Deep Learning-Based Prediction of IVF Success: A Transformer Model Approach
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Abstract
Introduction: Predicting the success of assisted reproductive technology (ART) remains a significant challenge due to the complex interplay of clinical, embryological, and demographic factors. This study aimed to develop and evaluate machine learning models, particularly deep learning-based approaches, to identify key predictors of ART success and improve outcome prediction accuracy.
Methods: A retrospective study was conducted on 500 infertile couples undergoing ART treatment between 2019 and 2024. A comprehensive dataset, including 84 clinical, embryological, and demographic variables, was analyzed. The key predictors included endometrial thickness, endometrial pattern, embryo transfer day, and hormonal markers (PRL, LH). Four machine learning models were implemented: Decision Tree, Random Forest, XGBoost, and a Transformer-Based Model. Data preprocessing involved feature selection, missing data handling, normalization, and oversampling techniques to address class imbalance. The models were trained and validated using k-fold cross-validation, and performance was assessed using accuracy, precision, recall, and F1 score.
Results: The Transformer-Based Model achieved the highest accuracy (99.7%), outperforming traditional machine learning models. Endometrial pattern (r = 0.69) and endometrial thickness (r = 0.82) were the strongest predictors of ART success, emphasizing the dominant role of uterine factors. While female age and infertility duration had a weak negative correlation, male infertility factors and lifestyle variables (smoking, alcohol consumption) showed minimal predictive significance. Model-based feature importance confirmed uterine and embryological factors as the primary determinants of ART success, suggesting a shift in treatment focus.
Conclusions: This study highlights the superiority of deep learning models in ART success prediction, with uterine factors emerging as the strongest predictors. Integrating AI-driven predictive models into clinical practice can enable personalized ART treatment, improved patient counseling, and optimized embryo transfer strategies, ultimately enhancing fertility outcomes.
1- René C, Landry I, de Montigny F. "Couples’ experiences of pregnancy resulting from assisted reproductive technologies: A qualitative meta-synthesis. "Int J Nurs Stud Adv.;4:100059, (2022).
2- Kuhnt AK, Passet-Wittig J. Families formed through assisted reproductive technology: Causes, experiences, and consequences in an international context." Vol. 14, Reproductive biomedicine & society online. Elsevier; p. 289–96, (2022).
3- Fang Y, Wu R, Lee JM, Chan LHM, Chan KYJ- "Microfluidic in-vitro fertilization technologies: Transforming the future of human reproduction." TrAC Trends Anal Chem.;160:116959, (2023).
4- Graham ME, Jelin A, Hoon Jr AH, Wilms Floet AM, Levey E, Graham EM. "Assisted reproductive technology: Short‐and long‐term outcomes." Dev Med Child Neurol.;65(1):38–49, (2023).
5- Chiware TM, Vermeulen N, Blondeel K, Farquharson R, Kiarie J, Lundin K, et al. "IVF and other ART in low-and middle-income countries: a systematic landscape analysis." Hum Reprod Update.;27(2):213–28, (2021).
6- Bulletti FM, Sciorio R, Conforti A, De Luca R, Bulletti C, Palagiano A, et al. "Causes of embryo implantation failure: A systematic review and metaanalysis of procedures to increase embryo implantation potential." Front Endocrinol (Lausanne).; 15:1429193, (2025).
7- Ribas‐Maynou J, Yeste M, Becerra‐Tomás N, Aston KI, James ER, Salas‐Huetos A. "Clinical implications of sperm DNA damage in IVF and ICSI: updated systematic review and meta‐analysis." Biol Rev.; 96(4):1284–300, (2021).
8- Amini P, Ramezanali F, Parchehbaf-Kashani M, Maroufizadeh S, Omani-Samani R, Ghaheri A. "Factors associated with in vitro fertilization live birth outcome: a comparison of different classification methods." Int J Fertil Steril.;15(2):128, (2021).
9- Siristatidis C, Stavros S, Drakeley A, Bettocchi S, Pouliakis A, Drakakis P, et al. "Omics and artificial intelligence to improve in vitro fertilization (IVF) success: a proposed protocol." Diagnostics.;11(5):743, (2021).
10- Hew Y, Kutuk D, Duzcu T, Ergun Y, Basar M. "Artificial Intelligence in IVF Laboratories: Elevating Outcomes Through Precision and Efficiency." Biology (Basel).;13(12):988, (2024).
