Machine Learning in Reproductive Science: Human Embryo Selection and Beyond
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This webinar was recorded on July 16th, 2018 in San Francisco Bay Area. On-demand video can be viewed here: https://www.brighttalk.com/webcast/16463/330005 Description: In this talk, Oleksii Barash PhD, IVF Laboratory Research Director at the Reproductive Science Center of the San Francisco Bay Area, will discuss his team’s approach to applying machine learning for decision making during infertility treatment. Oleksii will also give a quick overview of how he uses Driverless AI to build models for predicting IVF outcomes and select the best embryo for embryo transfer. Speaker's Bio: Oleksii believes that evidence-based clinical decisions will greatly improve the efficiency and safety of the medicine. He received his Master degree in Clinical Embryology from University of Leeds (UK) and PhD in Cell Biology. The ultimate goal of his findings is to essentially transform medical records into medical knowledge.
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Machine Learning in Reproductive Science: Human Embryo Selection and Beyond
- 1. Machine Learning in Reproductive Science: human embryo selection and beyond Oleksii Barash, Ph.D. Reproductive Science Center of San Francisco Bay Area
- 2. Disclosure We have no financial relationship with any commercial interest related to the content of this activity
- 3. What is infertility? WHO - Infertility definitions and terminology • Failure to conceive within 12 months of regular unprotected intercourse. • Primary or secondary. • 84% of couples will conceive within 1 year and 92% within 2 years.
- 4. Scope of the problem • Infertility affects 12% of the reproductive age population in the US (≈12 million people) • Infertility affects men and women equally • More than 50% of infertility patients will have a baby with IVF (In Vitro Fertilization) treatment • Over 1.5M IVF cycles per year worldwide (≈ 200,000 in USA) in 2014 • Cost of one IVF cycle in US: $10K – $100K • Global IVF market $30-40bn
- 6. IVF is essentially manufacturing • Complex multidimensional process; • Constant intake flow of the patients; • Cutting edge labor and equipment; • Hundreds of contributing factors (Lab + Clinical); • Every patient is unique – limited standardization Ultimate goal – single healthy baby
- 7. Life in vitro – up to 6-7 days
- 8. What has changed? Why we start using ML in Reproductive Science?
- 9. Data is too large to handle it manually • Wide Electronic Medical Records adoption (2004 - 2015); • IoT devices – sensors, incubators, microscopes, lasers • Morpho-kinetics (time-lapse) • Preimplantation Genetic Testing • “Omics” era is coming
- 10. Embryo selection for the transfer • From 1 to 30+ embryos per IVF cycle (≈15 000 embryos per year) • Many morphological and kinetic features per embryo • Critical choice – no second chance
- 11. Risks of Twin compared to Singleton Pregnancy Risk Single Twin Premature delivery (8 mos) 11% 58% Very Premature delivery (7 mos) 2% 12% Birth weight < 3.5 pounds 1% 10% Cerebral Palsy 1.6/1000 7.3/1000 Infant Mortality 6/1000 29/1000
- 12. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2010 2011 2012 2013 2014 2015 2016 %ofcycles ETx1 ETx2 ETx3 ETx4 ETx5 ～ Average age – 36.0 ± 5.5 y.o. ～ 39.3% of all patients are over 38 y.o. SET rate in non-PGT cycles (2010-2016), fresh D5 ET, N=3925
- 13. Traditional embryo evaluation M. Montag, 2014
- 14. Time-lapse monitoring M. Montag, 2014
- 15. Non-invasive imaging and predictions
- 16. EEVA (Early Embryo Viability Assessment) • Xtend algorithm: – over 1,000 combinations of potential parameters – includes egg age, cell count and Post P3 analysis – which measures cell activity after the four cell stage – Post P3 is the result of a proprietary analysis based on 74 computer-based attributes that are combined into one parameter – each embryo gets a developmental potential score ranging from 1 (highest) to 5 (lowest). – 84% specificity vs 52% by traditional assessment – The odds ratio of predicting blastocyst formation is 2.57 vs 1.67 by traditional assessment
- 19. 3D embryo models
- 21. Preimplantation Genetic Testing National Human Genome Research Institute, 2017 • Log scale!
