Detection Of Alzheimer's Disease Research Articles

Potential ocular indicators to distinguish posterior cortical atrophy and typical Alzheimer’s disease: a cross-section study using optical coherence tomography angiography

BackgroundPosterior cortical atrophy (PCA) is a form of dementia that frequently displays significant visual dysfunction and relatively preserved cognitive and executive functions, thus hindering early diagnosis and treatment. This study aimed to investigate possible fundus markers in PCA patients and compare them with those of typical Alzheimer’s disease (AD) patients to seek potential diagnostic patterns.MethodsAge-matched PCA and AD patients and healthy controls (HC) completed optometry, intraocular pressure measurement, neuropsychologic assessments, optical coherence tomography (OCT), and optical coherence tomography angiography (OCTA) examination in one visit. Overall, six outcomes of thicknesses of various retinal layers and seven outcomes of the retinal microvascular network were calculated. After adjusting for age, sex, and years of education, the OCT and OCTA results were analyzed using analysis of covariance and generalized linear models. Correlation analyses were performed using Spearman correlation, and ROC curves were plotted.ResultsTwelve PCA patients, nineteen AD patients, and thirty HC, aged 45–80 years were included. Fifty HC, thirty AD, and twenty PCA eyes were available for foveal avascular zone (FAZ) area analysis; forty-nine HC, thirty-four AD, and eighteen PCA eyes were available for OCT and OCTA assessments. PCA patients had thinner retinal nerve fiber layer and ganglion cell layer + inner plexiform layer than HC in the 0–3 mm circle and 1–3 mm ring. Few structural differences were observed between the AD group and the other two groups. The flow area of the superficial capillary plexus and the intermediate capillary plexus was smaller in the PCA group than in the HC group in the 0–1 mm circle, 0–3 mm circle. MMSE performed better than any combination of optical parameters in identifying AD and PCA from HC (AUC = 1), while the combination of MoCA, retinal thickness and vascular density of ICP in the 1-3 mm ring, with flow area of ICP in the 0-1 mm circle showed the strongest ability to distinguish PCA from AD (AUC = 0.944).ConclusionsPCA patients exhibited similar impairment patterns to AD patients in the fundus structure and microvascular network. OCTA may aid in the non-invasive detection of AD and PCA, but still remains to be substantiated.

A Novel Approach to Detection of Alzheimer’s Disease from Handwriting: Triple Ensemble Learning Model

The irreversible degeneration of nerve cells in the body dramatically affects the motor skills and cognitive abilities used effectively in daily life. There is no known cure for neurodegenerative diseases such as Alzheimer’s. However, in the early diagnosis of such diseases, the progression of the disease can be slowed down with specific rehabilitation techniques and medications. Therefore, early diagnosis of the disease is essential in slowing down the disease and improving patients’ quality of life. Neurodegenerative diseases also affect patients’ ability to use fine motor skills. Losing fine motor skills causes patients’ writing skills to deteriorate gradually. Information about Alzheimer’s disease can be obtained based on the deterioration in the patient’s writing skills. However, manual detection of Alzheimer’s disease (AD) from handwriting is a time-consuming and challenging task that varies from physician to physician. Machine learning-based classifiers are extremely popularly used with high-performance scores to solve the challenging manual detection of AD. In this study, Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost) machine learning classification algorithms were combined with a Voting Classifier and trained and tested on the publicly available DARWIN (Diagnosis Alzheimer’s With haNdwriting) dataset. As a result of the experimental studies, the proposed Ensemble methodology achieved 97.14% Acc, 95% Prec, 100% Recall, 90.25% Spec, and 97.44% F1-score (Dice) performance values. Studies have shown that the proposed work is exceptionally robust.