11- Sadegh-Zadeh SA, Khanjani S, Javanmardi S, Bayat B, Naderi Z, Hajiyavand AM. "Catalyzing IVF outcome prediction: exploring advanced machine learning paradigms for enhanced success rate prognostication." Front Artif Intell.;7:1392611, (2024).
12- Hanevik HI, Hessen DO. "IVF and human evolution." Hum Reprod Update. ;28(4):457–79, (2022).
13- Medenica S, Zivanovic D, Batkoska L, Marinelli S, Basile G, Perino A, et al. "The future is coming: artificial intelligence in the treatment of infertility could improve assisted reproduction outcomes—the value of regulatory frameworks." Diagnostics.;12(12):2979, (2022).
14- Cai J, Jiang X, Liu L, Liu Z, Chen J, Chen K, et al. "Pretreatment prediction for IVF outcomes: generalized applicable model or centre-specific model?" Hum Reprod.;39(2):364–73, (2024).
15- AlSaad R, Abd-Alrazaq A, Choucair F, Ahmed A, Aziz S, Sheikh J. "Harnessing artificial intelligence to predict ovarian stimulation outcomes in in vitro fertilization: scoping review." J Med Internet Res.;26:e53396, (2024).
16- Barnett-Itzhaki Z, Elbaz M, Butterman R, Amar D, Amitay M, Racowsky C, et al. Machine learning vs. classic statistics for the prediction of IVF outcomes. J Assist Reprod Genet.;37(10):2405–12, (2020).
17- Chavez Badiola A. "Artificial Intelligence (AI) for Embryo Ranking and its Use in Human Assisted Reproduction." University of Kent; (2024).
18- Liu K, Zhang Y, Martin C, Ma X, Shen B. "Translational bioinformatics for human reproductive biology research: examples, opportunities and challenges for a future reproductive medicine." Int J Mol Sci.;24(1):4, (2022).
19- Zhang Q, Liang X, Chen Z. "A review of artificial intelligence applications in in vitro fertilization." J Assist Reprod Genet.;42(1):3–14, (2025).
20- Hariton E, Pavlovic Z, Fanton M, Jiang VS. "Applications of artificial intelligence in ovarian stimulation: a tool for improving efficiency and outcomes." Fertil Steril.;120(1):8–16, (2023).
21- Khan HL, Khan S, Bhatti S, Abbas S. "Role of artificial intelligence in quality assurance in ART: a review." Fertil Reprod.;5(01):1–7, (2023).
22- Bulletti FM, Berrettini M, Sciorio R, Bulletti C. "Artificial intelligence algorithms for optimizing assisted reproductive technology programs: A systematic review." Glob Transl Med.;2(2):1–12, (2023).
23- Mapari SA, Shrivastava D, Bedi GN, Pradeep U, Gupta A, Kasat PR, et al. "Revolutionizing Reproduction: The Impact of Robotics and Artificial Intelligence (AI) in Assisted Reproductive Technology: A Comprehensive Review." Cureus.;16(6), (2024).
24- Wu YC, Su ECY, Hou JH, Lin CJ, Lin KB, Chen CH. "Artificial intelligence and assisted reproductive technology: A comprehensive systematic review." Taiwan J Obstet Gynecol.;64(1):11–26, (2025).
25- de Santiago I, Polanski L. "Data-driven medicine in the diagnosis and treatment of infertility." J Clin Med.;11(21):6426, (2022).
26- Abdullah KAL, Atazhanova T, Chavez-Badiola A, Shivhare SB. "Automation in ART: paving the way for the future of infertility treatment." Reprod Sci.;30(4):1006–16, (2023).
27- VerMilyea M, Hall JMM, Diakiw SM, Johnston A, Nguyen T, Perugini D, et al. "Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF." Hum Reprod.;35(4):770–84, (2020).
28- Dehghan S, Rabiei R, Choobineh H, Maghooli K, Nazari M, Vahidi-Asl M. "Comparative study of machine learning approaches integrated with genetic algorithm for IVF success prediction." PLoS One.;19(10):e0310829, (2024).
29- Hassan MR, Al-Insaif S, Hossain MI, Kamruzzaman J. "A machine learning approach for prediction of pregnancy outcome following IVF treatment." Neural Comput Appl.;32(7):2283–97, (2020).
30- Yiğit P, Bener A, Karabulut S. "Comparison of machine learning classification techniques to predict implantation success in an IVF treatment cycle." Reprod Biomed Online.;45(5):923–34, (2022).
