A Survey on Automated Diagnosis of Alzheimer's Disease Using Optical Coherence Tomography and Angiography

Retinal optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) are promising tools for the (early) diagnosis of Alzheimer's disease (AD). These non-invasive imaging techniques are cost-effective and more accessible than alternative neuroimaging tools. However, interpreting and classifying multi-slice scans produced by OCT devices is time-consuming and challenging even for trained practitioners. There are surveys on machine learning and deep learning approaches concerning the automated analysis of OCT scans for various diseases such as glaucoma. However, the current literature lacks an extensive survey on the diagnosis of Alzheimer's disease or cognitive impairment using OCT or OCTA. This has motivated us to do a comprehensive survey aimed at machine/deep learning scientists or practitioners who require an introduction to the problem. The paper contains 1) an introduction to the medical background of Alzheimer's Disease and Cognitive Impairment and their diagnosis using OCT and OCTA imaging modalities, 2) a review of various technical proposals for the problem and the sub-problems from an automated analysis perspective, 3) a systematic review of the recent deep learning studies and available OCT/OCTA datasets directly aimed at the diagnosis of Alzheimer's Disease and Cognitive Impairment. For the latter, we used Publish or Perish Software to search for the relevant studies from various sources such as Scopus, PubMed, and Web of Science. We followed the PRISMA approach to screen an initial pool of 3073 references and determined ten relevant studies (N=10, out of 3073) that directly targeted AD diagnosis. We identified the lack of open OCT/OCTA datasets (about Alzheimer's disease) as the main issue that is impeding the progress in the field.Comment: Submitted to Computerized Medical Imaging and Graphics. Concept, methodology, invest, data curation, and writing org.draft by Yasemin Turkan. Concept, method, writing review editing, and supervision by F. Boray Te

Early Diagnosis of Alzheimer's disease by NIRF Spectroscopy and Nuclear Medicine-v.4.0

There is an urgent need for the early detection of diseases such as Alzheimer’s (AD) and Cancers in order to enable their successful treatment. Cancer is the second major cause of death after Heart Disease, and AD is the third major cause of death with major, human and financial/economics trillion dollar consequences for the society. Nuclear Medicine is concerned with applications in Medicine of Nuclear Science and Engineering techniques and knowledge. Three major Nuclear Medicine techniques that are established for diagnostic and research purposes are: Positron Emission Tomography (PET) and CAT/CT, Nuclear Magnetic Resonance Imaging (NMRI/MRI). However, these three techniques have also major limitations in terms of either cost or image resolution, as well as patient irradiation in the case of CAT/CT and PET. On the other hand, Near Infrared Chemical Imaging Microspectroscopy and certain Fluorescence spectroscopic techniques are capable of single cancer cell and/or single molecule detection and/or imaging. Such powerful capabilities, combined with low cost of diagnostics, make these novel techniques very attractive means for early detection of diseases such as cancer and Alzheimer’s, that are promising to reduce the fatality rate of patients through adequate diagnosis and treatment of such diseases at early stages. 
Currently NIH provides only inadequate funding for the clinical and research aspects of these novel investigation and clinical diagnostic techniques by FT-NIRS and Fluorescence spectrocopy for early detection of Alzheimer’s and Cancers.
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Early Diagnosis of Alzheimer's Disease by NIRF Spectroscopy\ud and Nuclear Medicine\ud

Novel approaches to Early Diagnosis of Alzheimer's Disease by NIRF Spectroscopy and Nuclear Medicine are presented and related cognitive, as well as molecular and cellular, models are critically evaluated.\u

Early Diagnosis of Alzheimer's disease by NIRF Spectroscopy and Nuclear Medicine

There is an urgent need for the early detection of diseases such as Alzheimer’s (AD) and Cancers in order to enable their successful treatment. Cancer is the second major cause of death after Heart Disease, and AD is the third major cause of death with major, human and financial/economics trillion dollar consequences for the society. Nuclear Medicine is concerned with applications in Medicine of Nuclear Science and Engineering techniques and knowledge. Three major Nuclear Medicine techniques that are established for diagnostic and research purposes are: Positron Emission Tomography (PET) and CAT/CT, Nuclear Magnetic Resonance Imaging (NMRI/MRI). However, these three techniques have also major limitations in terms of either cost or image resolution, as well as patient irradiation in the case of CAT/CT and PET. On the other hand, Near Infrared Chemical Imaging Microspectroscopy and certain Fluorescence spectroscopic techniques are capable of single cancer cell and/or single molecule detection and/or imaging. Such powerful capabilities, combined with low cost of diagnostics, make these novel techniques very attractive means for early detection of diseases such as cancer and Alzheimer’s, that are promising to reduce the fatality rate of patients through adequate diagnosis and treatment of such diseases at early stages. 
Currently NIH provides only inadequate funding for the clinical and research aspects of these novel investigation and clinical diagnostic techniques by FT-NIRS and Fluorescence spectrocopy for early detection of Alzheimer's and Cancers

Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.

Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders