Asada Yoshimasa, Shinohara Tomoya, Yonezawa Sho, Kinugawa Tomoki, Asano Emiko, Kojima Masae, Fukunaga Noritaka, Hashizume Natsuka, Hashiba Yoshiki, Inoue Daichi, Mizuno Rie, Saito Masaya, Kabeya Yoshinori
Asada Ladies Clinic Nagoya Japan.
Asada Institute for Reproductive Medicine Kasugai Japan.
Reprod Med Biol. 2024 Sep 1;23(1):e12603. doi: 10.1002/rmb2.12603. eCollection 2024 Jan-Dec.
Controlled ovarian stimulation (COS) is vital for IVF. We have developed an AI system to support the implementation of COS protocols in our clinical group.
We developed two models as AI algorithms of the AI system. One was the oocyte retrieval decision model, to determine the timing of oocyte retrieval, and the other was the prescription inference model, to provide a prescription similar to that of an expert physician. Data was obtained from IVF treatment records from the In Vitro Fertilization (IVF) management system at the Asada Ladies Clinic, and these models were trained with this data.
The oocyte retrieval decision model achieved superior sensitivity and specificity with 0.964 area under the curve (AUC). The prescription inference model achieved an AUC value of 0.948. Four models, namely the hCG prediction model, the hMG prediction model, the Cetrorelix prediction model, and the Estradiol prediction model included in the prescription inference model, achieved AUC values of 0.914, 0.937, 0.966, and 0.976, respectively.
The AI algorithm achieved high accuracy and was confirmed to be useful. The AI system has now been implemented as a COS tool in our clinical group for self-funded treatments.
控制性卵巢刺激（COS）对体外受精（IVF）至关重要。我们开发了一种人工智能（AI）系统，以支持在我们的临床团队中实施COS方案。
我们开发了两个模型作为AI系统的AI算法。一个是卵母细胞采集决策模型，用于确定卵母细胞采集的时间，另一个是处方推理模型，用于提供与专家医生相似的处方。数据来自浅田女士诊所体外受精（IVF）管理系统的IVF治疗记录，并使用这些数据对这些模型进行训练。
卵母细胞采集决策模型在曲线下面积（AUC）为0.964时具有较高的敏感性和特异性。处方推理模型的AUC值为0.948。处方推理模型中包含的四个模型，即hCG预测模型、hMG预测模型、西曲瑞克预测模型和雌二醇预测模型，AUC值分别为0.914、0.937、0.966和0.976。
AI算法具有较高的准确性，并被证实是有用的。该AI系统现已作为一种COS工具在我们的临床团队中用于自费治疗。
