application of ai technology for the diagnosis and treatment of geriatric diseases

Conditions: Parkinson Disease; Alzheimer Disease; Nervous System Diseases; Geriatric Diseases

Summary: 1) characteristics of handwriting, gait, speech, eye movements, biological samples (blood, urine, stool, saliva, etc.), images, eeg, and other relevant markers in patients with alzheimer's disease. (2) characteristics of handwriting, gait, language, eye movement, biological samples (blood, urine, stool, saliva, etc.), imaging, eeg, and other relevant markers in patients with parkinson's disease. (3) characteristics of handwriting, gait, language, eye movement, biological samples (blood, urine, stool, saliva, etc.), images, eeg, and other relevant markers in patients with other neurological disorders. (4) characteristics of handwriting, gait, language, eye movement, biological samples (blood, urine, stool, saliva, etc.), images, eeg and other relevant markers in elderly patients.

1. study variables (factors) and measurements 1. all enrolled subjects collected data including general clinical information, pd patients completed motor function assessment and non-motor function assessment, ad patients completed cognitive function assessment and mood-sleep assessment, patients with other neurological disorders perfected disease-related clinical information and scale assessment, and healthy elderly and young controls perfected general clinical information and scale screening. 2. all selected subjects complete handwriting recording, gait assessment, language entry and eye movement recording, and collect biological samples (blood (3 ml/case), urine (3 ml/case), stool (2 g/case), saliva (3 ml/case) and other clinical test results), imaging, eeg and other relevant markers for the study of diagnostic and treatment plan and correlation analysis of ai technology. 1. motor symptom assessment: updrs score 2. non-motor symptom assessment: mini-mental state examination (mmse), montreal cognitive assessment (moca), hamilton anxiety scale (hamd), and hamilton anxiety diagnostic scale (had). hamilton anxiety scale (hamd), hamilton depression scale (hdmd), sf-36. 2. sample size 100 patients with primary parkinson's disease were included, 100 patients with alzheimer's disease were included, 100 patients with other neurological disorders were included, 80 healthy elderly controls, and 80 healthy young controls. 3 data collection and management data collection was firstly collected by applying the paper version of the case report form, and then entered into the computer by a specialized person. 4. statistical analysis methods for continuous variables, statistical descriptions were performed using mean ± standard deviation, and for categorical variables, frequency and rate (%) were used. for comparison of parameters between two groups, t-test or rank sum test was used for continuous type variables and chi-square test was used for categorical variables. for one-way correlation analysis, pearson or spearman correlation analysis was used. for multifactor analysis, generalized linear model or logistic regression model was used. all of the above parameters were considered statistically significant at p\<0.05. 5. quality control 1. clinical data disease diagnosis: determined by two parkinson's and movement disorders group deputy chief physicians and above; 2. scale assessment: adjudicated by two scale-trained neurologists; 3. statistics: performed by clinical statisticians. 6. research feasibility analysis qilu hospital of shandong university is a one-stop diagnosis and treatment center for parkinson's disease and a clinical research center of geriatrics in shandong province, with a large patient attendance group and clear clinical significance; our research center is equipped with handwriting analyzer, gait analyzer, speech and eye movement capture analyzer, and has analyzers for collecting biological samples, imaging information and eeg information, which has the basis for further in-depth research.

Eligibility criteria:
1. inclusion criteria 1.1 a total of 100 patients with primary parkinson's disease were enrolled. the inclusion criteria were as follows: patients with primary parkinson's disease, based on the 2015 mds diagnostic criteria for parkinson's disease. disorder (mmse score \< 24); (3) those with severe mental illness who are difficult to cooperate. 1.2 a total of 100 patients with alzheimer's disease were enrolled. the inclusion criteria were as follows: patients with alzheimer's disease, based on the iwg-2 clinical diagnostic criteria. 1.3 neurodegenerative diseases (non-pd and non-ad) group (n = 100) : meeting the diagnostic criteria of the corresponding diseases. 1.4 eighty healthy elderly controls were included. the inclusion criteria were as follows: ① voluntarily participated in this study and signed the informed consent; ② age and gender were matched with the case group. 1.5 80 young healthy controls, inclusion criteria: ① voluntarily participated in this study and signed the informed consent; ② gender was matched with the case group, and the age was less than 60 years old. 2. exclusion criteria 2.1 a total of 100 patients with pd were enrolled. exclusion criteria: ① patients with other central nervous system diseases, such as cerebrovascular diseases, brain trauma, inflammatory diseases of the central system, intracranial tumors and after neurosurgery; ② patients with various types of cognitive impairment (mmse score \< 24); (3) those with severe mental illness who are difficult to cooperate. 2.2 a total of 100 patients with alzheimer's disease were enrolled. the exclusion criteria were: ① patients with other central nervous system diseases, such as cerebrovascular diseases, brain trauma, central system inflammatory diseases, intracranial tumors and after neurosurgery; ② patients with severe mental illness who are difficult to cooperate. 2.3 neurodegenerative diseases (non-pd non-ad) group (n = 100), exclusion criteria: ① patients with other central nervous system diseases, such as cerebrovascular diseases, brain trauma, central system inflammatory diseases, intracranial tumors and after neurosurgery; ② patients with severe mental illness who are difficult to cooperate. 2.4 eighty healthy elderly controls were included. the exclusion criteria were as follows: ① history of chronic diseases related to the study subjects, such as cerebrovascular diseases, brain trauma, inflammatory diseases of the central system, intracranial tumors and neurosurgery; ② patients with various types of cognitive impairment (mmse score \< 24); ③ poor compliance. 2.5 80 young healthy controls, exclusion criteria: ① history of chronic diseases related to the study subjects, such as cerebrovascular diseases, brain trauma, inflammatory diseases of the central system, intracranial tumors and neurosurgical surgery; ② patients with various types of cognitive impairment (mmse score \< 24); ③ poor compliance.

Keywords: parkinson disease alzheimer disease nervous system diseases geriatric diseases ai deep learning