Diagnosis of Alzheimer's disease using laser-induced breakdown spectroscopy and machine learning

Gaudiuso, R.; Ewusi-Annan, Ebo; Xia, Weiming; Melikechi, Noureddine

doi:10.1016/j.sab.2020.105931

Cited by 33 publications

(15 citation statements)

References 17 publications

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“…The data analysis approach we adopted for this work is a modified version of the difference spectrum method that we developed in Gaudiuso et al. 37 We started by generating a classification model by using eight samples of known status (four GWI pos, four GWI neg). We averaged the LIBS spectra of the GWI pos samples, normalized over the total emission intensity, and did the same for the controls, to generate two mean spectra, one for each class.…”

Section: Resultsmentioning

confidence: 99%

“…The LIBS experimental setup and method used for this work are described in a previous publication from our group. 37 Essentially, they consist of a focusing 7 ns neodymium-doped yttrium aluminum garnet (Nd:YAG) laser (Surelite II, Continuum) pulses operating at 1064 nm on samples using an air-spaced doublet lens with focal length of 30 mm. The samples to be analyzed were loaded onto a three-dimensional computer-controlled translation stage located within a chamber (SciTrace, AtomTrace).…”

Section: Methodsmentioning

confidence: 99%

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Diagnosis of Gulf War Illness Using Laser-Induced Spectra Acquired from Blood Samples

et al. 2021

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Gulf War illness (GWI) is a chronic illness with no known validated biomarkers that affects the lives of hundreds of thousands of people. As a result, there is an urgent need for the development of an untargeted and unbiased method to distinguish GWI patients from non-GWI patients. We report on the application of laser-induced breakdown spectroscopy (LIBS) to distinguish blood plasma samples from a group of subjects with GWI and from subjects with chronic low back pain as controls. We initially obtained LIBS data from blood plasma samples of four GWI patients and four non-GWI patients. We used an analytical method based on taking the difference between a mean LIBS spectrum obtained with those of GWI patients from the mean LIBS spectrum of those of the control group, to generate a “difference” spectrum for our classification model. This model was cross-validated using different numbers of differential LIBS emission peaks. A subset of 17 of the 82 atomic and ionic transitions that provided 70% of correct diagnosis was selected test in a blinded fashion using 10 additional samples and was found to yield 90% classification accuracy, 100% sensitivity, and 83.3% specificity. Of the 17 atomic and ionic transitions, eight could be assigned unambiguously to species of Na, K, and Fe.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Diagnosis of Gulf War Illness Using Laser-Induced Spectra Acquired from Blood Samples

et al. 2021

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“…The LIBS experimental setup used for this work is described elsewhere (e.g. 12 ). Briefly, it consists of focusing 7 ns Nd:YAG laser (Surelite II, Continuum) pulses operating at 1064 nm on samples using an air-spaced doublet lens with focal length of 30 mm.…”

Section: Methodsmentioning

confidence: 99%

“…It owes its recent increase in popularity to this versatility as well as the speed of analysis and relative experimental simplicity requiring next to no sample preparation and its success as the geochemist on Mars Rover, “Curiosity” 5 . LIBS has also been used successfully for human and animal health 6 , including in the detection of biomarkers of cancers, viruses and bacteria 7 – 12 .…”

Section: Introductionmentioning

confidence: 99%

Laser induced breakdown spectroscopy for the rapid detection of SARS-CoV-2 immune response in plasma

Berlo

Xia

Zwillich

et al. 2022

Sci Rep

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As the SARS-CoV-2 pandemic persists, methods that can quickly and reliably confirm infection and immune status is extremely urgently and critically needed. In this contribution we show that combining laser induced breakdown spectroscopy (LIBS) with machine learning can distinguish plasma of donors who previously tested positive for SARS-CoV-2 by RT-PCR from those who did not, with up to 95% accuracy. The samples were also analyzed by LIBS-ICP-MS in tandem mode, implicating a depletion of Zn and Ba in samples of SARS-CoV-2 positive subjects that inversely correlate with CN lines in the LIBS spectra.

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“…The produced biosignatures were based on blood miRNA, mRNA, and protein, respectively. In [65], a combination of laser-induced breakdown spectroscopy and a supervised ML algorithm (QDA) was used for analyzing the micro drops of plasma samples to diagnose a patient with AD or no AD. As a specimen for the analysis, 67 plasma samples from 31 AD patients and 36 NC were taken.…”

Section: ) Ml-based Approaches In Ad Diagnosismentioning

confidence: 99%

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

et al. 2021

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Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure it. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment. INDEX TERMS Alzheimer's disease, brain tumor, deep learning, epilepsy, Parkinson's disease, machine learning NOMENCLATURE The most commonly used abbreviations in this survey are summarized in Table 1.

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Diagnosis of Alzheimer's disease using laser-induced breakdown spectroscopy and machine learning

Cited by 33 publications

References 17 publications

Diagnosis of Gulf War Illness Using Laser-Induced Spectra Acquired from Blood Samples

Diagnosis of Gulf War Illness Using Laser-Induced Spectra Acquired from Blood Samples

Laser induced breakdown spectroscopy for the rapid detection of SARS-CoV-2 immune response in plasma

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

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