Turki Ahmad, Raml Enas
Electrical and Computer Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Center of Excellence in Intelligent Engineering Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Children (Basel). 2023 Sep 27;10(10):1612. doi: 10.3390/children10101612.
This study systematically examines pediatric adnexal torsion, proposing a diagnostic approach using machine learning techniques to distinguish it from acute appendicitis. Our retrospective analysis involved 41 female pediatric patients divided into two groups: 21 with adnexal torsion (group 1) and 20 with acute appendicitis (group 2). In group 1, the average age was 10 ± 2.6 years, while in group 2, it was 9.8 ± 21.9 years. Our analysis found no statistically significant age distinctions between these two groups. Despite acute lower abdominal pain being a common factor, group 1 displayed shorter pain duration (28.9 h vs. 46.8 h, < 0.05), less vomiting (28% vs. 50%, < 0.05), lower fever incidence (4.7% vs. 50%, < 0.05), reduced leukocytosis (57% vs. 75%, < 0.05), and CRP elevation (30% vs. 80%, < 0.05) compared to group 2. Machine learning techniques, specifically support vector classifiers, were employed using clinical presentation, pain duration, white blood cell counts, and ultrasound findings as features. The classifier consistently demonstrated an average predictive accuracy of 87% to 97% in distinguishing adnexal torsion from appendicitis, as confirmed across various SVM models employing different kernels. Our findings emphasize the capacity of support vector machines (SVMs) and machine learning as a whole to augment diagnostic accuracy when distinguishing between adnexal torsion and acute appendicitis. Nevertheless, it is imperative to validate these results through more extensive investigations and explore alternative machine learning models for a comprehensive understanding of their diagnostic capabilities.
本研究系统地检查了小儿附件扭转，提出了一种使用机器学习技术将其与急性阑尾炎区分开来的诊断方法。我们的回顾性分析涉及41名女性儿科患者，分为两组：21例附件扭转患者（第1组）和20例急性阑尾炎患者（第2组）。第1组的平均年龄为10±2.6岁，而第2组为9.8±2.19岁。我们的分析发现这两组之间在年龄上没有统计学上的显著差异。尽管急性下腹痛是一个共同因素，但与第2组相比，第1组的疼痛持续时间较短（28.9小时对46.8小时，<0.05），呕吐较少（28%对50%，<0.05），发热发生率较低（4.7%对50%，<0.05），白细胞增多减少（57%对75%，<0.05），以及CRP升高（30%对80%，<0.05）。使用临床表现、疼痛持续时间、白细胞计数和超声检查结果作为特征，采用机器学习技术，特别是支持向量分类器。在使用不同核的各种支持向量机模型中得到证实，该分类器在区分附件扭转和阑尾炎方面始终表现出87%至97%的平均预测准确率。我们的研究结果强调了支持向量机（SVM）以及整个机器学习在区分附件扭转和急性阑尾炎时提高诊断准确性的能力。然而，必须通过更广泛的研究来验证这些结果，并探索替代的机器学习模型，以全面了解它们的诊断能力。