31- Patil SN, Wali U V, Swamy MK. "Selection of single potential embryo to improve the success rate of implantation in IVF procedure using machine learning techniques." In: 2019 International Conference on Communication and Signal Processing (ICCSP). IEEE; p. 881–6, (2019).
32- Yan K, Zhang D. "Feature selection and analysis on correlated gas sensor data with recursive feature elimination." Sensors Actuators B Chem.; 212:353–63, (2015).
33- Chen X wen, Jeong JC. "Enhanced recursive feature elimination." In: Sixth international conference on machine learning and applications (ICMLA 2007). IEEE;. p. 429–35, (2007).
34- Brijain M, Patel R, Kushik MR, Rana K. "A survey on decision tree algorithm for classification. "(2014);
35- Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. "Random forest: a classification and regression tool for compound classification and QSAR modeling." J Chem Inf Comput Sci.;43(6):1947–58, (2003).
36- Pereira GA, Hussain M. "A review of transformer-based models for computer vision tasks: Capturing global context and spatial relationships." arXiv Prepr arXiv240815178. (2024);
37- Zhang S, Fan R, Liu Y, Chen S, Liu Q, Zeng W. "Applications of transformer-based language models in bioinformatics: a survey." Bioinforma Adv.;3(1):vbad001, (2023).
38- Khan A, Rauf Z, Sohail A, Khan AR, Asif H, Asif A, et al. "A survey of the vision transformers and their CNN-transformer based variants." Artif Intell Rev.;56(Suppl 3):2917–70, (2023).
39- Chawla N V, Bowyer KW, Hall LO, Kegelmeyer WP. "SMOTE: synthetic minority over-sampling technique." J Artif Intell Res.;16:321–57, (2002).
40- Wang CW, Kuo CY, Chen CH, Hsieh YH, Su ECY. "Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization." PLoS One.;17(6):e0267554, (2022).
41- Mehrjerd A, Dehghani T, Jajroudi M, Eslami S, Rezaei H, Ghaebi NK. "Ensemble machine learning models for sperm quality evaluation concerning success rate of clinical pregnancy in assisted reproductive techniques." Sci Rep.;14(1):24283, (2024).
42- Tian T, Kong F, Yang R, Long X, Chen L, Li M, et al. "A Bayesian network model for prediction of low or failed fertilization in assisted reproductive technology based on a large clinical real-world data." Reprod Biol Endocrinol.;21(1):8, (2023).
43- Drapkina YS, Makarova NP, Vasiliev RA, Amelin V V, Kalinina EA. "Comparison of predictive models built with different machine learning techniques using the example of predicting the outcome of assisted reproductive technologies." Obstet Gynecol.;(2):97–105, (2024).
44- Peng J, Geng X, Zhao Y, Hou Z, Tian X, Liu X, et al. "Machine learning algorithms in constructing prediction models for assisted reproductive technology (ART) related live birth outcomes." Sci Rep.;14(1):32083, (2024).
2- Kuhnt AK, Passet-Wittig J. Families formed through assisted reproductive technology: Causes, experiences, and consequences in an international context." Vol. 14, Reproductive biomedicine & society online. Elsevier; p. 289–96, (2022).
3- Fang Y, Wu R, Lee JM, Chan LHM, Chan KYJ- "Microfluidic in-vitro fertilization technologies: Transforming the future of human reproduction." TrAC Trends Anal Chem.;160:116959, (2023).
4- Graham ME, Jelin A, Hoon Jr AH, Wilms Floet AM, Levey E, Graham EM. "Assisted reproductive technology: Short‐and long‐term outcomes." Dev Med Child Neurol.;65(1):38–49, (2023).
5- Chiware TM, Vermeulen N, Blondeel K, Farquharson R, Kiarie J, Lundin K, et al. "IVF and other ART in low-and middle-income countries: a systematic landscape analysis." Hum Reprod Update.;27(2):213–28, (2021).
6- Bulletti FM, Sciorio R, Conforti A, De Luca R, Bulletti C, Palagiano A, et al. "Causes of embryo implantation failure: A systematic review and metaanalysis of procedures to increase embryo implantation potential." Front Endocrinol (Lausanne).; 15:1429193, (2025).
7- Ribas‐Maynou J, Yeste M, Becerra‐Tomás N, Aston KI, James ER, Salas‐Huetos A. "Clinical implications of sperm DNA damage in IVF and ICSI: updated systematic review and meta‐analysis." Biol Rev.; 96(4):1284–300, (2021).
