Abstract
Background: About 50 million people worldwide suffered from dementia in 2018—two-thirds of those with Alzheimer’s disease (AD). By 2050, this number is expected to rise to 152 million—which is slightly larger than the country of Russia. About 90% of these people are over the age of 65, but early-onset dementia can occur at younger ages. Early detection is imperative to expedient treatment, which can improve outcomes over the span of diagnosis. Objectives: To conduct a meta-analysis of similar studies along with a systematic literature review to hasten the development of clinical practice guidelines to assist clinicians in the diagnosis of AD. We analyzed data points in each article published over the last 10 years to meet this objective: cost, efficiency, accuracy, acceptability (by physician and patient), patient satisfaction, and barriers to adoption. Methods: Four research databases were queried (PubMed, CINAHL Ultimate, Web of Science, and ScienceDirect). The review was conducted in accordance with a published protocol, the Kruse Protocol, and reported in accordance with PRISMA (2020). Results: Ten interventions were identified to help diagnose AD among older patients, and some involved a combination of methods (such as MRI and PET). The average sample size was 320.32 (SD = 437.51). These 10 interventions were identified as accurate, non-invasive, non-stressful, inexpensive, convenient, and rapid. Only one intervention was identified as statistically ineffective, and this same intervention was used effectively in other studies. The barriers identified were cost, training, expense of travel, and required physical presence of patient. The weighted average sensitivity was 85.16%, specificity was 88.53, and the weighted average effect size was 0.7339 (medium). Conclusion: Innovation can accurately diagnose AD, but not all methods are successful. Providers must ensure they have the proper training and familiarity with these interventions to ensure accuracy in diagnosis. While the physical presence of the patient is often required, many interventions are non-invasive, non-stressful, and relatively inexpensive.
1. Introduction
1.1. Rationale
Prior to the pandemic, Alzheimer’s disease (AD) was growing into the largest fear for older adults. In the US, AD is fatal for more people than breast cancer and prostate cancer, and it is the number one cause of death in Great Britain. The 2018 prevalence of dementia was 50 million, but it is expected to surpass 150 million by 2050. Alzheimer’s disease accounts for about two-thirds of the dementia population [1].
The disease has no cure: there are only about 10 drugs approved to manage AD’s symptoms. The disease manifests itself in the form of plaque on the brain that affects the communication of about 100 billion neurons, which degrades and ultimately destroys these neurons. At first, this destruction manifests itself in simple forgetfulness of recently learned things, but over time, the destruction becomes more severe, affecting speech, motor movement, and long-term memory [2]. Medicine today does not fully understand the etiology and pathogenesis of AD [3]. Medicine can treat but cannot prevent or cure the disease [4,5]. For these reasons, many healthcare professionals have promoted research related to the detection of this disease because more timely diagnoses could potentially reduce the overall burden faced by society [2].
For a short period, diagnosis could only be confirmed through autopsy; however, health information technology (HIT) has made strides toward diagnosis while the patient lives. Optical and electrochemical biosensors are being developed to sense biological responses and identify biomarkers for AD [6]. Canada recently published a white paper on the early diagnosis of AD. It included questionnaires and cognitive testing, diagnosis of neurodegeneration through MRI, and established biomarkers for neurodegeneration and Alzheimer’s pathology through imaging and biomarkers [7]. Wearables and other remote sensors track behavior and can help detect neurodegenerative diseases like AD in a timely and economical manner [8].
A systematic review was published in 2022 that evaluated apps, sensors, and virtual reality developed to help with timely diagnosis, management, and treatment of symptoms. Eight studies were analyzed from the last decade to identify solutions: deficits in finger dexterity, memory retrieval, and alertness and mood improvement [9]. This study did not identify how leveraging health information technology can diagnose AD.
A systematic review was published in 2021 that analyzed health monitoring and artificial intelligence (AI) for deep learning. This review found AI can be used in the early detection of chronic diseases. Cloud computing was a catalyst for this innovation, and the integration of a blockchain framework improved data security to help prevent the misuse of patient data [10]. This study did not identify how leveraging health information technology can diagnose AD.
1.2. Objectives
The purpose of this review is to conduct a meta-analysis of similar studies along with a systematic literature review to hasten the development of clinical practice guidelines to assist in the diagnosis of AD. To meet this objective, we analyzed several data points in each article published over the last 10 years. The intention was to analyze studies with strong methodological approaches to identify trends of effectiveness. This approach should give providers and families of those suffering from AD a pathway to a non-invasive, cost-effective technique to confirm an AD diagnosis. Findings from this review will hasten practice guidelines for rapid, inexpensive, and accurate diagnosis of AD.
