Life Whisperer Diagnostics (a subsidiary of Presagen), San Francisco, CA, USA, and Adelaide, SA, Australia.
Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, The University of Adelaide, Adelaide, SA, Australia.
Hum Reprod. 2022 Jul 30;37(8):1746-1759. doi: 10.1093/humrep/deac131.
Can an artificial intelligence (AI) model predict human embryo ploidy status using static images captured by optical light microscopy?
Results demonstrated predictive accuracy for embryo euploidy and showed a significant correlation between AI score and euploidy rate, based on assessment of images of blastocysts at Day 5 after IVF.
Euploid embryos displaying the normal human chromosomal complement of 46 chromosomes are preferentially selected for transfer over aneuploid embryos (abnormal complement), as they are associated with improved clinical outcomes. Currently, evaluation of embryo genetic status is most commonly performed by preimplantation genetic testing for aneuploidy (PGT-A), which involves embryo biopsy and genetic testing. The potential for embryo damage during biopsy, and the non-uniform nature of aneuploid cells in mosaic embryos, has prompted investigation of additional, non-invasive, whole embryo methods for evaluation of embryo genetic status.
STUDY DESIGN, SIZE, DURATION: A total of 15 192 blastocyst-stage embryo images with associated clinical outcomes were provided by 10 different IVF clinics in the USA, India, Spain and Malaysia. The majority of data were retrospective, with two additional prospectively collected blind datasets provided by IVF clinics using the genetics AI model in clinical practice. Of these images, a total of 5050 images of embryos on Day 5 of in vitro culture were used for the development of the AI model. These Day 5 images were provided for 2438 consecutively treated women who had undergone IVF procedures in the USA between 2011 and 2020. The remaining images were used for evaluation of performance in different settings, or otherwise excluded for not matching the inclusion criteria.
PARTICIPANTS/MATERIALS, SETTING, METHODS: The genetics AI model was trained using static 2-dimensional optical light microscope images of Day 5 blastocysts with linked genetic metadata obtained from PGT-A. The endpoint was ploidy status (euploid or aneuploid) based on PGT-A results. Predictive accuracy was determined by evaluating sensitivity (correct prediction of euploid), specificity (correct prediction of aneuploid) and overall accuracy. The Matthew correlation coefficient and receiver-operating characteristic curves and precision-recall curves (including AUC values), were also determined. Performance was also evaluated using correlation analyses and simulated cohort studies to evaluate ranking ability for euploid enrichment.
Overall accuracy for the prediction of euploidy on a blind test dataset was 65.3%, with a sensitivity of 74.6%. When the blind test dataset was cleansed of poor quality and mislabeled images, overall accuracy increased to 77.4%. This performance may be relevant to clinical situations where confounding factors, such as variability in PGT-A testing, have been accounted for. There was a significant positive correlation between AI score and the proportion of euploid embryos, with very high scoring embryos (9.0-10.0) twice as likely to be euploid than the lowest-scoring embryos (0.0-2.4). When using the genetics AI model to rank embryos in a cohort, the probability of the top-ranked embryo being euploid was 82.4%, which was 26.4% more effective than using random ranking, and ∼13-19% more effective than using the Gardner score. The probability increased to 97.0% when considering the likelihood of one of the top two ranked embryos being euploid, and the probability of both top two ranked embryos being euploid was 66.4%. Additional analyses showed that the AI model generalized well to different patient demographics and could also be used for the evaluation of Day 6 embryos and for images taken using multiple time-lapse systems. Results suggested that the AI model could potentially be used to differentiate mosaic embryos based on the level of mosaicism.
LIMITATIONS, REASONS FOR CAUTION: While the current investigation was performed using both retrospectively and prospectively collected data, it will be important to continue to evaluate real-world use of the genetics AI model. The endpoint described was euploidy based on the clinical outcome of PGT-A results only, so predictive accuracy for genetic status in utero or at birth was not evaluated. Rebiopsy studies of embryos using a range of PGT-A methods indicated a degree of variability in PGT-A results, which must be considered when interpreting the performance of the AI model.
These findings collectively support the use of this genetics AI model for the evaluation of embryo ploidy status in a clinical setting. Results can be used to aid in prioritizing and enriching for embryos that are likely to be euploid for multiple clinical purposes, including selection for transfer in the absence of alternative genetic testing methods, selection for cryopreservation for future use or selection for further confirmatory PGT-A testing, as required.
STUDY FUNDING/COMPETING INTEREST(S): Life Whisperer Diagnostics is a wholly owned subsidiary of the parent company, Presagen Holdings Pty Ltd. Funding for the study was provided by Presagen with grant funding received from the South Australian Government: Research, Commercialisation, and Startup Fund (RCSF). 'In kind' support and embryology expertise to guide algorithm development were provided by Ovation Fertility. 'In kind' support in terms of computational resources provided through the Amazon Web Services (AWS) Activate Program. J.M.M.H., D.P. and M.P. are co-owners of Life Whisperer and Presagen. S.M.D., M.A.D. and T.V.N. are employees or former employees of Life Whisperer. S.M.D, J.M.M.H, M.A.D, T.V.N., D.P. and M.P. are listed as inventors of patents relating to this work, and also have stock options in the parent company Presagen. M.V. sits on the advisory board for the global distributor of the technology described in this study and also received support for attending meetings.
