With the wide application of deep learning technology in disease diagnosis, especially the outstanding performance of convolutional neural network (CNN) in computer vision and image processing, more and more studies have proposed to use this algorithm to achieve the classification of Alzheimer’s disease (AD), mild cognitive impairment (MCI) and normal cognition (CN). This article systematically reviews the application progress of several classic convolutional neural network models in brain image analysis and diagnosis at different stages of Alzheimer’s disease, and discusses the existing problems and gives the possible development directions in order to provide some references.
ObjectiveTo systematically review the diagnostic value of FDG-PET, Aβ-PET and tau-PET for Alzheimer ’s disease (AD).MethodsPubMed, EMbase, The Cochrane Library, CNKI, WanFang Data, VIP and CBM databases were electronically searched to collect diagnostic tests of FDG-PET, Aβ-PET and tau-PET for AD from January 2000 to February 2020. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies; then, meta-analysis was performed by Meta-Disc 1.4 and Stata 14.0 software.ResultsA total of 31 studies involving 3 718 subjects were included. The results of meta-analysis showed that, using normal population as control, the sensitivity/specificity of FDG-PET and Aβ-PET in diagnosing AD were 0.853/0.734 and 0.824/0.771, respectively. Only 2 studies were included for tau-PET and meta-analysis was not performed.ConclusionsFDG-PET and Aβ-PET can provide good diagnostic accuracy for AD, and their diagnostic efficacy is similar. Due to limited quality and quantity of the included studies, more high quality studies are required to verify the above conclusions.
Biological markers play a pivotal role in the early and accurate diagnosis of Alzheimer’s disease, enabling precise identification and monitoring of therapeutic interventions. The detection of central β-amyloid and Tau proteins has become an indispensable tool in clinical trials. Recent years have witnessed substantial progress in the development of readily accessible and cost-effective blood biomarkers. This comprehensive article provides a comprehensive overview of the clinical applications of blood biomarkers, encompassing β-amyloid, phosphorylated Tau protein, neurofilament light chain protein, and glial fibrillary acidic protein, all of which have demonstrated clinical relevance in Alzheimer’s disease diagnosis. Notably, phosphorylated Tau protein exhibits superior diagnostic efficacy. The incorporation of blood biomarkers facilitates early screening, accurate diagnosis, and efficacious treatment of Alzheimer’s disease.
ObjectivesTo systematically review the efficacy and safety of butylphthalide soft capsule with routine treatment for Alzheimer’s disease (AD).MethodsDatabases including CNKI, WanFang Data, VIP, CBM, PubMed, EMbase, and The Cochrane Library were electronically searched from September 2002 to July 2018 to collect randomized controlled trials of butylphthalide soft capsule with routine treatment for Alzheimer’s disease. The trial was screened based on inclusion and exclusion criteria, and the methodological quality of the included trial was assessed. Meta-analysis was then performed by Revman 5.3 software.ResultsA total of 8 studies involving 576 patients were included. The butylphthalide soft capsule group included 283 patients and the control group included 293 patients. The result of meta-analysis showed that butylphthalide soft capsule with routine treatment (Donepezil hydrochloride or Memantine or EGb761) significantly improved the score of mini-mental state examination (MMSE) (MD=3.19, 95% CI 2.69 to 3.69, P<0.001) and clinical efficacy (RR=1.36, 95%CI 1.21 to 1.53, P<0.001). There was no significant difference in number of adverse events between the butylphthalide group and the control group (RR=1.13, 95%CI 0.77 to 1.67, P=0.52).ConclusionsBased on the routine treatment, combining with butylphthalide soft capsule can further facilitate cognitive function of AD and improve clinical efficacy. At the same time, no increase in adverse reactions has been found. However, due to the low quality of the included studies, more high quality randomized controlled trials are required to verify the results.
The electroencephalogram (EEG) signal is a general reflection of the neurophysiological activity of the brain, which has the advantages of being safe, efficient, real-time and dynamic. With the development and advancement of machine learning research, automatic diagnosis of Alzheimer’s diseases based on deep learning is becoming a research hotspot. Started from feedforward neural networks, this paper compared and analysed the structural properties of neural network models such as recurrent neural networks, convolutional neural networks and deep belief networks and their performance in the diagnosis of Alzheimer’s disease. It also discussed the possible challenges and research trends of this research in the future, expecting to provide a valuable reference for the clinical application of neural networks in the EEG diagnosis of Alzheimer’s disease.
Alzheimer's disease is a common neuro-degenerative disease. The clinical diagnosis mainly depends on the patient's complaint, the score of mini-mental state examination and Montreal cognitive assessment scale, and the comprehensive judgment of MRI and other imaging examinations. Retina is homologous to brain tissue, and their vascular systems have similar physiological characteristics to small blood vessels in the brain. Numerous studies found that the thickness of retinal nerve fiber layer, visual function, retinal blood vessels and retinal oxygen saturation were changed in AD patients to different degrees. To explore the formation mechanism and significance of ocular fundus changes in AD patients will be helpful to select specific, sensitive and simple methods for early observation and evaluation of AD.
