Pavanya Manohar, Chadaga Krishnaraj, J Vennila, Vasudeva Akhila, Rao Bhamini Krishna, Bhat Shashikala K
Department of Obstetrics and Gynecology, Dr TMA Pai Hospital (Udupi), Melaka Manipal Medical College Manipal Academy of Higher Education Manipal Karnataka India.
Manipal Institute of Technology Manipal Academy of Higher Education Manipal India.
Healthc Technol Lett. 2025 Sep 16;12(1):e70014. doi: 10.1049/htl2.70014. eCollection 2025 Jan-Dec.
Foetal outcomes with reduced foetal movements in the later pregnancy are widely reported. We intend to quantify early foetal movements (FMs) through a checklist and their foetal outcomes via explainable artificial intelligence. It is a prospective observational study of 356 foetuses in the first trimester, and we were able to screen only 230 foetuses for early foetal growth restriction (FGR). Of which 26 were FGR and 204 were normal and were identified from the dataset using non-probability convenience sampling techniques. JASP 0.18.3, Jamovi 2.3.21, and Google Collaboratory were used to construct the predictive model. Ultrasound scores of more than 8 had favourable indicators of a normal foetus. CatBoost had the highest accuracy and recall of 87; the highest precision of 79 was given by random forest (RF), decision tree (DT), K-nearest neighbour (KNN), and CatBoost; and the F1 score of 83 was given by CatBoost. The lowest Hamming loss of 0.13 was obtained via CatBoost. The highest Jaccard score of 0.87 was by CatBoost. The stacked model has an accuracy of 89, a precision of 79, and a recall of 83. Shapley additive explanations (SHAP), local interpretable model-agnostic explanations (LIME), QLattice, and Anchor also provided good explanations. The created model can serve as a warning tool to obstetricians to make timely medical decisions.
妊娠后期胎动减少的胎儿结局已有广泛报道。我们打算通过一份清单对早期胎动（FM）进行量化，并通过可解释的人工智能来分析其胎儿结局。这是一项对356例孕早期胎儿的前瞻性观察研究，我们仅能筛查出230例胎儿用于早期胎儿生长受限（FGR）的研究。其中26例为FGR，204例正常，这些是使用非概率便利抽样技术从数据集中确定的。使用JASP 0.18.3、Jamovi 2.3.21和谷歌协作平台构建预测模型。超声评分超过8分表明胎儿正常。CatBoost的准确率和召回率最高，为87；随机森林（RF）、决策树（DT）、K近邻（KNN）和CatBoost的精确率最高，为79；CatBoost的F1评分为83。通过CatBoost获得的最低汉明损失为0.13。CatBoost的最高杰卡德评分为0.87。堆叠模型的准确率为89，精确率为79，召回率为83。夏普利加法解释（SHAP）、局部可解释模型无关解释（LIME）、QLattice和Anchor也提供了很好的解释。所创建的模型可作为产科医生及时做出医疗决策的警示工具。