2. Methods
2.1. Eligibility Criteria
To be eligible for consideration for this systematic literature review, studies had to use as participants older adults who were undergoing a diagnosis for AD, in a study published in the last ten years, published in a peer-reviewed journal, using strong methodologies. A randomized controlled trial (RCT) was preferred, but we also allowed quasi-experimental, mixed methods, quantitative, and qualitative. A wide variety of interventions were preferred to calculate an overall effect size. This included physical markers, digital markers, and telemedicine interventions. Artificial intelligence interventions such as Bayesian networks and machine learning were also accepted. Machine learning often uses test sets to train the computer in what a positive diagnosis looks like, such as structural equation modeling (SEM). Then, the training is used on a larger set of data to identify the disease. Other systematic reviews were not included in the analysis so as not to confuse the results (because systematic reviews already reported on results from studies that may also be counted in our analysis). Studies must use humans as subjects, be published in the English language, and have a full-text version available for download to enable data extraction. We did not allow gray literature, editorials, protocols, or any other article that did not report study results. This review is registered with PROSPERO: ID 350266. The protocol used for this meta-analysis can be found at Kruse CS. Writing a systematic review for publication in a health-related degree program. JMIR research protocols. 2019 Oct 14;8(10):e15490 [11].
2.2. Information Sources
We queried four research databases PubMed (MEDLINE), CINAHL Ultimate (excluding MEDLINE), Web of Science (excluding MEDLINE), and ScienceDirect (excluding MEDLINE). These databases were searched between 25 July 2022 and 26 July 2022. MEDLINE was excluded from all databases except PubMed to help eliminate duplicates.
2.3. Search Strategy
We used the Medical Subject Headings (MeSH) of the U.S. Library of Medicine to create a Boolean search string combining key terms listed in the literature: “Alzheimer Disease” AND diagnosis AND (technology OR “artificial intelligence” OR mhealth). We used the same search strategy in all databases and employed the same filters, where available.
2.4. Selection Process
We searched for key terms in all databases, filtered results, and screened abstracts for applicability in accordance with the Kruse Protocol [11]. At least two reviewers screened all abstracts and rejected any study that did not report results (e.g., protocols, editorials, etc.). Three consensus meetings were held to determine which articles would be analyzed, which data-extraction items were significant measurements of effectiveness, and what observations should become themes. A kappa statistic was calculated. Results were reported in accordance with the PRISMA 2020 standard [12].
2.5. Data Collection Process
We used a standardized Excel spreadsheet as a data extraction tool, collecting key data items in each article. This spreadsheet was standardized in the published protocol, and it provides fields of value for administrators, clinicians, and policy makers [11].
2.6. Data Items
In accordance with the published protocol, we collected the following fields of data at each step: PICOS (Participant demographics, Intervention specifics, Comparison of results between intervention and control, specific medical Outcomes, and Study design), observed bias, effect size, sensitivity, specificity, F1, country of origin, statistics used, strength of evidence, quality of evidence, measures of effectiveness (cost, efficiency, invasiveness, etc.), and barriers to adoption. A narrative analysis was conducted to identify themes in the literature [13].
2.7. Study Risk of Bias Assessment
As each article was analyzed, reviewers noted items of bias (selection bias, sample bias, etc.). We assessed the quality of each study using the John’s Hopkins Nursing Evidence Based Practice tool (JHNEBP) [14]. Instances of bias helped interpret the results because bias can limit external validity.
2.8. Effect Measures
Because reviewers accepted mixed methods and qualitative studies, they were unable to standardize summary measures, as would be performed in a meta-analysis. Measures of effect were summarized in tables where they were reported. Odds ratios, correlation coefficients, and the F1 were converted to Cohen’s d [15,16]. A weighted average effect size, group specificity, and sensitivity were reported.
2.9. Synthesis Methods
Sensitivity, specificity, F1, and effect size were collected from studies. A meta-analysis of this data was performed. Reviewers also performed a thematic analysis of the data collected from studies. Same or similar observations were combined into themes. Themes and other observations were tabulated into affinity matrices.
2.10. Reporting Bias Assessment
The overall ratings of quality and strength of evidence, identified using the JHNEBP, provided us with an assessment of the applicability of the cumulative evidence. Reviewers also noted instances of bias such as selection bias, sample bias, and publication bias.
2.11. Additional Analyses and Certainty Assessment
A narrative analysis converted observations into themes (common threads between articles) [13]. We calculated the frequency of occurrence and reported this in a series of affinity matrices. The frequency reporting provides confidence in the analyzed data.
3. Results
3.1. Study Selection
Figure 1 provides the study selection process to include the inclusion and exclusion criteria from the four research databases. A kappa statistic was calculated based on the levels of agreement between reviewers (k = 0.83, strong agreement). Twenty-eight studies were selected for the systematic literature review. Based on the data provided in these articles, only 15 qualified for the meta-analysis.
Figure 1.
Study selection process.
3.2. Study Characteristics
Following the PRISMA (2020) checklist, a PICOS table was created to summarize the study characteristics of the 28 studies. This is tabulated in Table 1. Of the 28 studies analyzed over the 10-year period, zero were from 2012, three were from 2013 [17,18,19], two were from 2014 [20,21], zero were from 2015, one was from 2016 [22], two were from 2017 [23,24], five were from 2018 [25,26,27,28,29], three were from 2019 [30,31,32], three were from 2020 [33,34,35], seven were from 2021 [36,37,38,39,40,41,42], and two were from 2022 [43,44]. All involved older adults, 61% were quasi-experimental, 21% were observational, and 14% were true experiments. Of those analyzed, 9/28 (32%) used positron emission tomography (PET) to examine tau (plaque) on the brain, 6/28 (21%) examined blood-based biomarkers, 4/28 (14%) used some form of telehealth (video teleconferencing, virtual reality, or telemonitoring), 3/28 (11%) used artificial intelligence or machine learning to recognize patters from MRI or PET scans, while the other six interventions stood on their own without repeats (MRI, MRI + PET, arterial pulse, RNA, EEG, and spectral domain optical coherence tomography). The average sample size was 320.32 (SD = 437.51).