- 22. Preimplantation Genetic Testing Handyside, 2015 DNA sequencing DNA flow cell
- 23. Preimplantation Genetic Testing (PGT) at RSC ～ SET frequency in PGS IVF cycles (average age – 37.5 ± 4.29 y.o. ) – 89.9% FISH SNP – aCGH - NGS 661 1387 4 735 0 200 400 600 800 1000 1200 1400 0 200 400 600 800 1000 1200 1400 1600 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 NumberofIVFcycles Total volume PGT cases 78
- 24. Ongoing clinical pregnancy rates after single euploid embryo transfer, N = 1108 FETS O. Barash, K.A. Ivani, S.P. Willman, C. MacKenzie, S.C. Lefko, L.N. Weckstein. Clinical Pregnancy Rate after Single Euploid Embryo Transfer is Age Independent. Fertility and Sterility, Vol. 103, Issue 2, e10 y = -0.4883x + 64.691 0 10 20 30 40 50 60 70 80 90 DONORS ≤34 35-37 38-40 ≥41 Ongoingpregnancyrate,% Maternal age Ongoing PR, %
- 25. Live birth rate Maternal age Number of embryos for biopsy Morphology of the embryos SET vs eSET D5 vs D6 Biopsy Total gonadotropin dosage Number of previous failed cycles Number of normal embryos per cycle Number of eggs Euploidy rate Presented by RSC team: ASRM 2016, 2015, 2014; ESHRE 2015, 2016; PCRS 2014, 2015, 2016; PGDIS 2015, 2017 Factors affecting PGT outcomes
- 26. Live birth rate Embryo _Age Blastula tion_rat e Donor_ eggs Euploid y_rate Number _of_nor mal d5_to_t otal_rat io Total_d ay_5_bx Total_d ay_6_bx Total_fo r_biosy Bx_Day Emb_Ex pansion ICM TE Gender Best_E mbryo_ For_ET Elective _SET Cycle_n umber Number _of_Foll icles Zygotes Fert_rat e Unfert M2 M1 GV ATR Multi_P N PN_1 Degene rated Cleaved Cleavag e_rate Number _ext_cu ltureGood_e xt_cultu reNumber _to_blNumber _CryoGood_d 3_rateTVA_M D Number _of_tar nsfers_t o_deliv ery Semen_ Source Fresh_F rosen_s p BMI PATIEN TTYPET EXT NO_OF _DAYS SUMSTI M ASPIRA TED_O OCYTES HCG_D RUG TOTAL2 PN GRAVID ITY PREM TERM SAB BIOCHE MICAL LIFETIM E_SMO KED PRIORIV F PRIORF ET PRIORI UI HEIGHT WEIGHT PRIMAR YDIAGN OSIS SEMENS OURCE FSHLEV EL NEARES T_AMH MED1 Peak_E 2 TOTALI US FOLLICL ES_BIG GER_TH AN_14 ASPIRA TED_O OCYTES NO_FR OZEN NO_VIT INITIAL CONSUL T_PREM INITIAL CONSUL T_GRAV IDITY INITIAL CONSUL T_SAB INITIAL CONSUL T_TERM INITIAL CONSUL T_BIOC HEMICA L Stim protoco l Factors affecting PGT outcomes More factors? Bias? Reproducibility of the results?
- 27. What if we can evaluate ALL available factors?
- 28. What if we can assess ALL available factors? 20 factors: 202 = 400 plots 381 factors 3812 = 145,161 plots 20 x 20 Machine Learning
- 29. Algorithm Timeframe: Jan 2013 – Jul 2017 Retrospective analysis Number of PGS transfers: 918 Average age: 35.6 ± 4.8 ONLY Single embryo transfers Machine learning methods: • GLM (Generalized Linear Models) • RPART (Classification and Regression Trees) • GBM (Generalized Boosted Regression Models) IVF lab Embryo_Age Blastulation_rate Donor_eggs Euploidy_rate Number_of_normal d5_to_total_ratio Total_day_5_bx Total_day_6_bx Total_for_biopsy Bx_Day Embryo_Morphology Expansion ICM TE Gender Clinical_Outcome BEST_ EMBRYO_FOR_ET ELECTIVE_SET Number_of_tarnsfers_to_delivery Biopsy tech CYCLE # PEAK E2 TVA MD TVA TECH # Follicles >12 mm # EGGS # INSEM # 2PN % FERT # UNFERT #M2 or mature # INT # IMM # ATR # > 2PN # 1PN # DEG FERT CK TECH ICSI TECH SEMEN SOURCE FRESH/FROZEN SP CLEAVED % CLEAVED HATCH TECH # EXT CULTURE # GOOD EXT CULT # TO BLAST # CRYO % OF GOOD QUALITY EMBRYOS … clinical BMI PRIMARY_DX PATIENTTYPETEXT LUPRON STIM GNRHA MED1 SUMSTIM TRANSFER_DATE HCG_DRUG GRAVIDITY PREM TERM SAB BIOCHEMICAL PATIENTRACE LIFETIME_SMOKED SMOKING_FREQ PRIORIVF PRIORFET PRIORIUI HEIGHT WEIGHT STIMPROTOCOL LUPRONPROTOCOL PRIMARYDIAGNOSIS SECONDARYDIAGNOSIS TERTIARYDIAGNOSIS SEMENSOURCE PATIENTTYPE FSHLEVEL E2LEVEL NEAREST_AMH AFC MED1 MED2 MED3 MED4 MAX_E2 TOTALIUS FERT_METHOD_ICSI FERT_METHOD_IVF INITIALCONSULT_PREM INITIALCONSULT_GRAVIDITY INITIALCONSULT_SAB INITIALCONSULT_TERM INITIALCONSULT_BIOCHEMICAL Stim protocol … 320 variables per SET:
- 30. Lab + Clinical, 918 SETs Pregnant, %Non-Pregnant, % % of total SETs Presented by RSC team at ASRM 2017
- 31. Relevant feature selection algorithm* (Lab + Clinical) *Number of CART trees = 100