Objective To analyze the characteristic and temporal trend in mortality and disease burden of Alzheimer’s disease (AD) and other forms of dementia in Guangzhou from 2008 to 2019, and estimate the disease burden attributable to smoking to provide evidence for promoting local health policy of prevention and intervention of dementia. Methods Based on the data of Guangzhou surveillance point of the National Mortality Surveillance System (NMSS), the crude mortality, standardized mortality, years of life lost (YLL) of AD and other dementia were calculated. The indirect method was used to estimate years lived with disability (YLD) and disability-adjusted life years (DALY).The distribution and changing trends of the index rates were compared from 2008 to 2019 using Joinpoint Regression Program. Based on the data of Guangzhou Chronic Disease and Risk Factors Monitoring System in 2013, the indexes of disease burden of AD and other forms of dementia attributable to smoking in 2018 was calculated. Results The standardized mortality rate, YLL rate, YLD rate and DALY rate of AD and other forms of dementia in Guangzhou increased from 0.45/100 000, 0.05‰, 0.02‰ and 0.07 ‰ in 2008 to 1.28/100 000, 0.15‰, 0.07‰ and 0.22‰ in 2019, respectively. The average annual changing trend was statistically significant (AAPC=11.30%, 13.09%, 13.09%, 13.09%, P<0.001）. In most years, the mortality and disease burden of women were higher than those of men, but men had higher growing trend than women in standardized mortality rate, YLL rate, YLD rate and DALY rate from 2008 to 2019, with a slower growing speed after the year 2012.The disease burden of dementia attributable to smoking in men was significantly higher than that in women. Conclusion The mortality and disease burden of AD and other forms of dementia in Guangzhou have dramatically increased over the past twelve years. Intervention against modifiable factors such as smoking, and prevention and screening for dementia in key populations should be strengthened. Support policies for dementia care management should be adopted to reduce the disease burden caused by premature death and disability.
We in the present research proposed a classification method that applied infomax independent component analysis (ICA) to respectively extract single modality features of structural magnetic resonance imaging (sMRI) and positron emission tomography (PET). And then we combined these two features by using a method of weight combination. We found that the present method was able to improve the accurate diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Compared AD to healthy controls (HC): the study achieved a classification accuracy of 93.75%, with a sensitivity of 100% and a specificity of 87.64%. Compared MCI to HC: classification accuracy was 89.35%, with a sensitivity of 81.85% and a specificity of 99.36%. The experimental results showed that the bi-modality method performed better than the individual modality in comparison to classification accuracy.
Objective To observe the changes of retinal nerve fiber layer (RNFL) thickness in patients with Alzheimer's disease (AD). Methods Twenty eyes of 40 patients with mild and (or) moderate AD confirmed by clinical examination (AD group) were included in the study. There were 11 males and 9 females with an average age of (72.75±8.25) years. Age and gender-matched normal 20 objectives were in the normal control group. Among them, there were 11 males and 9 females with a mean age of (71.05±7.08) years. There was no significant difference in gender composition, age and intraocular pressure between the two groups (P＞0.05). There were significant differences in visual acuity, cup disc ratio and mini-mental state examination score (P＜0.05). All eyes underwent high-resolution optical coherence tomography (OCT) examination. With a diameter of 3.4 mm and a center on the center of the optic disc, circular fast scans on optic disc were performed to obtain an average disc RNFL thickness, signal threshold ＞6. Computer image analysis system was used to measure the RNFL thickness from superior, inferior, temporal and nasal quadrants, and the average RNFL thickness. The changes of RNFL thickness between the two groups and between different eyes of the same group were compared. Results Compared with the normal control group, the average (t=5.591), superior (t=8.169, 8.053) and inferior (t=12.596, 11.377) thickness of RNFL in both eyes in AD group were thinner, the differences were significant (P＜0.05); the temporal (t=1.966, 0.838）and nasal (t=2.071, 0.916) thickness of RNFL in both eyes of AD group were thinner, but the difference was not statistically significant (P＞0.05). There was no significant difference of the mean and different quadrant RNFL thickness between different eyes in AD group and normal control group (AD group: t=0.097, 0.821, 0.059, 0.020, 0.116; normal control group: t=0.791, 1.938, 1.806, 2.058, 1.005; P＞0.05). Conclusion The RNFL thickness around the optic disc in AD patients is thinner; This occurs first in superior and inferior quadrants of the optic disc.
Objective To evaluate the relationship between genetic polymorphism of ApoE and Alzheimer’s disease in Chinese population. Methods Such databases as PubMed, EBSCO, CNKI, CBM, and WangFang Data were searched from their establishment to December 2010 to collect the literature about the relationship between genetic polymorphism of ApoE and Alzheimer’s disease in Chinese population. RevMan 5.0 was adopted to conduct consistency check and data merging, and to evaluate publication bias. Results ApoEε4 was the risky allele (Plt;0.05) in Chinese population, and its pooled odds ratios and 95%CI was 3.53 (2.49 to 5.00). ApoEε3 was the protective alleles (Plt;0.05) in Chinese population, and its pooled odds ratios and 95%CI was 0.52 (0.40 to 0.68). ApoEε4/ε4, ApoEε4/ε3, and ApoEε4/ε2 were the risky genotypes (all Plt;0.05) in Chinese population, and their pooled odds ratios and 95%CI were 10.17 (4.25 to 24.19), 2.57 (2.04 to 3.25), and 1.94 (1.13 to 3.34), respectively. ApoEε3/ε3 was the protective genotype (Plt;0.05) in Chinese population, and its pooled odds ratios and 95%CI was 0.67 (0.57 to 0.77). Conclusion In Chinese population, some ApoE alleles and genotypes are associated with Alzheimer’s disease.