Machine-learning algorithm incorporating capacitated sperm intracellular pH predicts conventional in vitro fertilization success in normospermic patients.
Machine-learning algorithm incorporating capacitated sperm intracellular pH predicts conventional in vitro fertilization success in normospermic patients.
复制标题
结合获能精子细胞内pH值的机器学习算法预测正常精子患者的常规体外受精成功率
DOI：
10.1016/j.fertnstert.2020.10.038
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发表时间：
2021-04
影响因子：
6.7
通讯作者：
Santi, Celia Maria
中科院分区：
文献类型：
--
作者：
Gunderson, Stephanie Jean;Molina, Lis Carmen Puga;Spies, Nicholas;Balestrini, Paula Ania;Buffone, Mariano Gabriel;Jungheim, Emily Susan;Riley, Joan;Santi, Celia Maria
测量人类精子细胞内pH值（pHi），并开发一种机器学习算法来预测正常精子患者成功的常规体外受精（IVF）。对76例体外受精患者的精子进行了体外失能。用流式细胞术测量精子pHi，用计算机辅助精液分析测量高活动性。利用58例患者的临床数据、精子pHi和膜电位对梯度增强机器学习算法进行训练，预测成功的常规IVF，定义为受精比（受精卵母细胞数[2原核]/成熟卵母细胞数）大于0.66。该算法在来自18名患者的独立数据集上进行了验证。学术医疗中心。接受试管受精的正常精子男性。排除使用冷冻精子、已知男性因素不育或仅使用胞浆内单精子注射的患者。没有。成功的常规试管婴儿。精子pHi与高激活运动性和常规体外受精比例（n = 76）呈正相关，但与卵胞浆内单精子注射受精比例（n = 38）无关。在测试集（n = 58）数据的受试者工作曲线分析中，机器学习算法预测常规试管婴儿成功的平均准确率为0.72 (n = 18)，平均曲线下面积为0.81，平均灵敏度为0.65，平均特异性为0.80。在接受体外受精的正常精子患者中，精子pHi与常规受精结果相关。一种机器学习算法可以使用临床参数和能力标记来准确预测接受常规体外受精的正常精子男性的成功受精。
To measure human sperm intracellular pH (pHi) and develop a machine-learning algorithm to predict successful conventional in vitro fertilization (IVF) in normospermic patients. Spermatozoa from 76 IVF patients were capacitated in vitro. Flow cytometry was used to measure sperm pHi, and computer-assisted semen analysis was used to measure hyperactivated motility. A gradient-boosted machine-learning algorithm was trained on clinical data and sperm pHi and membrane potential from 58 patients to predict successful conventional IVF, defined as a fertilization ratio (number of fertilized oocytes [2 pronuclei]/number of mature oocytes) greater than 0.66. The algorithm was validated on an independent set of data from 18 patients. Academic medical center. Normospermic men undergoing IVF. Patients were excluded if they used frozen sperm, had known male factor infertility, or used intracytoplasmic sperm injection only. None. Successful conventional IVF. Sperm pHi positively correlated with hyperactivated motility and with conventional IVF ratio (n = 76) but not with intracytoplasmic sperm injection fertilization ratio (n = 38). In receiver operating curve analysis of data from the test set (n = 58), the machine-learning algorithm predicted successful conventional IVF with a mean accuracy of 0.72 (n = 18), a mean area under the curve of 0.81, a mean sensitivity of 0.65, and a mean specificity of 0.80. Sperm pHi correlates with conventional fertilization outcomes in normospermic patients undergoing IVF. A machine-learning algorithm can use clinical parameters and markers of capacitation to accurately predict successful fertilization in normospermic men undergoing conventional IVF.
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