "The Role of Artificial Intelligence in Male Infertility: Evaluation and Treatment: A Narrative Review" Uro 4, no. 2: 23-35. https://doi.org/10.3390/uro4020003
APA StyleVenishetty, N., Alkassis, M., & Raheem, O. (2024). The Role of Artificial Intelligence in Male Infertility: Evaluation and Treatment: A Narrative Review. Uro, 4(2), 23-35. https://doi.org/10.3390/uro4020003