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近年來，輔助生殖領域time-lapse胚胎時差培養技術逐漸被胚胎學家認可接受，Embryoscope時差培養箱，由於其強大的功能，安全的培養環境，直觀清晰的成像係統，強大的數據處理及分析係統，及其可靠的決策支持算法KIDScore D3和KIDScore D5，得到許多胚胎學家的一致好評。那麽KIDScore D3算法是如何建立的呢？下文即為KIDScore D3算法建立的方法和過程，部分原文和譯文如下：
Development of a generally applicable morphokinetic algorithm capable of predicting the implantation potential of embryos transferred on Day 3
STUDY QUESTION: Can a generally applicable morphokinetic algorithm suitable for Day 3 transfers of time-lapse monitored embryos originating from different culture conditions and fertilization methods be developed for the purpose of supporting the embryologist’s decision on which embryo to transfer back to the patient in assisted reproduction?
SUMMARY ANSWER: The algorithm presented here can be used independently of culture conditions and fertilization method and provides predictive power not surpassed by other published algorithms for ranking embryos according to their blastocyst formation potential.
WHAT IS KNOWN ALREADY: Generally applicable algorithms have so far been developed only for predicting blastocyst formation. A number of clinics have reported validated implantation prediction algorithms, which have been developed based on clinic-specific culture conditions and clinical environment. However, a generally applicable embryo evaluation algorithm based on actual implantation outcome has not yet been reported.
STUDY DESIGN, SIZE, DURATION: Retrospective evaluation of data extracted from a database of known implantation data (KID) originating from 3275 embryos transferred on Day 3 conducted in 24 clinics between 2009 and 2014. The data represented different culture conditions (reduced and ambient oxygen with various culture medium strategies) and fertilization methods (IVF, ICSI). The capability to predict blastocyst formation was evaluated on an independent set of morphokinetic data from 11 218 embryos which had been cultured to Day 5.
PARTICIPANTS/MATERIALS, SETTING, METHODS: The algorithm was developed by applying automated recursive partitioning to a large number of annotation types and derived equations, progressing to a five-fold cross-validation test of the complete data set and a validation test of different incubation conditions and fertilization methods. The results were expressed as receiver operating characteristics curves using the area under the curve (AUC) to establish the predictive strength of the algorithm.
MAIN RESULTS : By applying the here developed algorithm (KIDScore), which was based on six annotations (the number of pronuclei equals 2 at the 1-cell stage, time from insemination to pronuclei fading at the 1-cell stage, time from insemination to the 2-cell stage, time from insemination to the 3-cell stage, time from insemination to the 5-cell stage and time from insemination to the 8-cell stage) and ranking the embryos in five groups, the implantation potential of the embryos was predicted with an AUC of 0.650. On Day 3 the KIDScore algorithm was capable of predicting blastocyst development with an AUC of 0.745 and blastocyst quality with an AUC of 0.679. In a comparison of blastocyst prediction including six other published algorithms and KIDScore, only KIDScore and one more algorithm surpassed an algorithm constructed on conventional Alpha/ESHRE consensus timings in terms of predictive power.
LIMITATIONS, REASONS FOR CAUTION: Some morphological assessments were not available and consequently three of the algorithms in the comparison were not used in full and may therefore have been put at a disadvantage. Algorithms based on implantation data from Day 3 embryo transfers require adjustments to be capable of predicting the implantation potential of Day 5 embryo transfers. The current study is restricted by its retrospective nature and absence of live birth information. Prospective Randomized Controlled Trials should be used in future studies to establish the value of time-lapse technology and morphokinetic evaluation.
WIDER IMPLICATIONS OF THE FINDINGS: Algorithms applicable to different culture conditions can be developed if based on large data sets of heterogeneous origin.
STUDY FUNDING/COMPETING INTEREST(S): This study was funded by Vitrolife A/S, Denmark and Vitrolife AB, Sweden. B.M.P.’scompany BMP Analytics is performing consultancy for Vitrolife A/S. M.B. is employed at Vitrolife A/S. M.M.’s company ilabcomm GmbH received honorarium for consultancy from Vitrolife AB. D.K.G. received research support from Vitrolife AB.
Key words: time-lapse / algorithm / decision support tool / prediction model / morphokinetic / implantation / embryo selection /Blastocyst
摘要：到目前為止，尚隻有適用於預測囊胚形成的算法已經被開發出來。許多生殖中心報告了經過驗證的著床預測算法，這些算法是根據特定中心的培養條件和臨床環境開發的。然而，基於胚胎實際著床結果的普遍適用算法還沒有被報道。本研究回顧性評估了2009年至2014年間在24家中心進行的第3天移植的3275枚胚胎的已知植入數據（KID）。這些數據代表了不同的培養條件（減少氧含量或者使用不同培養基）和受精方法（IVF，ICSI），預測囊胚的形成是根據來自培養到第5天的11 218個胚胎的一組獨立的形態動力學數據進行評估的。該算法是通過將自動遞歸分區應用於大量注釋類型和導出的方程，對完整數據集進行了五次交叉驗證測試，並對不同的培養條件和受精方法進行了驗證測試。結果表示為樣本當前的特性曲線，通過曲線下麵積（AUC）來建立算法的預測強度 , 即 KIDScore D3算法，該算法基於六種注解（1細胞階段原核的均數2，1細胞階段從授精到原核消失的時間，從授精開始的時間到2細胞階段，從受精到3細胞階段的時間，從受精到5細胞階段的時間，從受精到8細胞階段），將胚胎分為五組，預測胚胎的著床潛力為0.650。KIDScore D3算法能夠以0.745的AUC預測囊胚發育，並以0.679的AUC預測囊胚質量。在囊胚預測的比較中，包括其他六個已發布的算法和KIDScore D3，隻有KIDScore D3和另一個算法在預測能力方麵超過了基於常規Alpha / ESHRE共識時構建的算法。由於無法進行某些形態學評估，有三個算法沒有得到充分利用，因此可能處於不利地位。基於 KIDScore D3算法需要進行調整，以能夠預測第5天胚胎移植的著床潛力。當前的研究受到其回顧性數據和缺乏胎兒活產信息的限製，因此未來研究中為了確定時差培養技術和形態動力學評估的價值，需要做前瞻性隨機對照試驗。