P-211 Development of an artificial intelligence software with consistent laboratory data from a single IVF center: performance of a new interface to predict clinical pregnancy
Authors: Lorenzon A , Nicolielo Barreto M , Erberelli R , Lourenço B , Alegretti J  R , Motta E  L , Chehin M  B , Moraes V  C , Maffeis M  C , Chéles D  S , Nogueira M  F , Rocha J  C 
Published: July 2024

Abstract

Study question
How is the performance of a Morphological Artificial Intelligence Assistant (MAIA) on embryo selection to clinical pregnancy prediction?

Summary answer
MAIA presented a sensibility of 79.8% and specificity of 72.9% to predict a clinical pregnancy and reached a performance of 64.7% on prospective validation test.

What is known already
Artificial intelligence (AI) tools have gained attention in assisted reproductive treatments. Algorithms for gametes and embryo classification and selection are commercially available in an exponential increasing race. Usually, those algorithms/software are developed based on data acquisition of several ART centers, from different countries and continents, which may overlap some but rarely all clinical and laboratory methodologies and protocols. In this way, the purpose to develop a software restricted to a single center may represent several advantages which is not highlighted at this moment. Here, we developed a full in-house software for embryo selection and prediction of clinical pregnancy.

Study design, size, duration
MAIA was developed through artificial neural networks technique and genetic algorithms using the input of morphological data from 1.008 sequential transferred blastocysts and known reproductive outcomes (clinical pregnancy, presence of gestational sac and heartbeat). Blastocysts images were obtained from IVF cycles based in a single ART center. All embryos were cultured in a time-lapse incubator (Embryoscope Plus, Vitrolife) and blastocyst digital image processing were analyzed considering 33 mathematic variables.

Participants/materials, setting, methods
Input data were randomized for training, validation and test (70, 15 and 15%, respectively). From 1008 images, 755 were used for effective AI learning, 174 were used for the blind test and 79 were excluded due to poor embryo visualization. For performance validation, the software was responsible for embryo selection in 34 elective single embryo transfers. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was measured to obtain predictive power.

Main results and the role of chance
In the blind test (n = 174), 71 embryos were correctly classified as positive for clinical pregnancy (CP) – true positive – and 18 embryos were misclassified as negative for CP. In negative CP, 62 embryos were correctly classified as negative for CP – true negative – and 23 embryos were misclassified as positive for CP. This represented a sensibility of 79.8%, specificity of 72.9% and precision of 75.5% for CP. The AUC was 76.52%. In performance validation, 34 elective single embryo transfers, meaning there were from cycles with more than 1 viable blastocyst for selection, the chosen embryo was decided by MAIA. In those, 22 achieved a clinical pregnancy (64.7%), in which MAIA predicted correctly the CP in 19 (86.4%) – the software may predict that none of embryos available would achieve a CP. From 12 negative outcomes, MAIA predicted in 25% that none of the embryos available would resulted in CP. Overall prediction for positive and negative CP in performance validation was 64.7%.

Limitations, reasons for caution
MAIA software was built with the database from a single IVF center and its performance may be impacted if used in different ART services. The number of single embryo transfers used to validate the sofware performance is still low.

Wider implications of the findings
This is a new artificial intelligence software for embryo selection and clinical pregnancy prediction. Different from all available commercial software, MAIA was built in homogenous clinical and laboratory protocols, analyzing the database from a single IVF center, and were able to reach a performance of 64.7% on prospective validation test.

Trial registration number
Not applicable.