Identification of Alzheimer’s Disease by Imaging: A Comprehensive Review
Abstract
:1. Introduction
1.1. Image Processing and Its Applications in Disease Detection
1.2. Evidence of Alzheimer’s Disease Influencing Central Nervous System
1.3. Evidence of Alzheimer’s Disease Influencing the Visual System at the Molecular Stage
1.4. Neurochemistry Deficiency in the AD Retina
2. Non-Invasive Imaging of the Retina
2.1. Image Reconstruction-Recognizing Objects over Background Luminance
2.2. Image Filtering-Biomarkers for AD Diagnosis Based on Structural Changes in the Retina, Optic Nerves, and Lenses
2.3. Image Segmentation-Optic Nerve Fibre Imaging and AD
2.4. Image Registration
3. Discussion
4. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Year, Authors [Ref] | Significant Finding |
---|---|
Yan et al., 2021 [19] | rNFL and retinal vascular density were considerably lower in persons with Alzheimer’s disease than in healthy controls. Some patients’ cognitive dysfunction has been linked to a decrease in retinal vessel density. |
Mei et al., 2020 [20] | Investigation of the link between the retinal and cerebral Aβ content in APP mice. In the retina, curcumin can stain Aβ, but it has been found to suppress the level of this staining compound. |
Chua et al., 2020 [21] | AD patients’ superficial and deep capillary plexus showed considerably decreased vascular density compared with healthy controls. |
Wu et al., 2020 [22] | OCT-A on AD patients revealed significantly lower deep retinal capillary plexus micro vascular densities than on matched controls. |
Kim et al., 2019 [23] | Both individuals with serious Alzheimer’s disease and those with mild to medium Alzheimer’s disease had thinning of the rNFL assessed by OCT. |
Zabel et al., 2019 [24] | Comparing patients with AD to healthy controls, their rNFL was significantly thinner, but this was not statistically significant. SD-OCT observed changes that were not specific. |
Ko et al., 2018 [18] | Researchers found that patients without the neurodegenerative disease had lower cognitive function and the possibility of cognitive reduction as a result of the presence of thinning rNFL. |
Sanchez’ et al., 2018 [25] | For both cognitively healthy and Alzheimer’s disease persons, the thickness of the peripapillary rNFL is the same. |
Koronyo et al., 2017 [26] | Curcumin fluorochrome was used to detect a deposit at the retinal level. Imagery of the retina A was captured using solid lipid curcumin and an adapted scanning laser eye scope. |
Den Haan et al., 2017 [27] | Mild cognitive impairment (MCI) and Alzheimer’s disease (AD) patients exhibit reduced retinal nerve fibre zone and macular thickness compared with healthy controls, respectively. |
Mutlu et al., 2017 [28] | Gray and white matter volumes were found to be less when the RNFL and GCL were smaller as measured by OCT. |
Casaletto et al., 2017 [16] | On OCT, overall macular and macular ganglion cell volumes were shown to decrease. |
Jiang et al., 2016 [29] | A meta-analysis of five studies with few sample sizes was unable to make any conclusions about pathological retinal degeneration. |
Pillai et al., 2016 [30] | There is no variation in rNFL, GCL, or macular volume on OCT in Alzheimer’s disease patients compared with healthy control subjects. |