Dual Attention Aware Octave Convolution Network for Early-Stage Alzheimer's Disease Detection

Some of the most fundamental human capabilities, including thought, speech, and movement, may be lost due to brain illnesses. The most prevalent form of dementia, Alzheimer's disease (AD), is caused by a steady decline in brain function and is now incurable. Despite the challenges associated with making a conclusive diagnosis of AD, the field has generally shifted toward making diagnoses justified by patient records and neurological analysis, such as MRI. Reports of studies utilizing machine learning for AD identification have increased in recent years. In this publication, we report the results of our most recent research. It details a deep learning-based, 3D brain MRI-based method for automated AD detection. As a result, deep learning models have become increasingly popular in recent years for analyzing medical images. To aid in detecting Alzheimer's disease at an initial phase, we suggest a dual attention-aware Octave convolution-based deep learning network (DACN). The three main parts of DACN are as follows: First, we use Patch Convolutional Neural Network (PCNN) to identify discriminative features within each MRI patch while simultaneously boosting the features of abnormally altered micro-structures in the brain; second, we use an Octave convolution to minimize the spatial redundancy and widen the field of perception of the brain's structure; and third, we use a dual attention aware convolution classifier to dissect the resulting depiction further. An outstanding test accuracy of 99.87% is reached for categorizing dementia phases by employing the suggested method in experiments on a publically available ADNI (Alzheimer's Disease Neuroimaging Initiative) dataset. The proposed model was more effective, efficient, and reliable than the state-of-the-art models through our comparisons.

Utilizing Siamese 4D-AlzNet and Transfer Learning to Identify Stages of Alzheimer’s Disease

Alzheimer's disease (AD) is the general form of dementia, leading to a progressive neurological disorder characterized by memory loss due to brain cell damage. Artificial Intelligence (AI) assists in the early identification and prediction of AD patients, determining future risks and benefits for radiologists and doctors to save time and cost. Since deep learning (DL) approaches work well with massive datasets and have recently become helpful for AD detection, there remains an area for improvement in automating detection performance. Present approaches somehow addressed the challenges of limited annotated data samples for binary classification. This contrasts with prior state-of-the-art techniques, which were constrained by their incapacity to capture abstract-level information. In this paper, we proposed a Siamese 4D-AlzNet model comprised of four parallel convolutional neural network (CNN) streams (Five CNN layer blocks) and customized transfer learning models (Frozen VGG-19, Frozen VGG-16, and customized AlexNet). Siamese 4D-AlzNet was vertically and horizontally stored, and the spatial features were passed to the final layer for classification. For experiments, T1-weighted MRI images comprised of four distinct subject classes, normal control (NC), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI), and AD, have been employed. Our proposed models achieved outstanding accuracy, with a remarkable 95.05% accuracy distinguishing between normal and AD subjects. The performance across remaining binary class pairs consistently exceeded 90%. We thoroughly compared our model with the latest methods using the same dataset as our reference. Our proposed model improved NC-AD and MCI-AD classification accuracy by 2% 7%.

Hybrid federated learning with brain-region attention network for multi-center Alzheimer's disease detection

Identifying reproducible and interpretable biomarkers for Alzheimer's disease (AD) detection remains a challenge. AD detection using multi-center datasets can expand the sample size to improve robustness but might lead to a data privacy problem. Moreover, due to the high cost of labeling data, a lot of unlabeled data in each center is not fully utilized. To address this, a hybrid FL (HFL) framework is proposed that not only uses unlabeled data to train deep learning networks, but also achieves data privacy protection. We propose a novel Brain-region Attention Network (BANet), which highlights important regions via attention to represent the region of interest (ROIs).Specifically, we use a brain template to extract ROI signals from the preprocessed structure magnetic resonance imaging (sMRI) data. In addition, we add a self-supervised loss to the current loss to guide the attention map generation to learn the representations from unlabeled data. Finally, we evaluate our method on a multi-center database which is constructed using five AD datasets. The experimental results show that the proposed method performs better than state-of-the-art methods, achieving mean accuracy rates of 85.69 %, 63.34 %, and 69.89 % on the AD vs. NC, MCI vs. NC, and AD vs. MCI respectively. The source code is available for reproducibility at: https://github.com/yuliangCarmelo/HFL.

Detection of Early Stage Alzheimer's Disease in Gradient-Based MR Images Using Deep Learning Methods

Alzheimer's Disease (AD), a form of dementia prevalent in older age, is a neurodegenerative condition impacting brain nerve cells. Early-stage AD symptoms are often subtle, complicating timely diagnosis. Early detection allows for intervention, slowing disease progression and facilitating appropriate treatments. Deep learning methods, particularly gradient-based images, prove promising for early Alzheimer's detection in Magnetic Resonance (MR) imaging. Gradient-based images, highlighting details in low-intensity images and enhancing contrast, play a vital role in determining structures' location, shape, and size, notably in techniques like Magnetic Resonance Imaging (MRI). This study aims to boost model performance in early AD detection by applying the Gradient filter before training deep learning models on diverse-angle and constant-density brain MRI images. The dataset comprises three categories representing early-stage AD, including images of mild cognitive impairment and healthy individuals. Original and gradient-filtered image subsets were inputted into deep learning models. Results indicate superior performance of gradient-based images, with the Densenet201 deep learning model achieving the highest accuracy at 98.63%.