Table 1.
PICOS.
3.3. Risk of Bias within and across Studies
We used the JHNEBP quality assessment tool to identify the strength and quality of the evidence. Of the group of articles analyzed, 18/28 (64%) were strength II (quasi-experimental), 6/28 (21%) were strength III (non-experimental, qualitative, or meta-analysis), and 4/28 (14%) were strength I (experimental study, RCT). Additionally, 26/28 (93%) were quality A (consistent results with sufficient sample sizes, adequate control, definitive conclusions), and 2/28 (7%) were quality B (well-defined, reproducible search strategies, consistent results with sufficient numbers of well-defined studies, and definitive conclusions). Most articles analyzed were high-quality with strong methods.
Reviewers also noted instances of bias. The most common bias identified was selection bias. Every study (100%, 28/28) analyzed in this review was conducted in one region of one country, creating a selection bias. Additionally, in 6/28 (21%) of the studies analyzed, instances of sample bias were identified because the sample was heavily skewed with one gender or race.
3.4. Results of Individual Studies
Following the Kruse Protocol, reviewers independently recorded observations from each article, commensurate with the objective. Once all reviewers had recorded their observations, a consensus meeting was held to discuss the findings. The result of this meeting was a thematic analysis: making sense of the data. When reviewers noted an observation multiple times, it was recorded as a theme to summarize the observations. These themes are tabulated in Table 2 along with the strength and quality assessments. A match of observation to theme is provided in Appendix A. A summary of other observations can be found in Appendix B.
Table 2.
Summary of analysis sorted most recent to oldest.
3.5. Results of Syntheses
In 13 articles, effect size was not reported, which eliminated them from the meta-analysis. In 15/28 studies (54%), sufficient data were provided to conduct a meta-analysis. The necessary fields were specificity, sensitivity, F1, odds ratio, Pearson’s r, and Cohen’s d for effect size. Ten articles reported sensitivity and specificity [17,19,20,26,28,30,33,38,39,42]. The overall sensitivity was 0.8516, specificity was 0.8853, precision (PPV) was 0.8388, accuracy was 0.9074, F1 = 0.8452, OR = 44.31 CI [35.53, 55.28]. The confusion matrix is illustrated in Figure 2. True positives, false negatives, true negatives, and false positives were collected, and odds ratios were calculated. All were converted to Cohen’s d [15,16]. The weighted average effect size was = 0.7339, which is a medium effect size that approaches a large effect size. This means the weighted average effect size has a strong medium effect: health information technology interventions can diagnose AD using non-invasive methods. The full table and calculations are listed in Appendix C.
Figure 2.
Confusion matrix.
3.6. Additional Analysis and Certainty of Evidence
A series of affinity matrices were created to summarize the additional analysis. Themes and observations were organized this way to reflect the probability of their occurrence in the group for analysis.
3.7. Interventions of HIT to Diagnose AD
Table 3 summarizes the interventions observed. Four themes and six individual observations were identified by the reviewers for a total of 28 occurrences in the literature. PET was identified in 9/28 (32%) of the studies analyzed [23,27,29,32,36,37,40,41,43]. PET biomarkers can identify tau plaque on the brain. This intervention is less invasive, but the confined space of the machine can cause stress. Blood-based biomarkers were identified in 6/28 (21%) of the studies analyzed [17,25,31,34,39,42]. This intervention is more invasive than imaging and causes more risk. Various forms of telehealth were implemented to diagnose AD in 4/28 (14%) of the studies analyzed [20,24,33,44]. These ranged from video teleconference (VTC) and virtual reality to other televisits to assess cognition. It is important to note that results through telemedicine were the same as traditional visits, which means a clinic can expand productivity without expanding the physical plant, and it exposes the patient to less risk or iatrogenic illness or injury. Multiple forms of artificial intelligence (Bayesian networking, machine learning) were utilized in 3/28 (11%) of the articles analyzed [19,22,28]. These interventions utilized PET biomarkers, MRI biomarkers, or arterial spin diagnostics. Six other interventions occurred only once in the literature (21%). These included MRI biomarkers only, MRI and PET biomarkers combined, arterial pulse, RNA, EEG, and spectral domain optical coherence tomography [18,21,26,30,35,38].
Table 3.
Interventions of HIT to diagnose AD.
