First symptom guides diagnosis and prognosis in neurodegenerative diseases – a retrospective study of autopsy proven cases

Introduction Recent developments in artificial intelligence (AI) and neuroimaging offer new opportunities for improving diagnosis and prognosis of dementia. To synthesise the available literature, we performed a systematic review. Methods We systematically reviewed primary research publications up to January 2021, using AI for neuroimaging to predict diagnosis and/or prognosis in cognitive neurodegenerative diseases. After initial screening, data from each study was extracted, including: demographic information, AI methods, neuroimaging features, and results. Results We found 2709 reports, with 252 eligible papers remaining following screening. Most studies relied on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset (n=178) with no other individual dataset used more than 5 times. Algorithmic classifiers, such as support vector machine (SVM), were the most commonly used AI method (47%) followed by discriminative (32%) and generative (11%) classifiers. Structural MRI was used in 71% of studies with a wide range of accuracies for the diagnosis of neurodegenerative diseases and predicting prognosis. Lower accuracy was found in studies using a multi-class classifier or an external cohort as the validation group. There was improvement in accuracy when neuroimaging modalities were combined, e.g. PET and structural MRI. Only 17 papers studied non-Alzheimers disease dementias. Conclusion The use of AI with neuroimaging for diagnosis and prognosis in dementia is a rapidly emerging field. We make a number of recommendations addressing the definition of key clinical questions, heterogeneity of AI methods, and the availability of appropriate and representative data. We anticipate that addressing these issues will enable the field to move towards meaningful clinical translation.

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Salivary Redox Biomarkers in Selected Neurodegenerative Diseases

Journal of Clinical Medicine ◽

10.3390/jcm9020497 ◽

2020 ◽

Vol 9 (2) ◽

pp. 497 ◽

Cited By ~ 12

Author(s):

Mateusz Maciejczyk ◽

Anna Zalewska ◽

Karolina Gerreth

Keyword(s):

Oxidative Stress ◽

Neurodegenerative Diseases ◽

Neuronal Apoptosis ◽

Medical Personnel ◽

Brain Inflammation ◽

Non Invasive ◽

Diagnosis And Prognosis ◽

The Central Nervous System ◽

Saliva Collection ◽

Cerebral Degeneration

Neurodegenerative diseases (NDDs), such as Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease, are disorders, which cause irreversible and progressive deterioration of the central nervous system. The pathophysiology of NDDs is still not fully explained; nevertheless, oxidative stress is considered as a critical mediator of cerebral degeneration, brain inflammation, as well as neuronal apoptosis. Therefore, it is not surprising that redox biomarkers are increasingly used in the diagnosis of neurodegenerative diseases. As saliva is a very easy to obtain bioliquid, it seems promising to use this biomaterial in the diagnosis of NDDs. Saliva collection is easy, cheap, stress-free, and non-infectious, and it does not require the help of a specialised medical personnel. Additionally, the concentrations of many salivary redox biomarkers correlate with their content in blood serum as well as the degree of disease progression, which makes them non-invasive indicators of NDDs. This paper reviews the latest knowledge concerning the use of salivary redox biomarkers in the diagnosis and prognosis of selected neurodegenerative diseases.

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Peer Review #2 of "Metagenomic next-generation sequencing for the clinical diagnosis and prognosis of acute respiratory distress syndrome caused by severe pneumonia: a retrospective study (v0.1)"

10.7287/peerj.9623v0.1/reviews/2 ◽

2020 ◽

Author(s):

A Liu

Keyword(s):

Acute Respiratory Distress Syndrome ◽

Retrospective Study ◽

Next Generation Sequencing ◽

Respiratory Distress Syndrome ◽

Respiratory Distress ◽

Peer Review ◽

Distress Syndrome ◽

Severe Pneumonia ◽

Diagnosis And Prognosis ◽

Generation Sequencing

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