Predicting early Alzheimer’s with blood biomarkers and clinical features

Alzheimer’s disease (AD) is an incurable neurodegenerative disorder that leads to dementia. This study employs explainable machine learning models to detect dementia cases using blood gene expression, single nucleotide polymorphisms (SNPs), and clinical data from Alzheimer’s Disease Neuroimaging Initiative (ADNI). Analyzing 623 ADNI participants, we found that the Support Vector Machine classifier with Mutual Information (MI) feature selection, trained on all three data modalities, achieved exceptional performance (accuracy = 0.95, AUC = 0.94). When using gene expression and SNP data separately, we achieved very good performance (AUC = 0.65, AUC = 0.63, respectively). Using SHapley Additive exPlanations (SHAP), we identified significant features, potentially serving as AD biomarkers. Notably, genetic-based biomarkers linked to axon myelination and synaptic vesicle membrane formation could aid early AD detection. In summary, this genetic-based biomarker approach, integrating machine learning and SHAP, shows promise for precise AD diagnosis, biomarker discovery, and offers novel insights for understanding and treating the disease. This approach addresses the challenges of accurate AD diagnosis, which is crucial given the complexities associated with the disease and the need for non-invasive diagnostic methods.

Flanagan's condensed protocol for neurodegenerative diseases. Implementation in a clinical autopsy setting with partial supervision of a neuropathologist.

The Condensed Protocol (CP) was originally developed for the evaluation of Alzheimer's Disease (AD) and other neurodegenerative diseases as a workable alternative to the complex and costly established autopsy guidelines. The study objective is to examine the degree of implementation of the CP in the pathology department of a third level university hospital in a period of 5years. Clinical autopsies performed between 2016 and 2021 on patients aged 65years or over and did not require a specific neuropathological examination were reviewed. Histological screening and staging of neurodegenerative diseases was performed using the original immunohistochemical stains. Out of 255 autopsies, 204 met the inclusion criteria and 190 could be reviewed. The CP was applied to 99 cases; histological signs of neurodegenerative disease were observed in 92. Sampling errors were detected in 59 cases. Immunohistochemical studies were performed in 68 cases. The diseases identified were: 31 cases of AD (12 low grade; 19 intermediate), 18 amyloid angiopathy, 15 primary age-related tauopathy, 6 argyrophilic grain disease, 3 progressive supranuclear palsy, 1 Lewy body disease (of 22 cases), and 2 limbic-predominant age TDP43 encephalopathy (of 5 cases). In 30 out of 83 cases, there was more severe vascular pathology in complete sections of frontal cortex and lentiform nucleus. The CP allows reliable detection and staging of AD and related neurodegenerative diseases in clinical autopsies. However, supervision by a neuropathologist seems necessary for a fully successful implementation of the CP in a clinical hospital setting.

Secure federated learning for Alzheimer's disease detection.

Machine Learning (ML) is considered a promising tool to aid and accelerate diagnosis in various medical areas, including neuroimaging. However, its success is set back by the lack of large-scale public datasets. Indeed, medical institutions possess a large amount of data; however, open-sourcing is prevented by the legal requirements to protect the patient's privacy. Federated Learning (FL) is a viable alternative that can overcome this issue. This work proposes training an ML model for Alzheimer's Disease (AD) detection based on structural MRI (sMRI) data in a federated setting. We implement two aggregation algorithms, Federated Averaging (FedAvg) and Secure Aggregation (SecAgg), and compare their performance with the centralized ML model training. We simulate heterogeneous environments and explore the impact of demographical (sex, age, and diagnosis) and imbalanced data distributions. The simulated heterogeneous environments allow us to observe these statistical differences' effect on the ML models trained using FL and highlight the importance of studying such differences when training ML models for AD detection. Moreover, as part of the evaluation, we demonstrate the increased privacy guarantees of FL with SecAgg via simulated membership inference attacks.

