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ML in Reproductive
Science:
human embryo
selection and beyond
Oleksii Barash, PhD
IVF Laboratory Research Director
Reprodu...
What is infertility?
Scope of the problem
• Infertility affects 12% of the reproductive age population in the US (≈12
mill...
IVF is essentially manufacturing
• Complex multidimensional process;
• Constant intake flow of the patients;
• Cutting edg...
What has changed?
Why we start using ML in Reproductive Science?
Data is too large to handle it manually
• Wide Electronic Medical Records
adoption (2004 - 2015);
• IoT devices – sensors,...
Life in vitro – up to 6-7 days
• From 0 to 30+ embryos per IVF cycle (≈15 000 embryos per year at RSC)
• Many features per...
Non-invasive imaging and predictions
• Xtend algorithm:
• over 1,000 combinations of potential parameters
• includes egg a...
EEVA Xtend algorithm
Preimplantation Genetic Testing
SNP array / Next Gen Sequencing
DNA flow cell
Live birth rate
Embryo
_Age
Blastula
tion_ra
te
Donor_
eggs Euploid
y_rate
Numbe
r_of_no
rmal
d5_to_t
otal_rat
io Total_d
...
What if we can evaluate
ALL available factors?
What if we can assess ALL available factors?
20 factors:
202 = 400 plots
381 factors
3812 = 145,161 plots
20 x 20
Machine ...
Lab + Clinical factors, 11k embryos, >2000 patients
Pregnant, %Non-Pregnant, %
% of total SETs
Presented by RSC team at AS...
Relevant feature selection algorithm*
(Lab + Clinical)
*Number of CART trees = 100
Building the model to predict IVF outcome
Only weak predictors are present
Relatively small sample size
A lot of features ...
Building the model to predict IVF outcome
• Benchmark AUC – Starting point
• Feature engineering
• Feature importance
• Fe...
ReproScore (the probability of positive outcome )
Patient
Name
Embryo
Morphology
Genetics Reproscore FET date
Patient A 3A...
What lies beyond?
Personalized decisions
• Where I am:
• Can I have a baby (age, medical history, genetic profile)?
• What...
Life in vitro… More data?
4 weeks!
3D embryo models
Conclusion
1. Machine learning is not yet widely used in clinical practice
2. Augmented decision making with machine learn...
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Oleksii Barash, Reproductive Science Center of Bay Area - Machine Learning in Reproductive Science

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This session was recorded in San Francisco on February 9th, 2019 and can be viewed here: https://youtu.be/dE2ntPX9WeQ

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.

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.

Veröffentlicht in: Technologie
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Oleksii Barash, Reproductive Science Center of Bay Area - Machine Learning in Reproductive Science

  1. 1. ML in Reproductive Science: human embryo selection and beyond Oleksii Barash, PhD IVF Laboratory Research Director Reproductive Science Center of SF Bay Area @oleksii.barash #H2OWORLD
  2. 2. What is infertility? 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
  3. 3. 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 is single healthy baby
  4. 4. What has changed? Why we start using ML in Reproductive Science?
  5. 5. 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
  6. 6. Life in vitro – up to 6-7 days • From 0 to 30+ embryos per IVF cycle (≈15 000 embryos per year at RSC) • Many features per embryo • Critical choice – no second chance
  7. 7. Non-invasive imaging and predictions • 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
  8. 8. EEVA Xtend algorithm
  9. 9. Preimplantation Genetic Testing SNP array / Next Gen Sequencing DNA flow cell
  10. 10. Live birth rate Embryo _Age Blastula tion_ra te Donor_ eggs Euploid y_rate Numbe r_of_no rmal d5_to_t otal_rat io Total_d ay_5_b x Total_d ay_6_b x Total_f or_bios y Bx_Day Emb_Ex pansion ICM TE Gender Best_E mbryo_ For_ET Elective _SET Cycle_n umber Numbe r_of_Fo llicles Zygotes Fert_ra te Unfert M2 M1 GV ATR Multi_P N PN_1 Degene rated Cleaved Cleavag e_rate Numbe r_ext_c ultureGood_e xt_cult ureNumbe r_to_blNumbe r_CryoGood_d 3_rateTVA_M D Numbe r_of_ta rnsfers _to_del ivery Semen _Sourc e 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 PRIORI VF PRIORF ET PRIORI UI HEIGHT WEIGH T PRIMA RYDIAG NOSIS SEMEN SOURC E FSHLEV EL NEARES T_AMH MED1 Peak_E 2 TOTALI US FOLLICL ES_BIG GER_T HAN_1 4 ASPIRA TED_O OCYTES NO_FR OZEN NO_VIT INITIAL CONSU LT_PRE M INITIAL CONSU LT_GRA VIDITY INITIAL CONSU LT_SAB INITIAL CONSU LT_TER M INITIAL CONSU LT_BIO CHEMI CAL Stim protoco l Factors affecting clinical outcomes More factors? Bias? Reproducibility? 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
  11. 11. What if we can evaluate ALL available factors?
  12. 12. What if we can assess ALL available factors? 20 factors: 202 = 400 plots 381 factors 3812 = 145,161 plots 20 x 20 Machine Learning
  13. 13. Lab + Clinical factors, 11k embryos, >2000 patients Pregnant, %Non-Pregnant, % % of total SETs Presented by RSC team at ASRM 2017 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 patient:
  14. 14. Relevant feature selection algorithm* (Lab + Clinical) *Number of CART trees = 100
  15. 15. Building the model to predict IVF outcome Only weak predictors are present Relatively small sample size A lot of features (>300) Accuracy of predictions = 0.8412 AUC = 0.8236
  16. 16. Building the model to predict IVF outcome • Benchmark AUC – Starting point • Feature engineering • Feature importance • Feature transformations • Non-important features • Model interpretation • Time – series
  17. 17. ReproScore (the probability of positive outcome ) Patient Name Embryo Morphology Genetics Reproscore FET date Patient 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 Patient 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 0 100 200 300 400 500 600 0.1-0.2 0.2-0.3 0.3-0.4 0.4-0.5 0.5-0.6 0.6-0.7 0.7-0.8 0.8-0.9 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Numberofpatients Predicted probability of Positive outcome ActualclinicalPR,% Actual clinical PR, % Number of patients
  18. 18. What lies beyond? Personalized decisions • 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
  19. 19. Life in vitro… More data? 4 weeks! 3D embryo models
  20. 20. 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 4. Transition from knowledge driven to data driven care 5. This is a personal revolution as much as analytical

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