8- Amini P, Ramezanali F, Parchehbaf-Kashani M, Maroufizadeh S, Omani-Samani R, Ghaheri A. "Factors associated with in vitro fertilization live birth outcome: a comparison of different classification methods." Int J Fertil Steril.;15(2):128, (2021).
9- Siristatidis C, Stavros S, Drakeley A, Bettocchi S, Pouliakis A, Drakakis P, et al. "Omics and artificial intelligence to improve in vitro fertilization (IVF) success: a proposed protocol." Diagnostics.;11(5):743, (2021).
10- Hew Y, Kutuk D, Duzcu T, Ergun Y, Basar M. "Artificial Intelligence in IVF Laboratories: Elevating Outcomes Through Precision and Efficiency." Biology (Basel).;13(12):988, (2024).
11- Sadegh-Zadeh SA, Khanjani S, Javanmardi S, Bayat B, Naderi Z, Hajiyavand AM. "Catalyzing IVF outcome prediction: exploring advanced machine learning paradigms for enhanced success rate prognostication." Front Artif Intell.;7:1392611, (2024).
12- Hanevik HI, Hessen DO. "IVF and human evolution." Hum Reprod Update. ;28(4):457–79, (2022).
13- Medenica S, Zivanovic D, Batkoska L, Marinelli S, Basile G, Perino A, et al. "The future is coming: artificial intelligence in the treatment of infertility could improve assisted reproduction outcomes—the value of regulatory frameworks." Diagnostics.;12(12):2979, (2022).
14- Cai J, Jiang X, Liu L, Liu Z, Chen J, Chen K, et al. "Pretreatment prediction for IVF outcomes: generalized applicable model or centre-specific model?" Hum Reprod.;39(2):364–73, (2024).
15- AlSaad R, Abd-Alrazaq A, Choucair F, Ahmed A, Aziz S, Sheikh J. "Harnessing artificial intelligence to predict ovarian stimulation outcomes in in vitro fertilization: scoping review." J Med Internet Res.;26:e53396, (2024).
16- Barnett-Itzhaki Z, Elbaz M, Butterman R, Amar D, Amitay M, Racowsky C, et al. Machine learning vs. classic statistics for the prediction of IVF outcomes. J Assist Reprod Genet.;37(10):2405–12, (2020).
17- Chavez Badiola A. "Artificial Intelligence (AI) for Embryo Ranking and its Use in Human Assisted Reproduction." University of Kent; (2024).
18- Liu K, Zhang Y, Martin C, Ma X, Shen B. "Translational bioinformatics for human reproductive biology research: examples, opportunities and challenges for a future reproductive medicine." Int J Mol Sci.;24(1):4, (2022).
19- Zhang Q, Liang X, Chen Z. "A review of artificial intelligence applications in in vitro fertilization." J Assist Reprod Genet.;42(1):3–14, (2025).
20- Hariton E, Pavlovic Z, Fanton M, Jiang VS. "Applications of artificial intelligence in ovarian stimulation: a tool for improving efficiency and outcomes." Fertil Steril.;120(1):8–16, (2023).
21- Khan HL, Khan S, Bhatti S, Abbas S. "Role of artificial intelligence in quality assurance in ART: a review." Fertil Reprod.;5(01):1–7, (2023).
22- Bulletti FM, Berrettini M, Sciorio R, Bulletti C. "Artificial intelligence algorithms for optimizing assisted reproductive technology programs: A systematic review." Glob Transl Med.;2(2):1–12, (2023).
23- Mapari SA, Shrivastava D, Bedi GN, Pradeep U, Gupta A, Kasat PR, et al. "Revolutionizing Reproduction: The Impact of Robotics and Artificial Intelligence (AI) in Assisted Reproductive Technology: A Comprehensive Review." Cureus.;16(6), (2024).
24- Wu YC, Su ECY, Hou JH, Lin CJ, Lin KB, Chen CH. "Artificial intelligence and assisted reproductive technology: A comprehensive systematic review." Taiwan J Obstet Gynecol.;64(1):11–26, (2025).
25- de Santiago I, Polanski L. "Data-driven medicine in the diagnosis and treatment of infertility." J Clin Med.;11(21):6426, (2022).
26- Abdullah KAL, Atazhanova T, Chavez-Badiola A, Shivhare SB. "Automation in ART: paving the way for the future of infertility treatment." Reprod Sci.;30(4):1006–16, (2023).
