Hall J M M, Nguyen T V, Dinsmore A W, Perugini D, Perugini M, Fukunaga N, Asada Y, Schiewe M, Lim A Y X, Lee C, Patel N, Bhadarka H, Chiang J, Bose D P, Mankee-Sookram S, Minto-Bain C, Bilen E, Diakiw S M
Life Whisperer Diagnostics (a subsidiary of Presagen), San Francisco, CA, USA, and Adelaide, Australia; Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Adelaide, Australia; Adelaide Business School, The University of Adelaide, Adelaide, Australia.
Life Whisperer Diagnostics (a subsidiary of Presagen), San Francisco, CA, USA, and Adelaide, Australia.
Reprod Biomed Online. 2024 Dec;49(6):104403. doi: 10.1016/j.rbmo.2024.104403. Epub 2024 Aug 13.
Can federated learning be used to develop an artificial intelligence (AI) model for evaluating oocyte competence using two-dimensional images of denuded oocytes in metaphase II prior to intracytoplasmic sperm injection (ICSI)?
The oocyte AI model demonstrated area under the curve (AUC) up to 0.65 on two blind test datasets. High sensitivity for predicting competent oocytes (83-88%) was offset by lower specificity (26-36%). Exclusion of confounding biological variables (male factor infertility and maternal age ≥35 years) improved AUC up to 14%, primarily due to increased specificity. AI score correlated with size of the zona pellucida and perivitelline space, and ooplasm appearance. AI score also correlated with blastocyst expansion grade and morphological quality. The sum of AI scores from oocytes in group culture images predicted the formation of two or more usable blastocysts (AUC 0.77).
An AI model to evaluate oocyte competence was developed using federated learning, representing an essential step in protecting patient data. The AI model was significantly predictive of oocyte competence, as defined by usable blastocyst formation, which is a critical factor for IVF success. Potential clinical utility ranges from selective oocyte fertilization to guiding treatment decisions regarding additional rounds of oocyte retrieval.
In total, 10,677 oocyte images with associated metadata were collected prospectively by eight IVF clinics across six countries. AI training used federated learning, where data were retained on regional servers to comply with data privacy laws. The final AI model required a single image as input to evaluate oocyte competence, which was defined by the formation of a usable blastocyst (≥expansion grade 3 by day 5 or 6 post ICSI).
能否使用联邦学习来开发一种人工智能（AI）模型，该模型利用卵母细胞胞浆内单精子注射（ICSI）前处于减数分裂中期II的裸卵的二维图像来评估卵母细胞的质量？
在两个盲测数据集上，卵母细胞AI模型的曲线下面积（AUC）高达0.65。预测有发育能力卵母细胞的高敏感性（83%-88%）被较低的特异性（26%-36%）所抵消。排除混杂的生物学变量（男性因素不育和母亲年龄≥35岁）可使AUC提高多达14%，这主要是由于特异性增加。AI评分与透明带和卵周间隙的大小以及卵质外观相关。AI评分还与囊胚扩张等级和形态质量相关。来自群体培养图像中卵母细胞的AI评分总和可预测两个或更多可用囊胚的形成（AUC为0.77）。
使用联邦学习开发了一种评估卵母细胞质量的AI模型，这是保护患者数据的关键一步。该AI模型对于由可用囊胚形成所定义的卵母细胞质量具有显著的预测性，而可用囊胚形成是体外受精成功的关键因素。潜在的临床应用范围从选择性卵母细胞受精到指导关于额外轮次卵母细胞采集的治疗决策。
六个国家的八家体外受精诊所前瞻性地收集了总共10677张带有相关元数据的卵母细胞图像。AI训练使用联邦学习，数据保留在区域服务器上以符合数据隐私法。最终的AI模型需要一张图像作为输入来评估卵母细胞质量，卵母细胞质量由可用囊胚的形成来定义（ICSI后第5天或第6天≥扩张等级3）。
