Children with developmental language disorder (DLD) encounter difficulties in
acquiring various language structures. Early identification and intervention
are crucial to prevent negative long-term outcomes impacting the academic,
social, and emotional development of children. The study aims to develop an
automated method for the identification of DLD using artificial intelligence,
specifically a neural network machine learning algorithm. This protocol is
applied for the first time in Cypriot Greek children, which is generally
considered underresearched in the context of DLD. The neural network model was
trained using perceptual and production data elicited from children with DLD
and healthy controls. The k-fold technique was used to crossvalidate the
algorithm. The performance of the model was evaluated using metrics such as
accuracy, precision, recall, F1 score, and ROC/AUC curve to assess its ability
to make accurate predictions on a set of unseen data. The results demonstrated
high classification values for all metrics (between 0.92 and 0.98), indicating
the high accuracy of the neural model in classifying children with DLD.
Additionally, the variable importance analysis revealed that the language
production skills of children had a more significant impact on the performance
of the model compared to perception skills. Neural networks represent powerful
tools for detecting DLD, providing early and quick assessments of the disorder,
and having the potential to improve clinical outcomes.

利用神经网络机器学习算法，开发了一种自动化方法用于检测儿童发展性语言障碍，通过感知和产出数据对模型进行训练，并且获得了较高的准确性以及对于儿童语言发展技能的重要性。