Table 4 summarizes the effectiveness indicators observed. Five themes and three individual observations were identified by the reviewers for a total of 53 occurrences in the literature. The most identified theme was that the interventions were accurate in detecting/diagnosing AD. This occurred in 21/53 (40%) occurrences [17,18,19,20,21,22,23,24,26,28,32,34,35,36,37,38,39,40,42,43,44]. In 11/53 (21%) occurrences, the intervention was identified as non-invasive [20,21,25,26,27,28,33,35,36,38,41]. In 8/53 (15%) of the occurrences, two themes emerged: the intervention was identified as non-stressful for the patient [25,26,27,28,33,35,36,41], and the intervention was identified as inexpensive to implement [20,24,26,28,30,31,38,44]. In 2/53 (4%) of the occurrences, the intervention could be repeated without degradation of the results [26,41]. Three observations only occurred once in the literature: convenient, rapid, and ineffective [29,31,38]. Interestingly, the same intervention (PET biomarkers) was used successfully in other studies.
Table 4.
Effectiveness of intervention of HIT to diagnose AD.
Table 5 summarizes the barriers observed. Five themes and two individual observations were recorded. The most common theme was cost. This occurred in 23/91 (25%) of the occurrences [17,18,19,20,21,22,23,27,28,30,31,32,33,34,35,36,37,39,40,42,43,44]. The need for training was identified in 21/91 (23%) of the occurrences [17,18,19,20,21,22,23,24,26,27,28,30,31,32,33,34,37,38,39,40,42]. In 18/91 (20%) of the occurrences, two themes were identified: requires the physical presence of the patient, and travel expenses are incurred (correlation of these two themes is high) [17,18,21,23,27,28,30,31,32,34,35,36,37,39,40,41,42,43]. In 9/91 (10%) of the occurrences that involved a blood draw, it was identified that taking blood is both invasive and incurs risk to both the patient and the provider [17,22,30,31,34,37,39,40,42]. One intervention involved post-mortem examinations to assess neurodegeneration of brain cells, so it was noted that a procedure to diagnose the living needs to be developed [29].
Table 5.
Barriers commensurate with the adoption of HIT to diagnose AD.
4. Discussion
A meta-analysis of similar studies was conducted along with a systematic literature review to identify ways to hasten the development of clinical practice guidelines to assist in the diagnosis of AD. Of the 28 studies analyzed, 10 interventions were identified. The interventions were effective at detection of AD in all but one study [29]. PET biomarkers [23,27,29,32,36,37,40,41,43] and blood-based biomarkers [17,25,31,34,39,42] accounted for more than 50% of the interventions. PET biomarkers were also used in combination with MRI biomarkers in one study [35]. While interventions were found to be effective at detecting AD, accuracy was only noted in 40% of the interventions [17,18,19,20,21,22,23,24,26,28,32,34,35,36,37,38,39,40,42,43,44]. Interventions were found to be non-invasive, non-stressful, relatively inexpensive, repeatable without degradation of results, convenient, and rapid. While most interventions required the physical presence of the patient, there were multiple interventions, such as AI and VTC that did not [19,20,22,24,28,33,44]. AI analyzed existing data, and VTC allowed providers to detect AD with surprising accuracy.
Accurate and early diagnosis of AD is as important to the patient as it is to their family [45]. The patient needs to know about pending secondary effects such as cognitive decline, sleep disruption, or stroke incident to AD. Their families need to know of AD detection to prepare for supportive relationships and quality of life. Providers need to know about the effectiveness of the 10 interventions identified in this research and their relative level of effectiveness ( = 0.7339). The relative effectiveness of these interventions (PET biomarkers, blood-based biomarkers, telehealth, AI/machine learning, MRI biomarkers, PET biomarkers) should lead to new clinical practice guidelines (CPGs). Policy makers need to develop reimbursement mechanisms for the most effective early detection interventions. Many interventions were listed as relatively inexpensive, but medical procedures in general are beyond the out-of-pocket reach of most patients or their care givers. Researchers need to know about early detection interventions to identify more potential participants in the research of this disease to seek treatments and a cure. Many of the interventions are shown to be highly accurate, non-invasive, non-stressful to the patient, repeatable, convenient, and rapid. These considerations can also be utilized to effect change to CPGs in that they show justifiable reasons for the changes. Despite barriers to implementation such as cost, training, physical presence of the patient being necessary, and travel, the aforementioned interventions provide tools to assist in the accurate diagnosis of AD.
Future research needs to expand some of these interventions into RCTs to firmly establish new CPGs. These new CPGs need to be advertised as widely available to patients and their families. Caring for those with dementia often requires significant changes to lifestyle, habits, and spending. Families need notice to make these adjustments to prepare for their new role and new relationship with the family member.
Limitations
A limitation of this review was the length of time we looked back in the literature (10 years). We chose ten years because it should have identified the most recent advancements in diagnosis intervention. However, a longer time frame may have identified additional interventions. Another limitation is publication bias. We only looked at published works. There are possibly more successful interventions that have not yet been published. Another limitation is that we eliminated editorials and opinion articles because these did not provide empirical evidence. It is possible additional interventions could have been identified from gray literature.
5. Conclusions
Ten interventions from the literature from the last 10 years were identified as rapid and effective to diagnose AD. While some of these interventions are riskier and involve a blood draw, others are non-invasive and non-stressful to the patient. Additional research needs to expand these interventions into robust RCTs to establish new CPGs for the medical community.