A machine learning-based data-driven approach to Alzheimer’s disease diagnosis using statistical and harmony search methods

Alzheimer’s disease (AD) is the most prevalent brain disorder which affects millions of people worldwide. Early detection is crucial for possible treatment. In this regard, machine learning (ML) approaches are widely utilized for AD detection. In this paper, we propose an ML-based method that drastically reduces the dimensionality of features while maintaining the relevant features and boosting the overall performance. To remove irrelevant features, first statistical feature extraction method is applied, and then further reduction among remaining features is applied by utilizing the harmony search method (HSM). The selected features are the most informative features that are fed to the different classifiers. To test the effectiveness of the proposed method, we deployed three classification techniques including support vector machine (SVM), k-nearest neighbor (k-NN), and decision tree (DT). The experimental results show that the proposed method has a higher performance while decreasing the dimensionality of feature space. To guarantee that the performance of the proposed method is accurate, we applied an ensemble of three classifiers (SVM, KNN, and DT) for classification. The results of the proposed method verify that this method can be successfully deployed for AD detection, due to its high performance and low dimensional features, and can help improve the accuracy and efficiency of Alzheimer’s disease diagnosis. The proposed method demonstrated a significant improvement, achieving high performance in AD/HC classification, with accuracy, sensitivity, specificity, F1-score, MCC, and Cohen’s Kappa rates reaching 95.5%, 97%, 94%, 95.56%, 0.9104, and 0.9109, respectively. AD/HC classification displayed the highest performance. Additionally, in the more challenging pMCI/sMCI classification, the method achieved an accuracy of 78.50%, sensitivity of 84.00%, specificity of 73.00%, F1-score of 79.62%, MCC of 0.57, and Cohen’s Kappa of 0.59.

Sound insights: The potential of hearables for early detection and continuous monitoring of Alzheimer’s disease

Early identification using non-invasive biological markers is crucial for detecting and treating Alzheimer's disease (AD). Hearables are intra-aural wearable devices equipped with in-ear microphones capable of capturing inner body signals, including heartbeat, breathing, and blinking. These non-verbal audio events give valuable insight into the individual's physiological state, offering a unique method to detect and monitor subtle changes in the body, which holds the potential for early disease detection. Central auditory (CA) tests, such as understanding speech in background noise, have shown promise in tracking AD’s effects on the central nervous system. They also provide sensitive, quantitative, and repeatable assessments over time. Therefore, this study will assess physiological signals captured by a hearable during central auditory assessment. Thirty-five participants with AD and mild cognitive impairment will be recruited, in addition to age-matched control participants. Hearables will be used to capture physiological data and perform CA tests such as hearing in noise test, triple digits test, dichotic digits test, and dichotic sentence identification test. Moreover, a picture description task, where participants will have to produce coherent speech that holds potential for tracking the decline in cognitive abilities, will be administered. The data collected will be used to build an open-access database serving as the foundation of future research on early detection and monitoring of AD using hearables.

Extracellular vesicle response following focused ultrasound-mediated blood–brain barrier opening for the detection of Alzheimer’s pathology

Focused ultrasound (FUS), in conjunction with systemically administered microbubbles, has been shown to induce transient and non-invasive blood–brain barrier opening (BBBO). Originally developed as a method of improving targeted drug delivery, FUS-BBBO has recently been proposed as a means of amplifying detection of disease biomarkers in the bloodstream. We hereby describe a method that can detect extracellular vesicles (EVs) after BBB opening in Alzheimer’s disease (AD) mice and patients. BBBO is shown to enhance the release of EVs with disease-specific cargo to the bloodstream when targeting amyloid affected regions and shown to be more sensitive than cell free DNA. In this presentation, we will describe the overall methodology and quantify the concentration and content of EVs isolated from the serum of mice following drug-free FUS-BBBO in both mice and patients. In AD mice and patients, an average EV concentration increase of 164% and 100% 1 h after FUS-BBBO containing both amyloid and tau was, respectively, found compared to 0% under sham conditions and highly correlated with BBBO volume. Whole genome RNA sequencing and mass spectrometry protein identification demonstrated inflammatory and proliferation gene upregulation. These findings highlight the potential of FUS-BBBO to increase EV serum concentration towards early AD detection.

