OBJECTIVE To describe a computer algorithm designed for in vitro fertilization (IVF) management and to assess the algorithm's accuracy in the day-to-day decision making during ovarian stimulation for IVF when compared to evidence-based decisions by the clinical team. DESIGN Descriptive and comparative study of new technology. SETTING Private fertility practice. INTERVENTION(S) None. PATIENT(S) Data were derived from monitoring during ovarian stimulation from IVF cycles. The database consisted of 2,603 cycles (1,853 autologous and 750 donor cycles) incorporating 7,376 visits for training. An additional 556 unique cycles were used for challenge and to calculate accuracy. There were 59,706 data points. Input variables included estradiol concentrations in picograms per milliliter; ultrasound measurements of follicle diameters in two dimensions in millimeters; cycle day during stimulation and dose of recombinant follicle-stimulating hormone during ovarian stimulation for IVF. MAIN OUTCOME MEASURE(S) Accuracy of the algorithm to predict four critical clinical decisions during ovarian stimulation for IVF: [1] stop stimulation or continue stimulation. If the decision was to stop, then the next automated decision was to [2] trigger or cancel. If the decision was to return, then the next key decisions were [3] number of days to follow-up and [4] whether any dosage adjustment was needed. RESULT(S) Algorithm accuracies for these four decisions are as follows: continue or stop treatment: 0.92; trigger and schedule oocyte retrieval or cancel cycle: 0.96; dose of medication adjustment: 0.82; and number of days to follow-up: 0.87. These accuracies are for first iteration of the algorithm. CONCLUSION(S) We describe a first iteration of a predictive analytic algorithm that is highly accurate and in agreement with evidence-based decisions by expert teams during ovarian stimulation during IVF. These tools offer a potential platform to optimize clinical decision making during IVF.

中文翻译：

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体外受精中的人工智能：用于体外受精期间卵巢刺激日常管理的计算机决策支持系统

目的 描述一种设计用于体外受精 (IVF) 管理的计算机算法，并与临床团队的循证决策相比，评估该算法在 IVF 卵巢刺激期间日常决策的准确性。设计 新技术的描述性和比较性研究。设置私人生育实践。干预措施 无。患者数据来自试管婴儿周期卵巢刺激期间的监测。该数据库包括 2,603 个周期（1,853 个自体周期和 750 个供体周期），包括 7,376 次培训访问。额外的 556 个独特循环用于挑战和计算准确性。有 59,706 个数据点。输入变量包括以皮克/毫升为单位的雌二醇浓度；以毫米为单位的二维卵泡直径的超声测量；刺激期间的周期日和体外受精卵巢刺激期间重组促卵泡激素的剂量。主要结果测量算法在 IVF 卵巢刺激期间预测四个关键临床决策的准确性：[1] 停止刺激或继续刺激。如果决定是停止，那么下一个自动决定是 [2] 触发或取消。如果决定返回，那么接下来的关键决定是 [3] 随访天数和 [4] 是否需要调整剂量。RESULT(S) 这四个决定的算法准确度如下：继续或停止治疗：0.92；触发和安排取卵或取消周期：0.96；药物调整剂量：0.82；和随访天数：0.87。这些精度用于算法的第一次迭代。结论 我们描述了预测分析算法的第一次迭代，该算法高度准确，并且与 IVF 期间卵巢刺激期间专家团队基于证据的决定一致。这些工具提供了一个潜在的平台来优化 IVF 期间的临床决策。