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人工智能 (AI) 模型能否使用光学显微镜拍摄的静态图像来预测人类胚胎的倍性状态？
结果表明，该模型对胚胎整倍体具有预测准确性，并显示出 AI 评分与整倍体率之间存在显著相关性，这是基于对第 5 天体外受精后囊胚的图像评估得出的。
具有正常人类染色体 46 条染色体的整倍体胚胎被优先选择用于转移，而不是非整倍体胚胎（异常染色体），因为它们与改善临床结局相关。目前，胚胎遗传状态的评估最常通过植入前遗传检测非整倍体（PGT-A）进行，该检测涉及胚胎活检和基因检测。在活检过程中胚胎可能受到损伤，以及镶嵌胚胎中非整倍体细胞的非均匀性，这促使人们研究其他非侵入性的、整体胚胎方法来评估胚胎的遗传状态。
研究设计、规模、持续时间：总共提供了来自美国、印度、西班牙和马来西亚的 10 家不同 IVF 诊所的 15192 个囊胚期胚胎图像及其相关临床结果。大部分数据为回顾性的，另外还有两个由 IVF 诊所提供的前瞻性收集的盲数据集，这些数据使用遗传学 AI 模型在临床实践中进行。这些图像中，共有 5050 个第 5 天体外培养的胚胎图像用于开发 AI 模型。这些第 5 天的图像是为在美国 2011 年至 2020 年间接受过 IVF 程序的 2438 名连续接受治疗的女性提供的。其余的图像用于在不同的环境中评估性能，或以不匹配纳入标准为由排除。
参与者/材料、设置、方法：遗传学 AI 模型使用具有遗传元数据的第 5 天囊胚的静态 2 维光学显微镜图像进行训练，这些元数据是从 PGT-A 中获得的。终点是根据 PGT-A 结果的倍性状态（整倍体或非整倍体）。通过评估敏感性（整倍体的正确预测）、特异性（非整倍体的正确预测）和总体准确性来确定预测准确性。还确定了马修相关系数和接收器工作特征曲线以及精度-召回曲线（包括 AUC 值）。还通过相关性分析和模拟队列研究来评估整倍体富集的排名能力来评估性能。
盲数据集的预测准确性为 65.3%，敏感性为 74.6%。当盲数据集清除质量差和标记错误的图像时，整体准确性增加到 77.4%。该性能可能与已经考虑到混杂因素（如 PGT-A 检测的变异性）的临床情况相关。AI 评分与整倍体胚胎比例之间存在显著正相关，得分非常高的胚胎（9.0-10.0）是得分最低的胚胎（0.0-2.4）的两倍。当使用遗传学 AI 模型对队列中的胚胎进行排名时，排名最高的胚胎为整倍体的概率为 82.4%，比随机排名高 26.4%，比 Gardner 评分高 13-19%。当考虑到前两名排名的胚胎中可能有一个为整倍体的可能性时，概率增加到 97.0%，而前两名排名的胚胎都为整倍体的概率为 66.4%。进一步的分析表明，AI 模型可以很好地推广到不同的患者人群，也可以用于评估第 6 天的胚胎和使用多个时间推移系统拍摄的图像。结果表明，该 AI 模型可以潜在地区分基于镶嵌程度的镶嵌胚胎。
局限性、谨慎的原因：虽然当前的调查是使用回顾性和前瞻性收集的数据进行的，但继续评估遗传学 AI 模型的实际用途将非常重要。所描述的终点是基于 PGT-A 结果的整倍体，因此没有评估在子宫内或出生时的遗传状态的预测准确性。使用一系列 PGT-A 方法对胚胎进行的重新活检研究表明，PGT-A 结果存在一定程度的变异性，在解释 AI 模型的性能时必须考虑到这一点。
这些发现共同支持在临床环境中使用这种遗传学 AI 模型来评估胚胎的倍性状态。结果可用于帮助确定具有更高整倍体可能性的胚胎的优先级和富集，以满足多种临床目的，包括在没有替代遗传测试方法的情况下选择转移、选择用于未来使用的冷冻保存或根据需要选择进一步的确认性 PGT-A 测试。
研究资金/利益冲突：Life Whisperer Diagnostics 是母公司 Presagen Holdings Pty Ltd 的全资子公司。该研究的资金由 Presagen 提供，南澳大利亚政府提供的研究、商业化和启动基金（RCSF）获得赠款。Ovation Fertility 提供胚胎学专业知识和“实物”支持以指导算法开发。亚马逊网络服务 (AWS) 激活计划通过“实物”支持提供计算资源。J.M.M.H、D.P. 和 M.P. 是 Life Whisperer 和 Presagen 的共同所有者。S.M.D、J.M.M.H、M.A.D.、T.V.N. 是 Life Whisperer 的员工或前员工。S.M.D、J.M.M.H、M.A.D.、T.V.N.、D.P. 和 M.P. 是与这项工作相关的专利的发明者，并且还拥有母公司 Presagen 的股票期权。M.V. 担任该技术全球分销商的顾问委员会成员，还获得了参加会议的支持。
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