Page 28 - AIH-2-3
P. 28

Artificial Intelligence in Health                                           AI in embryo selection for ART



               Technol. 2024;4:276-279.                           Methods Mol Biol. 2014;1154:171-231.
               doi: 10.48175/IJARSCT-19939                        doi: 10.1007/978-1-4939-0659-8_8
            75.  Lin J, Sun XX. Predictive modeling in reproductive   86.  Prostate pathophysiology. In:  Atlas  of  Clinical  Andrology.
               medicine. Reprod Dev Med. 2018;2(4):224-229.       United States: CRC Press; 2005. p. 146-157.
               doi: 10.4103/2096-2924.249888                      doi: 10.1201/b14619-17
            76.  Rosenwaks Z. Artificial intelligence in reproductive   87.  Kragh MF, Karstoft H. Embryo selection with artificial
               medicine: A fleeting concept or the wave of the future? Fertil   intelligence: How to evaluate and compare methods? J Assist
               Steril. 2020;114(5):905-907.                       Reprod Genet. 2021;38(7):1675-1689.
               doi: 10.1016/j.fertnstert.2020.10.002              doi: 10.1007/s10815-021-02254-6
            77.  Raimundo J, Cabrita P. Artificial intelligence at   88.  Merican ZZ, Yusof  UK, Abdullah  NL.  Review on Embryo
               assisted reproductive technology.  Procedia Comput Sci.   Selection Based on Morphology Using Machine Learning
               2021;181:442-447.                                  Methods; 2021. Available from: https://api.semanticscholar.
                                                                  org/corpusid:236880800 [Last accessed on 2025 Apr 28].
               doi: 10.1016/j.procs.2021.01.189
                                                               89.  Medenica S, Zivanovic D, Batkoska L, et al. The future is
            78.  Dimitriadis I, Zaninovic N, Badiola AC, Bormann CL.
               Artificial intelligence in the embryology laboratory:   coming: Artificial intelligence in the treatment of infertility
               A review. Reprod Biomed Online. 2022;44(3):435-448.  could improve assisted reproduction outcomes-the value of
                                                                  regulatory frameworks. Diagnostics. 2022;12(12):2979.
               doi: 10.1016/j.rbmo.2021.11.003
                                                                  doi: 10.3390/diagnostics12122979
            79.  Afnan MAM, Liu Y, Conitzer V,  et al. Interpretable, not   90.  Curchoe  CL.  Meetings  that matter: Time to  put artificial
               black-box, artificial intelligence should be used for embryo   intelligence  on  the  ART  roadmap.  J  Assist Reprod Genet.
               selection. Hum Reprod Open. 2021;2021(4):hoab040.
                                                                  2022;39(7):1493-1496.
               doi: 10.1093/hropen/hoab040
                                                                  doi: 10.1007/s10815-022-02520-1
            80.  Benchaib M, Labrune E, Giscard d’Estaing S, Salle B,
               Lornage J. Shallow artificial networks with morphokinetic   91.  Tran HP, Tran LNH, Dang HT, et al. A SWOT analysis of
                                                                  human- and machine learning- based embryo assessment.
               time‐lapse parameters coupled to ART data allow to predict   IEEE Access. 2020;8:227466-227481.
               live birth. Reprod Med Biol. 2022;21(1):e12486.
                                                                  doi: 10.1109/ACCESS.2020.3045772
               doi: 10.1002/rmb2.12486
                                                               92.  Kuo C, Zuo J, Han W, et al. Intelligent Assisted Reproduction:
            81.  Yu L, Lam KKW, Ng EHY,  et al.  Deep Learning-Based   Innovative Applications of artificial Intelligence in Embryo
               Embryo Assessment of Static Images can Reduce the Time to
               Live Birth in In Vitro Fertilization. medRxiv [Preprint]; 2024.  Health Assessment. Authorea [Preprints]; 2025.
                                                                  doi: 10.22541/au.173639046.67662628/v1
               doi: 10.1101/2024.10.28.24316259
                                                               93.  Presacan O, Dorobanțiu A, Thambawita V, et al. Embryo 2.0:
            82.  Liao Z, Yan C, Wang J, et al. A clinical consensus-compliant   Merging Synthetic and Real Data for Advanced AI Predictions
               deep  learning  approach  to quantitatively  evaluate  human   [Preprint]; 2024.
               in  vitro fertilization early embryonic development with
               optical microscope images. Artif Intell Med. 2024;149:102773.     doi: 10.48550/arxiv.2412.01255
               doi: 10.1016/j.artmed.2024.102773               94.  Kaveh S, Ghafari A, Khedri Z, et al. Investigating the Artificial
                                                                  Intelligence in Prediction and  Evaluation of Sperm and
            83.  Popa T, He C, Vasconcelos F, et al. P-168  Both artificial   Embryo Quality in In Vitro Fertilization (IVF): A Systematic
               intelligence and manual embryo selection methods show   Review. [Preprint (Version 1)]; 2024. [Last accessed on 2025
               sex-bias, favouring male embryos-insights from the largest   Apr 28].
               embryo sex study using time-lapse and PGT-A.  Hum
               Reprod. 2024;39(Suppl 1):deae108.539.              doi: 10.21203/rs.3.rs-5504223/v1
               doi: 10.1093/humrep/deae108.539                 95.  Embryo  Ranking  Intelligent  Classification  Algorithm.  C.  to,
                                                                  “Embryo Ranking Intelligent Classification Algorithm; 2020.
            84.  Illingworth PJ, Venetis C, Gardner DK, et al. Deep learning   Available from: https://en.wikipedia.org/wiki/embryo_
               versus manual morphology-based embryo selection in IVF:   ranking_intelligent_classification_algorithm? [Last accessed
               A randomized, double-blind noninferiority trial. Nat Med.   on 2025 Feb 19].
               2024;30(11):3114-3120.
                                                               96.  Leahy B, Jang WD, Yang H, et al. Automated Measurements
               doi: 10.1038/s41591-024-03166-5
                                                                  of Key Morphological Features of Human Embryos for IVF.
            85.  Huang JYJ, Rosenwaks Z. Assisted reproductive techniques.   Med Image Comput Comput Assist Interv. 2020:12265:25-35.


            Volume 2 Issue 3 (2025)                         22                        https://doi.org/10.36922/aih.7170
   23   24   25   26   27   28   29   30   31   32   33