ABSTRACT
Recent advances in artificial intelligence (AI) have shown great promise in the diagnosis, prediction, treatment plans, and monitoring of neurodegenerative disorders. AI algorithms can analyze huge quantities of data from numerous sources, including medical images, quantifiable proteins in urine, blood, and cerebrospinal fluid (CSF), genetic information, clinical records, electroencephalography (EEG) signals, driving behaviors, and so forth. Alzheimer's disease (AD) is one of the most common neurodegenerative disorders that progressively damage cognitive abilities and memory. This study specifically explores the possible application of AI in the diagnosis, prediction, monitoring, biomarker or drug discovery, and classification of AD.
ABSTRACT
INTRODUCTION: Congenital heart diseases are of immense importance and also a high prevalence. Contributing factors to developing these defects have not been abundantly studied. Therefore, the current study was conducted aiming at determining the effective factors on Congenital Heart Disease (CHD) in newborn infants of Northwest Iran. METHODS: A case-control study was carried out in North-West of Iran from 2002 to 2012 and a total of 473 infants entered the study. Required data were obtained through check lists completed by the information of hospital records and interview with mothers of 267 newborn infants with CHD together with medical records of mothers as the case group, and 206 medical records of healthy infants at the same period all together with those of their mothers as the control group. The obtained data were statistically analyzed using descriptive statistical methods, T-test, Spearman's correlation coefficient, and Multi-variable Logistic Regression Model (OR with 95% CI), using SPSS.19. In the present study, P value less than 0.05 was considered statistically significant. RESULTS: Based on the results of univariable analyses, the number of previous cesarean sections, past medical history of diseases, gestational age (GA), fetal weight at birth, diastolic blood pressure, fetal heart rate, pulse rate, fetal hemoglobin and hematocrit levels, and fetal head circumference at birth have significant relationship with incidence of congenital abnormalities (P<0.05). Family history, past cesarean sections history, past medical history and GA had significant relationship with CHD incidence. CONCLUSION: Based on the results of present study, in order to control and reduce the cases of CHD, it is crucial to make proper decisions and implement policies for reducing cesarean cases, lowering consanguineous marriages, providing proper pre-marriage counseling, prompt treatment of mothers' illnesses, improving pregnancy health care and mothers' health status for the purpose of better well-being of newborn infants.