Author Contributions
Conceptualization C.S.K. and M.E.M.; methodology C.S.K., M.E.M., B.H., R.W., T.C., R.S.; validation C.S.K.; formal analysis C.S.K., M.E.M., B.H., R.W., T.C., R.S.; investigation C.S.K. and M.E.M.; resources C.S.K.; data curation C.S.K., M.E.M., B.H., R.W., T.C., R.S.; writing C.S.K., M.E.M., B.H., R.W., T.C., R.S.; original draft preparation C.S.K., M.E.M., B.H., R.W., T.C., R.S.; writing, review, and editing C.S.K. and M.E.M.; visualization C.S.K.; supervision C.S.K.; project administration C.S.K. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Data from this study can be obtained by contacting the corresponding author.
Acknowledgments
We thank Lawrence Fulton for helping us with the meta-analysis.
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A. Observation-to-Theme Conversion
| Authors | Experimental Intervention | Intervention Theme | Effectiveness Indicators | Effectiveness Themes | Barriers to Adoption | Barrier Themes |
| Guo et al. [17] | Biomarkers (proteomics) in blood plasma to distinguish early AD from physiological aging and diagnose AD | Biomarkers | Accurate at detection of AD | Accurate at detecting AD | Taking blood plasma is invasive; processing the sample can be expensive; requires physical presence in medical facility with travel and expenses | Taking blood is invasive |
| Cost of intervention | ||||||
| Must train users | ||||||
| Requires physical presence of patient | ||||||
| Expense of travel involved | ||||||
| Kirbas et al. [18] | Spectral domain optical coherence tomography (SD-OCT) to measure retinal nerve fiber layer (RNFL) thickness to diagnose AD | Spectral domain optical coherence tomography | Accurate at detection of AD | Accurate at detecting AD | Expensive intervention; requires physical presence of patient with travel and expenses | Cost of intervention |
| Must train users | ||||||
| Requires physical presence of patient | ||||||
| Expense of travel involved | ||||||
| Wang et al. [19] | Bayesian network (BN) analysis based on regional gray matter volumes to identify differences in structural interactions among core default mode network (DMN) regions in structural MRI data to diagnose AD | AI/machine learning | Accurate at detection of AD | Accurate at detecting AD | Cost of MRI images and computers to run BN analysis on images | Cost of intervention |
| Must train users | ||||||
| Munro et al. [20] | Telepsychiatry and telepsychology through video teleconferencing (VTC) to diagnose AD | Telehealth/VTC/VR | Non-invasive, convenient, accurate, inexpensive | Non-invasive | Cost to acquire equipment, staff training | Cost of intervention |
| Accurate at detecting AD | Must train users | |||||
| Inexpensive | ||||||
| Zou et al. [21] | MRI biomarkers and MR spectroscopy (MRS) to detect changes in arterial blood flow to diagnose AD | MRI | Non-invasive, early detection of AD | Non-invasive | Cost to acquire equipment, staff training, requires physical presence of patient with travel and expenses | Cost of intervention |
| Accurate at detecting AD | Must train users | |||||
| Requires physical presence of patient | ||||||
| Expense of travel involved | ||||||
| Collij et al. [22] | Machine learning (AI) to arterial spin labeling to diagnose AD | AI/machine learning | Effective way to diagnose early stages of AD | Accurate at detecting AD | Invasive to draw blood or plasma, cost to acquire equipment, cost to train staff | Taking blood is invasive |
| Cost of intervention | ||||||
| Must train users | ||||||
| Hornberger et al. [23] | Positron emission tomography (PET) biomarkers to estimate AB neurotic plaque density to diagnose AD | PET | Effective way to diagnose early stages of AD | Accurate at detecting AD | Cost, physical presence of patient, train staff | Cost of intervention |
| Requires physical presence of patient | ||||||
| Expense of travel involved | ||||||
| Must train users | ||||||
| Zhou et al. [24] | Instrument trail-making task (iTMT) using a wearable sensor to identify motor-cognitive impairment and diagnose AD | Telehealth/VTC/VR | Effective way to diagnose early stages of AD | Accurate at detecting AD | Training of staff | Must train users |
| Inexpensive | ||||||
| Ashton et al. [25] | Biomarkers through saliva, including tau, to diagnose AD | Biomarkers | Non-invasive, non-stressful | Non-invasive | Not reported | Not reported |
| Non-stressful | ||||||
| Babiloni et al. [26] | Resting state electroencephalographic (rsEEG) rhythms to diagnose AD | EEG | Cost-effective, non-invasive, non-stressful, can be repeated without repetition effects, accurate method to diagnose AD | Inexpensive | Training of staff | Must train users |
| Non-invasive | ||||||
| Non-stressful | ||||||
| Repeatable without degradation of results | ||||||
| Accurate at detecting AD | ||||||
