Application of AI Technology for the Diagnosis and Treatment of Geriatric Diseases

Alzheimer Disease, Geriatric Diseases, Nervous System Diseases, Parkinson Disease

Parkinson Disease, Alzheimer Disease, Nervous System Diseases, geriatric diseases, AI, Deep learning

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.

No related papers found yet.

China