Application of artificial intelligence in early diagnosis of Parkinson disease
Code: G-1629
Authors: Sarah Mohammaditirabadi ℗, سجاد سپهری راد, Mehran Habibi Rezaei *
Schedule: Not Scheduled!
Tag: Cancer Diagnosis & Treatment
Download: Download Poster
Abstract:
Abstract
Background and aims: Parkinson's disease (PD) is a common neurological disorder that mainly affects motor neuron. The disease occurs due to the death of nerve cells, followed by a decrease in dopamine levels in the brain; as a result, it impairs the person's motor function. Unfortunately, there is no definitive cure for PD to date; therefore, patients with Parkinson's disease rely on early diagnosis, screening, and appropriate treatments to slow the progression of the disease. Artificial intelligence has recently impacted healthcare and clinical diagnoses, and has also played a promising role in the early diagnosis of PD. The purpose of this study is to respond to whether artificial intelligence tools could help in the early diagnosis of PD and thereby slow down the progression of the disease? Methods: In the present review article, we studied articles published in databases to date using the key. Results: Recent studies have shown that efforts have been made for early detection of PD using artificial intelligence, particularly using machine learning and deep learning algorithms. A type of this strategy is the PWP audio recording. This method emphasizes the correlation between sound and speech (as a non-invasive biomarker) and diagnosis. DL algorithms have also been developed in the fields of healthcare, computer vision, image recognition, etc. one of the advantages of DL methods is the way they display data. When PD symptoms are considered as input to the AI model, and due to lack of sample size, poor validation, clinical evaluation, and lack of big data configuration, it is important to ensure that the AI system is reliable, accurate, and has minimal AI bias. Conclusion: In conclusion, developing and investigating ways to detect PD in a new window. Symptoms of the disease may not appear for years, and diagnosing the disease before symptoms become can significantly improve the patient's quality of life. In many of the studied cases, DL algorithms like Convolutional Neural Networks in medical image analysis outperform traditional and manual methods. in diagnosing disease; Because they can automatically retrieve and evaluate features.
Keywords
Parkinson’s Disease, Artificial Intelligence, Machine Learning