| Jones et al. [27] | PET biomarkers in microtubule-associated protein tau (MAPT) to diagnose AD | PET | Non-invasive, non-stressful | Non-invasive | Cost to acquire equipment, staff training, requires physical presence of patient with travel and expenses | Cost of intervention |
| Non-stressful | Must train users | |||||
| Requires physical presence of patient | ||||||
| Expense of travel involved | ||||||
| Lee et al. [28] | Machine learning (AI) of MRI scans to assess cortical atrophy and diagnose AD | AI/machine learning | The AI model is inexpensive. Good predictive validity. Non-invasive and non-stressful | Inexpensive | Cost of MRI images, must train people on the AI procedure | Cost of intervention |
| Accurate at detecting AD | Must train users | |||||
| Non-invasive | Requires physical presence of patient | |||||
| Non-stressful | Expense of travel involved | |||||
| Lowe et al. [29] | Tau-PET biomarkers to understand neurofibrillary tangle development to diagnose AD | PET | A method to assess neurofibrillary tangle on the living is necessary to use the results of this study | Ineffective | This procedure was performed at autopsy | Need a procedure for the living |
| Fotuhi et al. [30] | Using RNA as blood-based biomarker to diagnose AD | RNA | Inexpensive | Inexpensive | Invasive to draw blood or plasma, cost to acquire equipment, cost to train staff, requires physical presence of patient, travel expenses | Taking blood is invasive |
| Cost of intervention | ||||||
| Must train users | ||||||
| Requires physical presence of patient | ||||||
| Expense of travel involved | ||||||
| Pase et al. [31] | Plasma total tau as a blood biomarker to diagnose AD | Biomarkers | Convenient, inexpensive | Convenient | Invasive to draw blood or plasma, cost to acquire equipment, cost to train staff, requires physical presence of patient, travel expenses | Taking blood is invasive |
| Inexpensive | Cost of intervention | |||||
| Must train users | ||||||
| Requires physical presence of patient | ||||||
| Expense of travel involved | ||||||
| Tahmi et al. [32] | PET biomarkers for quantifying amyloid-B plaques on the brain to diagnose AD | PET | Effective way to diagnose early stages of AD | Accurate at detecting AD | Cost of PET, training of staff | Cost of intervention |
| Must train users | ||||||
| Requires physical presence of patient | ||||||
| Expense of travel involved | ||||||
| Cabinio et al. [33] | Virtual reality to assess memory decline to diagnose AD | Telehealth/VTC/VR | Non-invasive, non-stressful | Non-invasive | Cost to acquire equipment, staff training | Cost of intervention |
| Non-stressful | Must train users | |||||
| Rajan et al. [34] | Blood biomarkers total tau (t-tau), neurofilament light (Nf-L), and glial fibrillary acidic protein (GFAP) to diagnose AD | biomarkers | Accurate at detection of AD | Accurate at detecting AD | Taking blood plasma is invasive, processing the sample can be expensive, must train users, requires physical presence in medical facility with travel and expenses | Taking blood is invasive |
| Cost of intervention | ||||||
| Must train users | ||||||
| Requires physical presence of patient | ||||||
| Expense of travel involved | ||||||
| Thientunyakit et al. [35] | MRI and PET biomarkers used to assess amyloid levels, glucose metabolism, morphologic change in brain to diagnose AD | MRI and PET | Non-invasive, non-stressful, accurate diagnosis tool | Non-invasive | MRI and PET are expensive interventions | Cost of intervention |
| Non-stressful | Requires physical presence of patient | |||||
| Accurate at detecting AD | Expense of travel involved | |||||
| Altomare et al. [36] | Using amyloid-PET and tau-PET biomarkers to diagnose AD | PET | Non-invasive, non-stressful, accurate diagnosis tool | Accurate at detecting AD | MRI and PET are expensive interventions | Cost of intervention |
| Non-invasive | Requires physical presence of patient | |||||
| Non-stressful | Expense of travel involved | |||||
| Desai et al. [37] | Tau concentration biomarkers based on activity levels to treat AD | PET | Effective way to slow cognitive decline | Accurate at detecting AD | Blood draw is invasive, cost of analysis expensive, must train staff on procedure, patient must be present at the hospital, travel expenses | Taking blood is invasive |
| Must train users | ||||||
| Cost of intervention | ||||||
| Requires physical presence of patient | ||||||
| Expense of travel involved | ||||||
| Lin et al. [38] | Arterial pulse spectrum and multilayer-perception analysis to diagnose classification of AD | Arterial pulse | Non-invasive, rapid, inexpensive, and objective method for detecting and monitoring the AD status | Non-invasive | Training of staff | Must train users |
| Rapid | ||||||
| Inexpensive | ||||||
| Accurate at detecting AD | ||||||
| Liu et al. [39] | Serum miR-24-3P to diagnose AD | Biomarkers | Biomarker can help diagnose AD | Accurate at detecting AD | Blood draw is invasive, cost of analysis expensive, must train staff on procedure, patient must be present at the hospital, travel expenses | Taking blood is invasive |
