Artificial intelligence (AI) has been proposed as a potential tool to help address many of the existing problems related with empirical or subjective assessments of clinical and embryological decision points during the treatment of infertility. AI technologies are reviewed and potential areas of implementation of algorithms are discussed, highlighting the importance of following a proper path for the development and validation of algorithms, including regulatory requirements, and the need for ecosystems containing enough quality data to generate it. As evidenced by the consensus of a group of experts in fertility if properly developed, it is believed that AI algorithms may help practitioners from around the globe to standardize, automate, and improve IVF outcomes for the benefit of patients. Collaboration is required between AI developers and healthcare professionals to make this happen.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
European IVF-Monitoring Consortium (EIM) for the European Society of Human Reproduction and Embryology (ESHRE), Calhaz-Jorge C, et al. Assisted reproductive technology in Europe, 2012: results generated from European registers by ESHRE. Hum Reprod. 2016;31:1638–52.
de Mouzon J, Goossens V, Bhattacharya S, Castilla JA, Ferraretti AP, Korsak V, et al. Assisted reproductive technology in Europe, 2006: results generated from European registers by ESHRE. Hum Reprod. 2010;25:1851–62.
Adamson GD, de Mouzon J, Chambers GM, Zegers-Hochschild F, Mansour R, Ishihara O, et al. International committee for monitoring assisted reproductive technology: world report on assisted reproductive technology, 2011. Fertil Steril. 2018;110:1067–80.
Centres for Disease Control and Prevention. Assisted reproductive technology: ART trends 2002–2011. http://www.cdc.gov/art/ART2011/section5.htm#f43. Accessed 16 Aug 2016.
Curchoe CL, Bormann CL. Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018. J Assist Reprod Genet. 2019;36:591–600.
Letterie GS, MacDonald A. A computerized decision –support system for day to day management of ovarian stimulation cycles during in vitro fertilization. Fertil Steril. 2019;112:e28.
Khosravi P, et al. Robust automated assessment of human blastocyst quality using deep learning. bioRxiv. 2018;394882. https://doi.org/10.1101/394882.
Hoo-Chang S, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imag. 2016;35:1285–98.
Turing AM. On computable numbers, with an application to the entscheidungsproblem. a correction. Proc Lond Math Soc. 1938;s2-43:544–6.
Turing AM. Computing machinery and intelligence. Mind. 1950;236:433–60.
Mcculloch W, Pitts W. A logical calculus of the ideas immanent in nerous activity (reprinted from 1943). Bull Math Biol. 1990;52:99–115.
Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks. Handb. Approx. Algorithms Metaheuristics. 2007;45-1–45–16. https://doi.org/10.1201/9781420010749.
The AI effect. How artificial intelligence is making health care more human. MIT Technology reviews insights. 2019.
Tran D, Cooke S, Illingworth PJ, Gardner DK. Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer. Hum Reprod. 2019;34:1011–8.
Shen D, Wu G, Suk H. Deep learning in medical image analysis. Physiol Behav. 2017;176:139–48.
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44–56.
Titano JJ, Badgeley M, Schefflein J, Pain M, Su A, Cai M, et al. Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat Med. 2018;24:1337–41.
Arbabshirani MR, Fornwalt BK, Mongelluzzo GJ, Suever JD, Geise BD, Patel AA, et al. Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical work flow integration. NPJ Digit Med. 2017;1:9. https://doi.org/10.1038/s41746-017-0015-z.
Chilamkurthy S, et al. Articles Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet. 2018;6736:1–9.
Nam JG, Park S, Hwang EJ, Lee JH. Development and validation of deep learning – based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology. 2019;290:218–28.
Singh R, et al. Deep learning in chest radiography: detection of findings and presence of change. PLoS ONE. 2018;13(13):1–12.
Lehman CD, et al. Mammographic breast density assessment using deep learning: clinical implementation. Radiology. 2018;00:1–7.
Lindsey R, Daluiski A, Chopra S, Lachapelle A, Mozer M, Sicular S, et al. Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci USA. 2018;115:11591–6.
Bejnordi BE, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017;318:2199–210.
Coudray N, et al. images using deep learning. Nat Med. 2018;24:1559–69.
Capper D, Jones DTW, Sill M, Hovestadt V, Schrimpf D, Sturm D, et al. DNA methylation-based classification of central nervous system tumours. Nature. 2018;555:469–74.
