Purpose: To study the utility of a training session offered to junior embryologists, comparing the results obtained with those reported by a group of senior embryologists.
Methods: The 62 junior embryologists participanting were asked to decide on the quality of the embryos and theg clinical decision to be taken.
Results: The junior embryologists' success rate following the training course was significantly higher than before for embryo classification (48.4% ± 20.4 vs. 59.7% ±16.7) (p < 0.05) and for clinical decision (54.7% ± 19.6 vs. 68.7% ± 17.6) (p < 0.005). Comparison of the degree of agreement between the categories assigned by the junior embryologists and those assigned by consensus among the group of senior embryologists revealed kappa values of k = 0.32 before the course and of k = 0.54 after it. The comparison between pre- and post-training junior and senior embryologists also reflected an improvement in the kappa index for clinical decision, from k = 0.54 to k = 0.68.
Conclusions: Training courses are shown to be an effective tool for increasing the degree of agreement between junior and senior embryologists.
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http://dx.doi.org/10.1007/s10815-011-9639-0 | DOI Listing |
J Med Internet Res
June 2024
Kai Health, Seoul, Republic of Korea.
Background: Current embryo assessment methods for in vitro fertilization depend on subjective morphological assessments. Recently, artificial intelligence (AI) has emerged as a promising tool for embryo assessment; however, its clinical efficacy and trustworthiness remain unproven. Simulation studies may provide additional evidence, provided that they are meticulously designed to mitigate bias and variance.
View Article and Find Full Text PDFMed Sci (Basel)
March 2024
Centre d'aide médicale à la procréation Fertilys, 1950 Maurice-Gauvin Street, Laval, QC H7S 1Z5, Canada.
The computer-assisted program SiD was developed to assess and select sperm in real time based on motility characteristics. To date, there are limited studies examining the correlation between AI-assisted sperm selection and ICSI outcomes. To address this limit, a total of 646 sibling MII oocytes were randomly divided into two groups as follows: the ICSI group (n = 320): ICSI performed with sperm selected by the embryologist and the ICSI-SiD group (n = 326): ICSI performed with sperm selected using SiD software.
View Article and Find Full Text PDFReprod Biomed Online
January 2022
Centro Scienze Natalità, Dept Ob/Gyn, IRCCS San Raffaele Scientific Institute, Milan, Italy.
Research Question: What is the intra- and inter-centre reliability in embryo grading performed according to the Istanbul Consensus across several IVF clinics?
Design: Forty Day 3 embryos and 40 blastocysts were photographed on three focal planes. Senior and junior embryologists from 65 clinics were invited to grade them according to the Istanbul Consensus (Study Phase I). All participants then attended interactive training where a panel of experts graded the same embryos (Study Phase II).
J Assist Reprod Genet
October 2021
Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Purpose: A deep learning artificial intelligence (AI) algorithm has been demonstrated to outperform embryologists in identifying euploid embryos destined to implant with an accuracy of 75.3% (1). Our aim was to evaluate the performance of highly trained embryologists in selecting top quality day 5 euploid blastocysts with and without the aid of a deep learning algorithm.
View Article and Find Full Text PDFBiomed Eng Online
November 2019
Department of Electrophysics, National Chiao Tung University, Hsinchu, 30010, Taiwan.
Background: Total motile sperm count (TMSC) and curvilinear velocity (VCL) are two important parameters in preliminary semen analysis for male infertility. Traditionally, both parameters are evaluated manually by embryologists or automatically using an expensive computer-assisted sperm analysis (CASA) instrument. The latter applies a point-tracking method using an image processing technique to detect, recognize and classify each of the target objects, individually, which is complicated.
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