| Must train users | ||||||
| Cost of intervention | ||||||
| Requires physical presence of patient | ||||||
| Expense of travel involved | ||||||
| Mila-Aloma et al. [40] | Using amyloid-B pathology to classify cognitively unimpaired individuals and diagnose AD | PET | Biomarker can help diagnose AD | Accurate at detecting AD | Blood draw is invasive, cost of analysis expensive, must train staff on procedure, patient must be present at the hospital, travel expenses | Taking blood is invasive |
| Must train users | ||||||
| Cost of intervention | ||||||
| Requires physical presence of patient | ||||||
| Expense of travel involved | ||||||
| Sajjad et al. [41] | Using PET biomarkers and synthetic data augmentation (DCGAN model) to diagnose AD | PET | Non-invasive, non-stressful, repeatable | Non-invasive | PET images are expensive, requires physical presence of pt, incurring expenses for travel | Cost of intervention |
| Non-stressful | Requires physical presence of patient | |||||
| Repeatable without degradation of results | Expense of travel involved | |||||
| Wu et al. [42] | Plasma biomarker (p-tau and t-tau) to diagnose AD | Biomarkers | Biomarker can help diagnose AD | Accurate at detecting AD | Blood draw is invasive, cost of analysis expensive, must train staff on procedure, patient must be present at the hospital, travel expenses | Taking blood is invasive |
| Must train users | ||||||
| Cost of intervention | ||||||
| Requires physical presence of patient | ||||||
| Expense of travel involved | ||||||
| Chun et al. [43] | 18F-THK5351 PET to diagnose AD | PET | Effective diagnostic tool | Accurate at detecting AD | PET images are expensive, requires physical presence of patient, incurring expenses for travel | Cost of intervention |
| Requires physical presence of patient | ||||||
| Expense of travel involved | ||||||
| Kim et al. [44] | Telemonitoring to diagnose AD | Telehealth/VTC/VR | Non-invasive, cost-effective | Accurate at detecting AD | Cost of sensors | Cost of intervention |
| Inexpensive |
Appendix B. Other Observations Incident to Review
| Authors | Sample Size | Bias within Study | Effect Size, Sensitivity, Specificity, and F1 | Country of Origin (Where Was the Study Conducted?) | Statistics Used |
| Guo et al. [17] | 165 | Germany only (selection bias) | sensitivity of 89.36% and a specificity of 79.17% | Germany | Independent Student’s t-test or Mann–Whitney U-test |
| Kirbas et al. [18] | 80 | Turkey only (selection bias) | not reported | Turkey | The normality of the distribution for all variables was assessed by the Kolmogorov–Smirnov test. Student t test was used for normally distributed variables and the Mann–Whitney U test was used for nonparametric variables |
| Wang et al. [19] | 181 | China only (selection bias) | 87.12% specificity and 81.25% sensitivity | China | BN analysis |
| Munro et al. [20] | 202 | US only (selection bias) | significant intraclass correlations(mean = 0.74; range: 0.55–0.91) | US | Bradley–Blackwood (BB) Procedure used to examine the bias between the testing formats by simultaneously testing the equality of means and equality of variances; if the BB procedure resulted in a significant result, both the paired t test (significant result indicating that means are biased) and the Pitman Test (significant results indicating that variances are biased) were examined to determine the source of bias. Bland–Altman plots used to explore magnitude of mean differences |
| Zou et al. [21] | 40 | China only (selection bias), 58% female (sample bias) | not reported | China | Independent sample t-test |
| Collij et al. [22] | 260 | Netherlands only (selection bias) | not reported | Netherlands | Not reported |
| Hornberger et al. [23] | 42 | US only (selection bias) | not reported | US | |
| Zhou et al. [24] | 30 | US only (selection bias) | strong effect size | US | Analysis of variance, Mann–Whitney U tests, and χ2 tests were used for between-group comparison according to the scale of the investigated variable and the distribution of the data. Analysis of covariance was employed to compare difference between groups for iTMT tests with and without adjustment for age, BMI, and education level. Sidak adjustment for pairwise comparison used for post-hoc analysis. Test-reliability was assessed using interclass correlation |
| Ashton et al. [25] | 160 | Sweden only (selection bias) | not reported | Sweden | Not reported |
| Babiloni et al. [26] | 83 | Italy only (selection bias), older patients only (sample bias), 82% male (sample bias) | a sensitivity of 90%, a specificity of 73.3%, an accuracy of 81.7%, and 0.86 of the AUROC curve | Italy | ANOVA. The degrees of freedom were corrected by using the Greenhouse–Geisser procedure when appropriate.Duncan test was used for post-hoc comparisons |