27- VerMilyea M, Hall JMM, Diakiw SM, Johnston A, Nguyen T, Perugini D, et al. "Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF." Hum Reprod.;35(4):770–84, (2020).
28- Dehghan S, Rabiei R, Choobineh H, Maghooli K, Nazari M, Vahidi-Asl M. "Comparative study of machine learning approaches integrated with genetic algorithm for IVF success prediction." PLoS One.;19(10):e0310829, (2024).
29- Hassan MR, Al-Insaif S, Hossain MI, Kamruzzaman J. "A machine learning approach for prediction of pregnancy outcome following IVF treatment." Neural Comput Appl.;32(7):2283–97, (2020).
30- Yiğit P, Bener A, Karabulut S. "Comparison of machine learning classification techniques to predict implantation success in an IVF treatment cycle." Reprod Biomed Online.;45(5):923–34, (2022).
31- Patil SN, Wali U V, Swamy MK. "Selection of single potential embryo to improve the success rate of implantation in IVF procedure using machine learning techniques." In: 2019 International Conference on Communication and Signal Processing (ICCSP). IEEE; p. 881–6, (2019).
32- Yan K, Zhang D. "Feature selection and analysis on correlated gas sensor data with recursive feature elimination." Sensors Actuators B Chem.; 212:353–63, (2015).
33- Chen X wen, Jeong JC. "Enhanced recursive feature elimination." In: Sixth international conference on machine learning and applications (ICMLA 2007). IEEE;. p. 429–35, (2007).
34- Brijain M, Patel R, Kushik MR, Rana K. "A survey on decision tree algorithm for classification. "(2014);
35- Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. "Random forest: a classification and regression tool for compound classification and QSAR modeling." J Chem Inf Comput Sci.;43(6):1947–58, (2003).
36- Pereira GA, Hussain M. "A review of transformer-based models for computer vision tasks: Capturing global context and spatial relationships." arXiv Prepr arXiv240815178. (2024);
37- Zhang S, Fan R, Liu Y, Chen S, Liu Q, Zeng W. "Applications of transformer-based language models in bioinformatics: a survey." Bioinforma Adv.;3(1):vbad001, (2023).
38- Khan A, Rauf Z, Sohail A, Khan AR, Asif H, Asif A, et al. "A survey of the vision transformers and their CNN-transformer based variants." Artif Intell Rev.;56(Suppl 3):2917–70, (2023).
39- Chawla N V, Bowyer KW, Hall LO, Kegelmeyer WP. "SMOTE: synthetic minority over-sampling technique." J Artif Intell Res.;16:321–57, (2002).
40- Wang CW, Kuo CY, Chen CH, Hsieh YH, Su ECY. "Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization." PLoS One.;17(6):e0267554, (2022).
41- Mehrjerd A, Dehghani T, Jajroudi M, Eslami S, Rezaei H, Ghaebi NK. "Ensemble machine learning models for sperm quality evaluation concerning success rate of clinical pregnancy in assisted reproductive techniques." Sci Rep.;14(1):24283, (2024).
42- Tian T, Kong F, Yang R, Long X, Chen L, Li M, et al. "A Bayesian network model for prediction of low or failed fertilization in assisted reproductive technology based on a large clinical real-world data." Reprod Biol Endocrinol.;21(1):8, (2023).
43- Drapkina YS, Makarova NP, Vasiliev RA, Amelin V V, Kalinina EA. "Comparison of predictive models built with different machine learning techniques using the example of predicting the outcome of assisted reproductive technologies." Obstet Gynecol.;(2):97–105, (2024).
44- Peng J, Geng X, Zhao Y, Hou Z, Tian X, Liu X, et al. "Machine learning algorithms in constructing prediction models for assisted reproductive technology (ART) related live birth outcomes." Sci Rep.;14(1):32083, (2024).
| Files | ||
| Issue | Vol 13 No 2 (2026) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v13i2.21932 | |
| Keywords | ||
| Assisted reproductive technology In vitro fertilization Clinical pregnancy prediction Endometrial receptivity Embryo transfer timing Machine learning Deep learning | ||
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How to Cite
1.
Zargar M, Rezaeijo SM, Najafian M, Shojaei K, Yousefvand V. Deep Learning-Based Prediction of IVF Success: A Transformer Model Approach. Frontiers Biomed Technol. 2026;13(2):327-345.
