Predicting a live birth by artificial intelligence incorporating both the blastocyst image and conventional embryo evaluation parameters

Table 1 The morphological features and clinical information of 5691 blastocysts and the univariate regression formulas[13] of the independent factors for predicting the probability of live birth

Independent factors	mean ± SD	Median	Minimum	Maximum	Formulas	Coefficients
Age	35.75 ± 4.78	36	20	48	k/[1 + Exp (β0 + β1x)]	β0 = -10.742 ± 4.106 (P = 0.0089); β1 = 0.284 ± 0.109 (P = 0.0088); K = 0.451
Number of embryo transfers procedures in the past	2.75 ± 2.36	2	1	30	1/[1 + Exp (β0 + β1x)]	β0 = 0.635 ± 1.158 (P = 0.584); β1 = 0.156 ± 0.123 (P = 0.204)
Anti-Müllerian hormone concentration (ng/mL)	3.91 ± 3.54	2.94	0.0	32.2	1/[1 + Exp (β0 + β1x)]	β0 = 1.282 ± 2.640 (P = 0.627); β1 = 0.062 ± 0.139 (P = 0.678)
Day-3 blastomere number	8.03 ± 1.74	8	2	17	k/(2πσ2)1/2 Exp (-(x-m)2/(2σ2))	σ = 4.668 ± 0.773 (P = 4.179 × 10-5); m = 11.624 ± 0.663 (P = 1.969 × 10-10); K = 4.643 ± 0.611 (P = 3.91 × 10-6)
Grade on day 3 (Class A = 1, B = 2, C = 3, D = 4)	1.87 ± 0.57	2	1	4	k/[1 + Exp (β0 + β1x)]	β0 = -7.967 ± 8.012 (P = 0.320); β1 = 2.584 ± 2.582 (P = 0.317); K = 0.319
Embryo cryopreservation day (Day 5 = 1, Day 6 = 2)	1.20 ± 0.40	1	1	2	β0 + β1x	β0 = 0.435; β1 = -0.131
Inner cell mass (A = 1, B = 2, C = 3)	1.58 ± 0.55	2	1	3	β0 + β1x	β0 = 0.479 ± 0.037 (P = 0.049); β1 = -0.131 ± 0.017 (P = 0.083)
Trophectoderm (A = 1, B = 2, C = 3)	2.01 ± 0.75	2	1	3	β0 + β1x	β0 = 0.526 ± 0.002 (P = 0.0026); β1 = -0.124 ± 0.001 (P = 0.005)
Average diameter (µm)	154.77 ± 24.12	153.8	81.3	242.5	1/[1 + Exp (β0 + β1x)]	β0 = 2.623± 5.312 (P = 0.621); β1 = -0.011 ± 0.030 (P = 0.723)
Body mass index (kg/m2)	21.30 ± 3.16	20.6	13.9	43.3	1/[1 + Exp (β0 + β1x)]	β0 = -0.631± 0.844 (P = 0.454); β1 = 0.079 ± 0.035 (P = 0.026)

Each formula was determined to fit the data distribution. Coefficients are shown as the mean ± SE.

Table 2 Discrimination ability of the best classifier of the original neural network architecture comparing the combination method[13]

Patient age (yr)	Accuracy	Sensitivity	Specificity	PPV	NPV	AUC	95%CI of the AUC	Cut-point
AI in this study
All ages	0.743	0.638	0.789	0.573	0.831	0.740	0.681-0.801	0.207
The combination method[13]
All ages	0.721	0.779	0.704	0.400	0.885	0.773	0.655-0.888	0.213
< 35	0.616	0.652	0.592	0.515	0.719	0.655	0.600-0.707	0.388
35-37	0.671	0.786	0.612	0.508	0.849	0.723	0.653-0.793	0.281
38-39	0.732	0.758	0.725	0.455	0.908	0.791	0.693-0.889	0.219
40-41	0.801	0.700	0.816	0.350	0.950	0.806	0.687-0.925	0.142
≥ 42	0.784	1.000	0.773	0.171	1.000	0.888	0.713-1.063	0.037

The number of the training data was 42792 consisted of both a blastocyst image and ten factors of conventional embryo evaluation (CEE). The value of the combination method was a function of multivariate logistic regression with CEE and artificial intelligence for predicting probability for live birth from an image of the blastocyst in patients categorized by age. The optimal cut-point of live birth was the value corresponding to the point with the lowest distance to the upper-left corner of the receiver operator characteristic curve[46]. The accuracies, sensitivities, and specificities were obtained by using cut-points. PPV: Positive predictive value; NPV: Negative predictive value; AUC: Area under the time concentration curve.