Using artificial intelligence to avoid human error in identifying embryos: a retrospective cohort study.
Using artificial intelligence to avoid human error in identifying embryos: a retrospective cohort study.
复制标题
使用人工智能避免识别胚胎时的人为错误：一项回顾性队列研究。
DOI：
10.1007/s10815-022-02585-y
复制
发表时间：
2022
影响因子：
3.1
通讯作者：
Shafiee,Hadi
中科院分区：
文献类型：
--
作者：
Hammer,KarissaC;Jiang,VictoriaS;Kanakasabapathy,ManojKumar;Thirumalaraju,Prudhvi;Kandula,Hemanth;Dimitriadis,Irene;Souter,Irene;Bormann,CharlesL;Shafiee,Hadi
PurposeTo确定卷积神经网络（CNN）是否可以用来准确地确定患者身份（ID）的卵裂和囊胚期胚胎的基础上的图像dataselong.MethodsA CNN模型进行了训练和验证超过三个重复的回顾性队列的4889延时胚胎图像。该算法处理了每例患者的胚胎图像，并生成了一个唯一的识别密钥，该密钥与第3天（授精后约65小时（hpi））和第5天（约105 hpi）的时间点的患者ID相关联，形成了我们的数据库。当该算法在第3天（~ 70 hpi）和第5天（~ 110 hpi）的稍后时间点评估胚胎时，它生成与库中可用的患者唯一密钥匹配的另一个密钥。这种方法进行了测试，使用400例患者胚胎队列在第3天和第5天和正确的胚胎识别与CNN算法的数量measured.ResultsCNN技术匹配的患者识别内的随机池的8例患者胚胎队列在第3天与100%的准确性（n= 400例患者; 3个重复）。对于第5天的胚胎队列，8例患者的随机池内的准确性为100%（n= 400例患者; 3个重复）。该技术根据每个胚胎独特的形态特征提供了稳健的见证步骤。该技术可以与现有的成像系统和实验室协议集成，以改善标本跟踪。
PurposeTo determine whether convolutional neural networks (CNN) can be used to accurately ascertain the patient identity (ID) of cleavage and blastocyst stage embryos based on image data alone.MethodsA CNN model was trained and validated over three replicates on a retrospective cohort of 4889 time-lapse embryo images. The algorithm processed embryo images for each patient and produced a unique identification key that was associated with the patient ID at a timepoint on day 3 (~ 65 hours post-insemination (hpi)) and day 5 (~ 105 hpi) forming our data library. When the algorithm evaluated embryos at a later timepoint on day 3 (~ 70 hpi) and day 5 (~ 110 hpi), it generates another key that was matched with the patient’s unique key available in the library. This approach was tested using 400 patient embryo cohorts on day 3 and day 5 and number of correct embryo identifications with the CNN algorithm was measured.ResultsCNN technology matched the patient identification within random pools of 8 patient embryo cohorts on day 3 with 100% accuracy (n= 400 patients; 3 replicates). For day 5 embryo cohorts, the accuracy within random pools of 8 patients was 100% (n= 400 patients; 3 replicates).ConclusionsThis study describes an artificial intelligence-based approach for embryo identification. This technology offers a robust witnessing step based on unique morphological features of each embryo. This technology can be integrated with existing imaging systems and laboratory protocols to improve specimen tracking.