Anjum Mohd, Shahab Sana, Yu Yang
Department of Computer Engineering, Aligarh Muslim University, Aligarh 202001, India.
Department of Business Administration, College of Business Administration, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Diagnostics (Basel). 2023 Feb 26;13(5):887. doi: 10.3390/diagnostics13050887.
Neurodegenerative diseases are a group of conditions that involve the progressive loss of function of neurons in the brain and spinal cord. These conditions can result in a wide range of symptoms, such as difficulty with movement, speech, and cognition. The causes of neurodegenerative diseases are poorly understood, but many factors are believed to contribute to the development of these conditions. The most important risk factors include ageing, genetics, abnormal medical conditions, toxins, and environmental exposures. A slow decline in visible cognitive functions characterises the progression of these diseases. If left unattended or unnoticed, disease progression can result in serious issues such as the cessation of motor function or even paralysis. Therefore, early recognition of neurodegenerative diseases is becoming increasingly important in modern healthcare. Many sophisticated artificial intelligence technologies are incorporated into modern healthcare systems for the early recognition of these diseases. This research article introduces a Syndrome-dependent Pattern Recognition Method for the early detection and progression monitoring of neurodegenerative diseases. The proposed method determines the variance between normal and abnormal intrinsic neural connectivity data. The observed data is combined with previous and healthy function examination data to identify the variance. In this combined analysis, deep recurrent learning is exploited by tuning the analysis layer based on variance suppressed by identifying normal and abnormal patterns in the combined analysis. This variance from different patterns is recurrently used to train the learning model for maximising of recognition accuracy. The proposed method achieves 16.77% high accuracy, 10.55% high precision, and 7.69% high pattern verification. It reduces the variance and verification time by 12.08% and 12.02%, respectively.
神经退行性疾病是一组涉及大脑和脊髓中神经元功能逐渐丧失的病症。这些病症可导致广泛的症状，如运动、言语和认知困难。神经退行性疾病的病因尚不清楚，但许多因素被认为与这些病症的发展有关。最重要的风险因素包括衰老、遗传、异常病症、毒素和环境暴露。这些疾病的进展以可见认知功能的缓慢下降为特征。如果不加以关注或未被注意到，疾病进展可能导致严重问题，如运动功能停止甚至瘫痪。因此，在现代医疗保健中，早期识别神经退行性疾病变得越来越重要。许多先进的人工智能技术被纳入现代医疗系统以早期识别这些疾病。这篇研究文章介绍了一种用于神经退行性疾病早期检测和进展监测的综合征依赖模式识别方法。所提出的方法确定正常和异常内在神经连接数据之间的差异。将观察到的数据与先前的健康功能检查数据相结合以识别差异。在这种综合分析中，通过基于在综合分析中识别正常和异常模式所抑制的差异来调整分析层，利用深度循环学习。来自不同模式的这种差异被反复用于训练学习模型以最大化识别准确率。所提出的方法实现了16.77%的高准确率、10.55%的高精度和7.69%的高模式验证率。它分别将差异和验证时间减少了12.08%和12.02%。
