Artificial Intelligence to Determine Fetal Sex.

IF 1.5 4区 医学 Q3 OBSTETRICS & GYNECOLOGY
American journal of perinatology Pub Date : 2024-10-01 Epub Date: 2024-02-09 DOI:10.1055/a-2265-9177
Emily H Frisch, Anant Jain, Mike Jin, Erik P Duhaime, Amol Malshe, Steve Corey, Robert Allen, Nicole M Duggan, Chanel E Fischetti
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引用次数: 0

Abstract

Objective:  This proof-of-concept study assessed how confidently an artificial intelligence (AI) model can determine the sex of a fetus from an ultrasound image.

Study design:  Analysis was performed using 19,212 ultrasound image slices from a high-volume fetal sex determination practice. This dataset was split into a training set (11,769) and test set (7,443). A computer vision model was trained using a transfer learning approach with EfficientNetB4 architecture as base. The performance of the computer vision model was evaluated on the hold out test set. Accuracy, Cohen's Kappa and Multiclass Receiver Operating Characteristic area under the curve (AUC) were used to evaluate the performance of the model.

Results:  The AI model achieved an Accuracy of 88.27% on the holdout test set and a Cohen's Kappa score 0.843. The ROC AUC score for Male was calculated to be 0.896, for Female a score of 0.897, for Unable to Assess a score of 0.916, and for Text Added a score of 0.981 was achieved.

Conclusion:  This novel AI model proved to have a high rate of fetal sex capture that could be of significant use in areas where ultrasound expertise is not readily available.

Key points: · This is the first proof-of-concept AI model to determine fetal sex.. · This study adds to the growing research in ultrasound AI.. · Our findings demonstrate AI integration into obstetric care..

人工智能确定胎儿性别
目的 本概念验证研究评估了人工智能(AI)模型从超声图像判断胎儿性别的可信度。研究设计 使用来自大量胎儿性别鉴定实践的 19212 张超声波图像进行分析。该数据集分为训练集(11769 张)和测试集(7443 张)。以 EfficientNetB4 架构为基础,使用迁移学习方法训练计算机视觉模型。计算机视觉模型的性能在保留测试集上进行了评估。准确率、Cohen's Kappa 和多类接收器工作特征 AUC 用于评估模型的性能。结果 在保留测试集上,人工智能模型的准确率达到 88.27%,科恩卡帕得分 0.843。男性的 ROC AUC 得分为 0.896,女性的 ROC AUC 得分为 0.897,无法评估的 ROC AUC 得分为 0.916,文本添加的 ROC AUC 得分为 0.981。结论 事实证明,这种新型人工智能模型对胎儿性别的捕捉率很高,在缺乏超声专业知识的地区可以发挥重要作用。
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来源期刊
American journal of perinatology
American journal of perinatology 医学-妇产科学
CiteScore
5.90
自引率
0.00%
发文量
302
审稿时长
4-8 weeks
期刊介绍: The American Journal of Perinatology is an international, peer-reviewed, and indexed journal publishing 14 issues a year dealing with original research and topical reviews. It is the definitive forum for specialists in obstetrics, neonatology, perinatology, and maternal/fetal medicine, with emphasis on bridging the different fields. The focus is primarily on clinical and translational research, clinical and technical advances in diagnosis, monitoring, and treatment as well as evidence-based reviews. Topics of interest include epidemiology, diagnosis, prevention, and management of maternal, fetal, and neonatal diseases. Manuscripts on new technology, NICU set-ups, and nursing topics are published to provide a broad survey of important issues in this field. All articles undergo rigorous peer review, with web-based submission, expedited turn-around, and availability of electronic publication. The American Journal of Perinatology is accompanied by AJP Reports - an Open Access journal for case reports in neonatology and maternal/fetal medicine.
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