Koronyo Hamaoui et al., 2011 [4] | It was discovered that systemic administration of curcumin caused the development of retinal postmortem eyes of Alzheimer’s patients to show a buildup of plaques. These plaques can be seen and accumulated at a primitive phase of illness. |
Patient | Mean | NS | N | NI | TI | T | TS |
---|---|---|---|---|---|---|---|
1 | 90.0 ± 8.5 | 75.0 ± 1.4 | 46.0 ± 9.9 | 111.5 ± 21.9 | 143.5 ± 9.2 | 73.0 ± 1.4 | 153.0 ± 17.0 |
2 | 102.0 ± 8.5 | 105.5 ± 3.5 | 60.5 ± 6.4 | 112.0 ± 7.1 | 166.5 ± 13.4 | 77.5 ± 17.7 | 157.5 ± 0.7 |
3 | 100.0 ± 0.0 | 99.0 ± 4.2 | 99.5 ± 0.7 | 127.5 ± 2.1 | 130.5 ± 2.1 | 61.5 ± 0.7 | 122.5 ± 0.7 |
4 | 99.0 | 124.0 | 92.0 | 127.0 | 134.0 | 58.0 | 108.0 |
5 | 110.0 ± 5.7 | 116.5 ± 4.9 | 81.0 ± 5.7 | 129.0 ± 4.2 | 148.5 ± 2.1 | 86.5 ± 13.4 | 151.0 ± 5.7 |
6 | 95.5 ± 4.9 | 110.5 ± 16.3 | 66.0 ± 2.8 | 91.5 ± 21.9 | 143.5 ± 23.3 | 68.0 ± 1.4 | 148.5 ± 14.8 |
7 | 108.0 ± 1.4 | 111.5 ± 16.3 | 87.0 ± 0.0 | 125.5 ± 4.9 | 147.5 ± 3.5 | 77.5 ± 4.9 | 139.0 ± 0.0 |
8 | 85.0 ± 1.4 | 85.5 ± 4.9 | 55.0 ± 1.4 | 73.0 ± 5.7 | 129.0 ± 9.9 | 81.5 ± 20.5 | 118.5 ± 7.8 |
9 | 90.5 ± 2.1 | 91.5 ± 7.8 | 67.0 ± 2.8 | 80.5 ± 2.1 | 147.0 ± 4.2 | 66.0 ± 0.0 | 139.0 ± 11.3 |
10 | 93.0 ± 0.0 | 110.0 ± 0.0 | 66.5 ± 0.7 | 124..0 ± 5.7 | 141.5 ± 7.8 | 59.5 ± 0.7 | 114.5 ± 0.7 |
Control | Mild AD | Moderate AD | Mild AD vs. Control | Moderate AD vs. Control | Mild AD vs. Moderate AD | |||||
---|---|---|---|---|---|---|---|---|---|---|
(n = 40) | (n = 39) | (n = 18) | % Difference | p-Value | % Difference | p-Value | % Difference | p-Value | ||
Visual Acuity (dec) | 1.00 ± 0.10 | 0.90 ± 0.20 | 0.90 ± 0.30 | −10.00 | <0.001 ** | −10.00 | 0.003 ** | 0.00 | 0.921 | |
Constrast Sensitivity (cpd) | 3 | 1.63 ± 0.29 | 1.49 ± 0.46 | 1.49 ± 0.29 | −8.59 | <0.001 ** | −8.59 | 0.009 ** | 0.00 | 0.422 |
6 | 1.84 ± 0.44 | 1.70 ± 0.33 | 1.55 ± 0.28 | −7.61 | <0.001 ** | −15.76 | <0.001 ** | −8.82 | 0.373 | |
12 | 1.54 ± 0.29 | 1.25 ± 0.32 | 1.16 ± 0.30 | −18.83 | <0.001 ** | −24.68 | <0.001 ** | −7.20 | 0.599 | |
18 | 1.10 ± 0.29 | 0.64 ± 0.56 | 0.64 ± 0.57 | −41.82 | <0.001 ** | −41.82 | <0.001 ** | 0.00 | 0.781 | |
Number of Errors | ||||||||||
Total | 5.0 ± 5.0 | 7.0 ± 4.0 | 12.00 ± 8.0 | 40.00 | 0.034 * | 140.00 | <0.001 ** | 71.43 | 0.001 ** | |
Tritan | 2.0 ± 2.0 | 3.0 ± 3.0 | 6.0 ± 5.0 | 50.00 | 0.002 ** | 200.00 | <0.001 ** | 100.00 | 0.017 * | |
Deutan | 1.0 ± 3.0 | 3.0 ± 3.0 | 5.0 ± 3.0 | 200.00 | 0.003 ** | 400.00 | <0.001 ** | 66.67 | 0.002 ** | |
PDT | 14.00 ± 2.00 | 12.00 ± 4.00 | 11.50 ± 3.0 | −14.29 | <0.001 ** | −17.86 | <0.001 ** | −4.17 | 0.650 |
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T., P.; V., S. Identification of Alzheimer’s Disease by Imaging: A Comprehensive Review. Int. J. Environ. Res. Public Health 2023, 20, 1273. https://doi.org/10.3390/ijerph20021273
T. P, V. S. Identification of Alzheimer’s Disease by Imaging: A Comprehensive Review. International Journal of Environmental Research and Public Health. 2023; 20(2):1273. https://doi.org/10.3390/ijerph20021273
Chicago/Turabian StyleT., Prasath, and Sumathi V. 2023. "Identification of Alzheimer’s Disease by Imaging: A Comprehensive Review" International Journal of Environmental Research and Public Health 20, no. 2: 1273. https://doi.org/10.3390/ijerph20021273