Steiner DF, MacDonald R, Liu Y, Truszkowski P, Hipp JD, Gammage C, et al. Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer. Am J Surg Pathol. 2018;42:1636–46.
Liu Y, Kohlberger T, Norouzi M, Dahl GE, Smith JL, Mohtashamian A, et al. Artificial intelligence – based breast cancer Nodal. Arch Pathol Lab Med. 2019;143:859–68.
Esteva A, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nat Publ Group. 2017;542:115–8.
Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018;29:1836–42.
Han SS, Kim MS, Lim W, Park GH, Park I. Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. J Invest Dermatol. 2018;138:1529–38.
Gulshan V, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;94043:1–9.
Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018. https://doi.org/10.1038/s41746-018-0040-6.
Kanagasingam Y, Xiao D, Vignarajan J, Preetham A, Mehrotra A. Evaluation of artificial intelligence – based grading of diabetic retinopathy in primary care. JAMA. 2018;1:1–6.
Long E, et al. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nat Biomed Eng. 2017;0024:1–8.
De Fauw J, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018;24:1342–54.
Burlina PM, Joshi N, Pekala M, Pacheco KD, Freund DE, Bressler NM. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol. 2017;135:1170–6.
Brown JM, Campbell JP, Beers A, Chang K, Ostmo S, Chan RVP, et al. Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA Ophthalmol. 2018;136:803–10.
Kermany DS, Goldbaum M, Cai W, Lewis MA. Identifying medical diagnoses and treatable diseases by image-based deep learning resource. Cell. 2018;172:1122–1131.e1129.
Mori Y, Kudo SE, Misawa M, Saito Y, Ikematsu H, Hotta K, et al. Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy. Ann Intern Med. 2018;169:357–66.
Wang P, Xiao X, Glissen Brown JR, Berzin TM, Tu M, Xiong F, et al. Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nat Biomed Eng. 2018;2:741–8.
Madani A, Arnaout R, Mofrad M. Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit Med. 2018;1–8. https://doi.org/10.1038/s41746-017-0013-1.
Zhang J, Gajjala S, Agrawal P, Tison GH, Hallock LA, Beussink-Nelson L, et al. Fully automated echocardiogram interpretation in clinical practice feasibility and diagnostic accuracy. Circulation. 2018;138:1623–35.
Fujisawa Y, Otomo Y, Ogata Y, Nakamura Y, Fujita R, Ishitsuka Y, et al. Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis. Br J Dermatol. 2019;180:373–81.
Celi LA, Csete M, Stone D. Optimal data systems: the future of clinical predictions and decision support. Curr Opin Crit Care. 2014;20:573–80.
Hee, K. Is data quality enough for a clinical decision?: apply machine learning and avoid bias. Proc. - 2017 IEEE Int. Conf. Big Data, Big Data 2017 2018-Janua, 2612–2619. 2017.
MacKay DJC. Information theory, inference, and learning algorithms. Cambridge University Press 2003; 2005. https://doi.org/10.1166/asl.2012.3830.
Bellman RE. Dynamic Programming: Princeton University Press; 2010.
Cho J et al. How much data is needed to train a medical image deep learning system to achieve neces-sary high accuracy. Conf Pap ICLR 2016 HOW. 2016.
Hestness J et al. Deep learning scaling is predictable, empirically. arXiv:1712.00409. 2017.
Hagemann BR, Leclerc J. Precision regulation for artificial intelligence. IBM Policy Lab 1–5.
Fertility AI Forum Group: Gerard Letterie, Integramed; Pascual Sánchez, Ginemed; Geoff Trew, The Fertility Partnership; Jason Swain, CCRM Management Co.; Marcos Meseguer, IVIRMA; Dan Nayot, Trio Fertility; Alison Campbell, CARE; Ian Huang, Storck–Binflux; Jan Choma, Cognexa; Kevin Loewke, DANA; María Paola Piqueras, Ginemed; Paul Nader, Baby Sentry; Michael Schindler, Meditex; Marck Marcom, Ideas EMR; Ed Vom, Planet Innovation; Eleanora Lippolis, Merck; Sebastian Bohl, Merck, Jan Kirsten, Merck; Daniel Abshagen, Merck; Diego Ezcurra, Merck.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Swain, J., VerMilyea, M.T., Meseguer, M. et al. AI in the treatment of fertility: key considerations. J Assist Reprod Genet 37, 2817–2824 (2020). https://doi.org/10.1007/s10815-020-01950-z