利用胚胎图像预测短期授精后受精的机器学习模型。

IF 2.7 3区 医学 Q2 OBSTETRICS & GYNECOLOGY
Reproductive Medicine and Biology Pub Date : 2025-04-15 eCollection Date: 2025-01-01 DOI:10.1002/rmb2.12649
Masato Saito, Hirofumi Haraguchi, Ikumi Nakajima, Shinya Fukuda, Chenghua Zhu, Norio Masuya, Kazunori Matsumoto, Yuya Yoshikawa, Tomoki Tanaka, Satoshi Kishigami, Leona Matsumoto
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引用次数: 0

摘要

目的:本研究建立了一种基于胚胎图像训练的机器学习模型(MLM),用于预测短期授精后早期挽救ICSI的受精情况,并将其预测性能与胚胎学家人工分类进行比较。方法:采用ResNet50软件对授精后4.5 h和8 h的胚胎图像进行预处理。采用光梯度增强机(Light Gradient Boosting Machine, Light GBM)进行向量训练。测试数据集中的受精由MLM评估,其中有7名高级胚胎学家和11名初级胚胎学家。预测指标采用重复测量方差分析和配对t检验进行分析。结果:MLM在准确率(0.71±0.01、0.75±0.05、0.61±0.05)、召回率(0.84±0.02、0.84±0.10、0.61±0.07)、f1评分(0.78±0.01、0.81±0.04、0.66±0.04)、曲线下面积(0.73±0.0 3、0.73±0.06、0.61±0.07)方面均优于初级胚胎学家。MLM可以通过分析图像中的细胞质变化,有效预测短期授精后的受精情况。这些结果强调了增强临床决策和改善患者预后的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A machine learning model for predicting fertilization following short-term insemination using embryo images.

Purpose: This study established a machine learning model (MLM) trained on embryo images to predict fertilization following short-term insemination for early rescue ICSI and compared its predictive performance with the embryologist's manual classification.

Methods: Embryo images at 4.5 and 8 h post-insemination were preprocessed into vectors using ResNet50. The Light Gradient Boosting Machine (Light GBM) was employed for training vectors. Fertilization in the test dataset was assessed by MLM, with seven senior and 11 junior embryologists. Predictive metrics were analyzed using repeated measures ANOVA and paired t-tests.

Results: Comparing MLM, senior embryologists, and junior embryologists, significant differences were observed in accuracy (0.71 ± 0.01, 0.75 ± 0.05, 0.61 ± 0.05), recall (0.84 ± 0.02, 0.84 ± 0.10, 0.61 ± 0.07), F1-score (0.78 ± 0.01, 0.81 ± 0.04, 0.66 ± 0.04), and area under the curve (0.73 ± 0.0 3, 0.73 ± 0.06, 0.61 ± 0.07), the MLM outperforming junior embryologists with <1 year of experience. No significant differences were observed between the MLM and senior embryologists with over 5 years of experience.

Conclusions: MLM can effectively predict fertilization following short-term insemination by analyzing cytoplasmic changes in images. These results underscore the potential to enhance clinical decision-making and improve patient outcomes.

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来源期刊
CiteScore
5.70
自引率
5.90%
发文量
53
审稿时长
20 weeks
期刊介绍: Reproductive Medicine and Biology (RMB) is the official English journal of the Japan Society for Reproductive Medicine, the Japan Society of Fertilization and Implantation, the Japan Society of Andrology, and publishes original research articles that report new findings or concepts in all aspects of reproductive phenomena in all kinds of mammals. Papers in any of the following fields will be considered: andrology, endocrinology, oncology, immunology, genetics, function of gonads and genital tracts, erectile dysfunction, gametogenesis, function of accessory sex organs, fertilization, embryogenesis, embryo manipulation, pregnancy, implantation, ontogenesis, infectious disease, contraception, etc.
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