| Jones et al. [27] | 284 | US only (selection bias) | not reported | US | t-test used to compare least-squares means of log tau PET standardized uptake value ratios (SUVRs) |
| Lee et al. [28] | 1342 | Korea only (selection bias) | sensitivity of 87.1% and specificity of 93.3% | Korea | Chi-square, Fisher’s exact, or Student’s t-tests |
| Lowe et al. [29] | 687 | US only (selection bias) | not reported | US | Hierarchical clustering |
| Fotuhi et al. [30] | 81 | Iran only (selection bias) | 75% sensitivity and 100% specificity and 0.89 (p < 0.0001, 95% CI 0.8109–0.9669) with 68% sensitivity and 100% specificity, respectively | Iran | t-test and one-way ANOVA |
| Pase et al. [31] | 1453 | US only (selection bias), 55% female (sample bias) | not reported | US | t-test and Spearman’s correlation coefficient |
| Tahmi et al. [32] | 52 | US only (selection bias) | not reported | US | 2-way ANOVA and t-tests |
| Cabinio et al. [33] | 139 | Italy only (selection bias) | sensitivity 84.4%, specificity 75.5% for SASG-total | Italy | Linear regression, one-way ANOVA, or Mann–Whitney; data were corrected for multiple comparison using the Bonferroni correction |
| Rajan et al. [34] | 1327 | US only (selection bias), 60% female, 60% African American (sample bias) | in a sensitivity analysis for the association of blood biomarkers with clinical AD adjusting for storage time, we found that the association of blood biomarkers was slightly higher for t-tau with an OR for clinical AD of 1.71 (95% CI = 1.42–2.03); lower for Nf-L, with an OR of 3.58 (95% CI = 2.68–4.93); and higher for GFAP, with an OR of 3.32 (95% CI = 2.34–4.55). | US | Mutlivariate logistic regression model with time-dependent log10-transformed t-tau, Nf-L, and GFAP |
| Thientunyakit et al. [35] | 51 | Thailand only (selection bias) | high | Thailand | One-way ANOVA, Chi-Square, Student t-test |
| Altomare et al. [36] | 136 | Switzerland only (selection bias) | not reported | Switzerland | Mann–Whitney to compare arms. Cohen’s k to calculate agreement. Chi-Square to assess changes in diagnosis. Proportional test used to assess difference in diagnostic changes between pathways |
| Desai et al. [37] | 1159 | US only (selection bias), 63% female (sample bias) | not reported | US | Mixed-effects regression |
| Lin et al. [38] | 161 | Taiwan only (selection bias) | accuracy of 82.86%, a specificity of 92.31%, and a 0.83 AUC of ROC curve | Taiwan | Machine learning (SEM)—Python, AD pts randomly split into training and validation sets. Cross-validation used with 80:20 training and validation |
| Liu et al. [39] | 198 | China only (selection bias) | China | t-test | |
| Mila-Aloma et al. [40] | 318 | Spain only (selection bias) | not reported | Spain | ANCOVA |
| Sajjad et al. [41] | 136 | Saudi Arabia only (selection bias) | Weighted average F1 = 0.83 | Saudi Arabia | Machine learning (SEM) |
| Wu et al. [42] | 159 | China only (selection bias), 58% female (sample bias) | 78.6% sensitivity and 94.2% specificity | China | Student t-test and Mann–Whitney U-test, Chi-Square used for categorical variables |
| Chun et al. [43] | 25 | Korea only (selection bias) | not reported | Korea | Student’s t-test and chi-square test for continuous and categorical variables |
| Kim et al. [44] | 18 | Korea only (selection bias) | not reported | Korea | Machine learning, 66% training, 34% testing |
Appendix C. Statistical Analysis
| 1st Author | N | Number Not Hlthy | Number Hlthy | True Positives | False Negatives | True Negatives | False Positives | Sensitivity (Recall) | Specificity | PPV (Precision) | Accuracy | F1 Score | OR | Notes |
| Guo [17] | 165 | 58 | 109 | 52 | 6 | 86 | 23 | 0.894 | 0.792 | 0.70 | 0.84 | 0.78 | 31.92 | |
| Wang [19] | 181 | 80 | 101 | 70 | 10 | 82 | 19 | 0.871 | 0.813 | 0.79 | 0.84 | 0.83 | 29.31 | |
| Munro [20] | 202 | 83 | 119 | 72 | 11 | 96 | 23 | 0.870 | 0.810 | 0.76 | 0.83 | 0.81 | 28.53 | |
| Babiloni [26] | 83 | 53 | 30 | 48 | 5 | 22 | 8 | 0.900 | 0.733 | 0.86 | 0.84 | 0.88 | 24.71 | |
| Lee [28] | 1342 | 473 | 869 | 412 | 61 | 811 | 58 | 0.871 | 0.933 | 0.88 | 0.91 | 0.87 | 94.02 | |
| Fotuhi [30] | 81 | 45 | 36 | 31 | 14 | 36 | 0 | 0.680 | 1.000 | 1.00 | 0.82 | 0.81 | 152.37 | Full AD vs. Healthy Controls only, Haldane–Anscombe correction for OR) |
| Cabinio [33] | 139 | 32 | 107 | 27 | 5 | 81 | 26 | 0.844 | 0.755 | 0.51 | 0.78 | 0.63 | 16.67 | |
| Lin [38] | 161 | 87 | 74 | 72 | 15 | 68 | 6 | 0.829 | 0.923 | 0.93 | 0.87 | 0.87 | 58.03 | |
| Liu [39] | 198 | 104 | 94 | 91 | 13 | 73 | 21 | 0.875 | 0.777 | 0.81 | 0.83 | 0.84 | 24.39 | |
| Wu [42] | 159 | 148 | 121 | 116 | 32 | 114 | 7 | 0.786 | 0.942 | 0.94 | 1.45 | 0.86 | 59.65 | |
| Totals | 2711 | 1163 | 1660 | 990 | 173 | 1470 | 190 | |||||||
| Overall Sensitivity | 0.8516 | |||||||||||||
| Overall Specificity | 0.8853 | |||||||||||||
| Overall PPV | 0.8388 | |||||||||||||
| Overall Accuracy | 0.9074 | |||||||||||||
| Overall F1 Score | 0.8452 |
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