Beyond CSF and Neuroimaging Assessment: Evaluating Plasma miR-145-5p as a Potential Biomarker for Mild Cognitive Impairment and Alzheimer's Disease.

Alzheimer's disease (AD) is the most common cause of dementia. New strategies for the early detection of MCI and sporadic AD are crucial for developing effective treatment options. Current techniques used for diagnosis of AD are invasive and/or expensive, so they are not suitable for population screening. Cerebrospinal fluid (CSF) biomarkers such as amyloid β1-42 (Aβ1-42), total tau (T-tau), and phosphorylated tau181 (P-tau181) levels are core biomarkers for early diagnosis of AD. Several studies have proposed the use of blood-circulating microRNAs (miRNAs) as potential novel early biomarkers for AD. We therefore applied a novel approach to identify blood-circulating miRNAs associated with CSF biomarkers and explored the potential of these miRNAs as biomarkers of AD. In total, 112 subjects consisting of 28 dementia due to AD cases, 63 MCI due to AD cases, and 21 cognitively healthy controls were included. We identified seven Aβ1-42-associated plasma miRNAs, six P-tau181-associated plasma miRNAs, and nine Aβ1-42-associated serum miRNAs. These miRNAs were involved in AD-relevant biological processes, such as PI3K/AKT signaling. Based on this signaling pathway, we constructed an miRNA-gene target network, wherein miR-145-5p has been identified as a hub. Furthermore, we showed that miR-145-5p performs best in the prediction of both AD and MCI. Moreover, miR-145-5p also improved the prediction performance of the mini-mental state examination (MMSE) score. The performance of this miRNA was validated using different datasets including an RT-qPCR dataset from plasma samples of 23 MCI cases and 30 age-matched controls. These findings indicate that blood-circulating miRNAs that are associated with CSF biomarkers levels and specifically plasma miR-145-5p alone or combined with the MMSE score can potentially be used as noninvasive biomarkers for AD or MCI screening in the general population, although studies in other AD cohorts are necessary for further validation.

The Optimization of a Natural Language Processing Approach for the Automatic Detection of Alzheimer's Disease Using GPT Embeddings.

The development of noninvasive and cost-effective methods of detecting Alzheimer's disease (AD) is essential for its early prevention and mitigation. We optimize the detection of AD using natural language processing (NLP) of spontaneous speech through the use of audio enhancement techniques and novel transcription methodologies. Specifically, we utilized Boll Spectral Subtraction to improve audio fidelity and created transcriptions using state-of-the-art AI services-locally-based Wav2Vec and Whisper, alongside cloud-based IBM Cloud and Rev AI-evaluating their performance against traditional manual transcription methods. Support Vector Machine (SVM) classifiers were then trained and tested using GPT-based embeddings of transcriptions. Our findings revealed that AI-based transcriptions largely outperformed traditional manual ones, with Wav2Vec (enhanced audio) achieving the best accuracy and F-1 score (0.99 for both metrics) for locally-based systems and Rev AI (standard audio) performing the best for cloud-based systems (0.96 for both metrics). Furthermore, this study revealed the detrimental effects of interviewer speech on model performance in addition to the minimal effect of audio enhancement. Based on our findings, current AI transcription and NLP technologies are highly effective at accurately detecting AD with available data but struggle to classify probable AD and mild cognitive impairment (MCI), a prodromal stage of AD, due to a lack of training data, laying the groundwork for the future implementation of an automatic AD detection system.

Gene expression patterns of CRYM and SIGLEC10 in Alzheimer's disease: potential early diagnostic indicators.

Alzheimer's disease (AD) is a neurological condition that may lead to dementia as well as a slow and steady decline in cognitive ability. Finding early signs that may be used in the diagnosis of ADis still a difficult aim to achieve in the field of medical practice. The purpose of this research was to investigate to determineany differences in the gene expression patterns of crystallin mu (CRYM) and sialic acid-binding immunoglobulin-like lectin 10 (SIGLEC10) in whole blood samples obtained from fifty individuals who were diagnosed with ADand fifty individuals as a control group. When compared with controls, it was discovered that the expression of the CRYM gene was substantially decreased in AD patients, but the expression of the SIGLEC10 gene was significantly higher. A positive correlation between CRYM and SIGLEC10 was noticed solely in patients with AD. Furthermore, assessing the diagnostic value of these genes, CRYM and SIGLEC10 transcript levels displayed an area under the curve (AUC) of 0.74 and 0.81, respectively. These results suggest that alterations in CRYM and SIGLEC10 expression may be implicated in AD pathology and that these genes expression levels can potentially serve as biomarkers for early detection and diagnosis of AD. Nevertheless, further validation of these findings requires the inclusion of more extensive and heterogeneous cohorts. The findings derived from this study possess the capability to offer a significant contribution towards the progression of innovative diagnostic and therapeutic strategies for AD.