- 32. Building the model to predict IVF outcome Only weak predictors are present Relatively small sample size (10K) A lot of features (>300) Accuracy of predictions = 0.8412 AUC = 0.8236
- 33. Building the model to predict IVF outcome (PGT only) • Benchmark AUC – Starting point • Feature engineering • Feature importance • Feature transformations • Non-important features • Model interpretation • Time – series
- 34. Building the model to predict IVF outcome (FETs only) Relative Importance Feature Description 0.95784 403_NumCatTE_Prior full term_Prior pre-term_TE_0 Out-of-fold mean of the response grouped by: ['Prior full term', 'Prior pre-term', 'TE'] using 5 folds (numeric columns are bucketed into 25 equally populated bins) 0.55907 164_CV_TE_# EXT CULTURE_FACNAME_LUPRON_ PGD.1_Retrieval MD_Retrieval technician_Thawing technician_0 Out-of-fold mean of the response grouped by: ['# EXT CULTURE', 'FACNAME', 'LUPRON', 'PGD.1', 'Retrieval MD', 'Retrieval technician', 'Thawing technician'] using 5 folds 0.35233 217_BIOCHEMICAL BIOCHEMICAL (original)
- 35. GLM model to confirm Presented by RSC team at ESHRE 2018 Coeff. = coefficient expressed in logits; CI = 95% confidence interval for the odds ratio; AIC: 1397.3 Number of Fisher Scoring iterations: 4 Table I. Multiple logistic regression analysis of the association between history of previous biochemical pregnancies, blastocyst morphology, biopsy day and clinical PR in IVF PGS cycles with single embryo transfer. Coeff. Std. Error p Value Odds Ratio CI Lower Limit CI Upper Limit (Intercept) 3.530 0.957 2.25E-04 34.126 5.259 224.449 Prior. Biochem -0.507 0.073 4.33E-12 0.602 0.520 0.693 Embryo Morph. 0.723 0.266 6.67E-03 2.060 1.223 3.482 Biopsy day -0.523 0.135 1.04E-04 0.592 0.455 0.772
- 36. Accuracy of the model – 0.8412 Presented by RSC team at ESHRE 2018 • The probability of positive clinical outcome was calculated for each embryo (8429 embryos • Probability ranged from 0.1062 to 0.9355 (baseline prediction – 0.63) • Best cut off – 0.548
- 37. ReproScore Patient Name Embryo Morphology Genetics Reproscore FET date A 3AA Euploid 0.692727 12/17/2017 3AB 45, XX; Monosomy 7 0.692415 5B-B- 47, XY; Tri/polysomy 16 0.648626 5BB Euploid 0.674588 6/4/2015 2B-B- 47, XY; Tri/polysomy 9 0.647992 5B-B 47, XY; Tri/polysomy 6 0.666277 B 2BB Euploid 0.407558 5/18/2018 5AA 47, XY; Tri/polysomy 16 0.372037 5AB Euploid 0.364438 3AB Euploid 0.364438 6/6/2017
- 38. ReproScore Viable embryos Morphologically normal embryos Genetically normal embryos Highest ReproScore Culture conditions Time-lapse Next Generation Sequencing Model Ensambling/predictions
- 39. The current problem with the models: A vs B
- 41. Personalized decisions to be made in each IVF cycle • Where I am: – Can I have a baby (age, medical history, genetic profile)? – What are my chances? – Can I afford it? • How to choose treatment plan: – Hormonal Stimulation protocol / dosage / duration – Lutheal support, etc… • How many embryos to transfer (1, 2 or 3) • Which embryo to transfer: – Morphological screening – Genetic screening – Gender
- 42. Univfy Univfy algorithm: • Takes patient data • Predictive model based on 13,000 IVF cycles; • Chances for positive outcome • Chances of twins if 2 embryos were transferred
- 43. Celmatix Celmatix algorithm: • Incorporated in our EMR (ARTworks) • Software as a service (SaaS model) • Data analytics platform to help optimize patient management and counseling
- 44. Endometrial Receptivity Analysis (ERA) by Igenomix Patented in 2009: PCT/ES 2009/000386 Customized microarray (238 genes) Bioinformatic analysis of data obtained by the customized microarray Classification and prediction from gene expression.
- 45. Endometrial Receptivity Analysis (ERA) Receptive Model Classifies the Molecular Receptivity Status of the Endometrium Post-ReceptivePre-Receptive
- 46. Life in vitro… 4 weeks!
- 47. Conclusion 1. Machine learning is not yet widely used in clinical practice 2. Augmented decision making with machine learning 3. Auto ML for rapid experimentation knowledge discovery
- 48. Thank you! Lab: K. A. Ivani, Ph.D. O. O. Barash, Ph.D. N. Huen S. C. Lefko C. MacKenzie J. Ciolkosz E. Homen E. Jaramillo MDs: L. N. Weckstein S. P. Willman M. R. Hinckley D. S. Wachs E. M. Rosenbluth S. P. Reid M. V. Homer E. I. Lewis