Alzheimer’s Disease Detection Using Deep Learning on Neuroimaging: A Systematic Review

Alzheimer’s disease (AD) is a pressing global issue, demanding effective diagnostic approaches. This systematic review surveys the recent literature (2018 onwards) to illuminate the current landscape of AD detection via deep learning. Focusing on neuroimaging, this study explores single- and multi-modality investigations, delving into biomarkers, features, and preprocessing techniques. Various deep models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative models, are evaluated for their AD detection performance. Challenges such as limited datasets and training procedures persist. Emphasis is placed on the need to differentiate AD from similar brain patterns, necessitating discriminative feature representations. This review highlights deep learning’s potential and limitations in AD detection, underscoring dataset importance. Future directions involve benchmark platform development for streamlined comparisons. In conclusion, while deep learning holds promise for accurate AD detection, refining models and methods is crucial to tackle challenges and enhance diagnostic precision.

Removing outliers from the normative database improves regional atrophy detection in single-subject voxel-based morphometry

PurposeSingle-subject voxel-based morphometry (VBM) compares an individual T1-weighted MRI to a sample of normal MRI in a normative database (NDB) to detect regional atrophy. Outliers in the NDB might result in reduced sensitivity of VBM. The primary aim of the current study was to propose a method for outlier removal (“NDB cleaning”) and to test its impact on the performance of VBM for detection of Alzheimer’s disease (AD) and frontotemporal lobar degeneration (FTLD).MethodsT1-weighted MRI of 81 patients with biomarker-confirmed AD (n = 51) or FTLD (n = 30) and 37 healthy subjects with simultaneous FDG-PET/MRI were included as test dataset. Two different NDBs were used: a scanner-specific NDB (37 healthy controls from the test dataset) and a non-scanner-specific NDB comprising 164 normal T1-weighted MRI from 164 different MRI scanners. Three different quality metrics based on leave-one-out testing of the scans in the NDB were implemented. A scan was removed if it was an outlier with respect to one or more quality metrics. VBM maps generated with and without NDB cleaning were assessed visually for the presence of AD or FTLD.ResultsSpecificity of visual interpretation of the VBM maps for detection of AD or FTLD was 100% in all settings. Sensitivity was increased by NDB cleaning with both NDBs. The effect was statistically significant for the multiple-scanner NDB (from 0.47 [95%-CI 0.36–0.58] to 0.61 [0.49–0.71]).ConclusionNDB cleaning has the potential to improve the sensitivity of VBM for the detection of AD or FTLD without increasing the risk of false positive findings.

An effective and robust lattice Boltzmann model guided by atlas for hippocampal subregions segmentation.

Given the varying vulnerability of the rostral and caudal regions of the hippocampus to neuropathology in the Alzheimer's disease (AD) continuum, accurately assessing structural changes in these subregions is crucial for early AD detection. The development of reliable and robust automatic segmentation methods for hippocampal subregions (HS) is of utmostimportance. Our aim is to propose and validate a HS segmentation model that is both training-free and highly generalizable. This method should exhibit comparable accuracy and efficiency to state-of-the-art techniques. The segmented HS can serve as a biomarker for studying the progression ofAD. We utilized the functional magnetic resonance imaging of the Brain's Integrated Registration and Segmentation Tool (FIRST) to segment the entire hippocampus. By intersecting the segmentation results with the Brainnetome (BN) atlas, we obtained coarse segmentation of the four HS regions. This coarse segmentation was then employed as a shape prior term in the lattice Boltzmann (LB) model, as well as for initializing contours. Additionally, image gradients and local gray levels were integrated into the external force terms of the LB model to refine the coarse segmentation results. We assessed the segmentation accuracy of the model using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and evaluated the potential of the segmentation results as AD biomarkers on both the ADNI and Xuanwudatasets. The median Dice similarity coefficients (DSC) for the left caudal, right caudal, left rostral, and right rostral hippocampus were 0.87, 0.88, 0.88, and 0.89, respectively. The proportion of segmentation results with a DSC exceeding 0.8 was 77%, 78%, 77%, and 94% for the respective regions. In terms of volume, the correlation coefficients between the segmentation results of the four HS regions and the gold standard were 0.95, 0.93, 0.96, and 0.96, respectively. Regarding asymmetry, the correlation coefficient between the segmentation result's right caudal minus left caudal and the corresponding gold standard was 0.91, while for right rostral minus left rostral, it was 0.93. Over time, we observed a decline in the volumes of the four HS regions and the total hippocampal volume of mild cognitive impairment (MCI) converters. Analysis of inter-group differences revealed that, except for the right rostral region in the ADNI dataset, the p-values for the four HS regions in the normal controls (NC), MCI, and AD groups from both datasets were all below 0.05. The right caudal hippocampal volume demonstrated correlation coefficients of 0.47 and 0.43 with the mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA), respectively. Similarly, the left rostral hippocampal volume showed correlation coefficients of 0.50 and 0.58 with MMSE and MoCA,respectively. Our framework allows for direct application to different brain magnetic resonance (MR) datasets without the need for training. It eliminates the requirement for complex image preprocessing steps while achieving segmentation accuracy comparable to deep learning (DL) methods even with small sample sizes. Compared to traditional active contour models (ACM) and atlas-based methods, our approach exhibits significant speed advantages. The segmented HS regions hold promise as potential biomarkers for studying the progression ofAD.

Adjunct Methods for Alzheimer's Disease Detection: A Review of Auditory Evoked Potentials.

The auditory afferent pathway as a clinical marker of Alzheimer's disease (AD) has sparked interest in investigating the relationship between age-related hearing loss (ARHL) and AD. Given the earlier onset of ARHL compared to cognitive impairment caused by AD, there is a growing emphasis on early diagnosis and intervention to postpone or prevent the progression from ARHL to AD. In this context, auditory evoked potentials (AEPs) have emerged as a widely used objective auditory electrophysiological technique for both the clinical diagnosis and animal experimentation in ARHL due to their non-invasive and repeatable nature. This review focuses on the application of AEPs in AD detection and the auditory nerve system corresponding to different latencies of AEPs. Our objective was to establish AEPs as a systematic and non-invasive adjunct method for enhancing the diagnostic accuracy of AD. The success of AEPs in the early detection and prediction of AD in research settings underscores the need for further clinical application and study.

Enhancing Early Alzheimer's Disease Detection: Leveraging Pre-trained Networks and Transfer Learning

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide. Early and accurate AD detection is crucial for timely intervention and improving patient outcomes. Lately, there have been notable advancements in using deep learning approaches to classify neuroimaging data associated with Alzheimer's disease. These methods have shown substantial progress in achieving accurate classification results. Nevertheless, the concept of end-to-end learning, which has the potential to harness the benefits of deep learning fully, has yet to garner extensive focus in the realm of neuroimaging. This is attributed mainly to the persistent challenge in neuroimaging, namely the limited data availability. This study employs neuroimages and Transfer Learning (TL) to identify early signs of AD and different phases of cognitive impairment. By employing transfer learning, the study uses Magnetic Resonance Imaging (MRI) images from the Alzheimer's Disease Neuroimaging (ADNI) database to classify images into various categories, such as Cognitively Normal (CN), Early Mild Cognitive Impairment (EMCI), Mild Cognitive Impairment (MCI), Late Mild Cognitive Impairment (LMCI), and Alzheimer's Disease (AD). The classification task involves training and testing three pre-trained networks: VGG-19, ResNet-50, and Inception V3. The study evaluates the performance of these networks using the confusion matrix and its associated metrics. Among the three models, ResNet-50 achieves the highest recall rate of 99.25%, making it more efficient in detecting the early stages of AD development. The study further examines the performance of the pre-trained networks on a class-by-class basis using the parameters derived from the confusion matrix. This comprehensive analysis provides insights into how each model performs for different classes within the AD classification framework. Overall, the research underscores the potential of deep learning and transfer learning in advancing early AD detection and emphasizes the significance of utilizing pre-trained models for this purpose.