Nuclear magnetic resonance-based screening of thalassemia and quantification of some hematological parameters using chemometric methods

Arjmand, Mohammad; Kompany‐Zareh, Mohsen; Vasighi, Mahdi; Parvizzadeh, Nastran; Zamani, Zahra; Nazgooei, Fereshteh

doi:10.1016/j.talanta.2010.02.014

Cited by 19 publications

(14 citation statements)

References 32 publications

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“…The number of patients could vary from less than 20 patients [20,21,24,35,40,43,55] to more than 1000 patients [37,47,51,62,72,75]. As expected, the number of patients was usually more important for publications targeting more than one disease (groups 2 and 3) than for publications targeting one specific rare disease (group 1) ( Table 2).…”

Section: Methodsmentioning

confidence: 53%

“…Regarding the diseases under study, an important variability in the number of targeted diseases was identified. The most individually studied diseases were thalassemia [20,28,29,47], Down syndrome [21,30,34], cystic fibrosis [46,48,52], Marfan syndrome [25,27] and Huntington disease [19,33].…”

Section: Publication Targetmentioning

confidence: 99%

“…Support vector machine (SVM) was the most popular method, corresponding to 10 studies. Deep learning was used in 6 studies using images [44,57,59,62] or fluids [20,31] and exhibited good performance on such data. Associations of different algorithms, such as fusion algorithms, were encountered in 2 studies [52,53].…”

Section: Developed Modelsmentioning

confidence: 99%

“…The protocol to evaluate and validate the developed models was highly study-dependent and is detailed in the next subsections. The following characteristics were considered: Hybrid (7 studies) Yes [27,[73][74][75][76]78] No [25] Data driven (1 study) Yes [72] Fluids (12 studies) Hybrid (2 studies) Yes [28,29] Data driven (10 studies) Yes [20,23,24,31,36,42,47,48,56,60] Images (16 studies) Hybrid (2 studies) Yes [41,45] Data driven (14 studies) Yes [19, 21, 22, 30, 33-35, 43, 44, 49, 51, 57, 59, 62] Questionnaires (3 studies) Data driven (3 studies) Yes [52,53,58] Family history and combined material (8 studies) Knowledge-based (5 studies) No [26,32,38,46,50] Hybrid (2 studies) Yes [37,39] Data driven (1 study) Yes [40] References are listed in column "articles" according to the type of material considered and the model used (presence/absence of prior knowledge and of machine learning). The number of studies according to material and knowledge is given in parentheses Fig.…”

Section: Evaluation and Validationmentioning

confidence: 99%

See 3 more Smart Citations

Diagnosis support systems for rare diseases: a scoping review

Faviez

Chen

Garcelon

et al. 2020

Orphanet J Rare Dis

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Introduction: Rare diseases affect approximately 350 million people worldwide. Delayed diagnosis is frequent due to lack of knowledge of most clinicians and a small number of expert centers. Consequently, computerized diagnosis support systems have been developed to address these issues, with many relying on rare disease expertise and taking advantage of the increasing volume of generated and accessible health-related data. Our objective is to perform a review of all initiatives aiming to support the diagnosis of rare diseases. Methods: A scoping review was conducted based on methods proposed by Arksey and O'Malley. A charting form for relevant study analysis was developed and used to categorize data. Results: Sixty-eight studies were retained at the end of the charting process. Diagnosis targets varied from 1 rare disease to all rare diseases. Material used for diagnosis support consisted mostly of phenotype concepts, images or fluids. Fifty-seven percent of the studies used expert knowledge. Two-thirds of the studies relied on machine learning algorithms, and one-third used simple similarities. Manual algorithms were encountered as well. Most of the studies presented satisfying performance of evaluation by comparison with references or with external validation. Fourteen studies provided online tools, most of which aimed to support the diagnosis of all rare diseases by considering queries based on phenotype concepts. Conclusion: Numerous solutions relying on different materials and use of various methodologies are emerging with satisfying preliminary results. However, the variability of approaches and evaluation processes complicates the comparison of results. Efforts should be made to adequately validate these tools and guarantee reproducibility and explicability.

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Section: Methodsmentioning

confidence: 53%

Section: Publication Targetmentioning

confidence: 99%

Section: Developed Modelsmentioning

confidence: 99%

Section: Evaluation and Validationmentioning

confidence: 99%

See 2 more Smart Citations

Diagnosis support systems for rare diseases: a scoping review

Faviez

Chen

Garcelon

et al. 2020

Orphanet J Rare Dis

View full text Add to dashboard Cite

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“…Yekdeli Kermanshahi and Tabaraki et al classified Iranian bottled waters as indicated by manufacturers' labelings [224]. Nuclear magnetic resonancebased screening of thalassemia and quantification of some hematological parameters using chemometric methods were proposed by Arjmand et al [225]. Qualitative discrimination between paracetamol tablets made by near infrared spectroscopy and chemometrics with regard to polymorphism was reported by Fatemi et al [226].…”

Section: Classificationmentioning

confidence: 99%

A perspective on the growth of chemometrics in Iran: a glance into activities between 2005 and 2012

Naseri

Bahram

2013

Journal of Chemometrics

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Although the first publications in the field of chemometrics by Iranian researchers go back nearly 23 years, Iran's chemometrics has experienced significant growth after the year 2000. More than 1500 papers have been published by Iranian chemometricians between 2005 and 2012 which is about seven times more than those published before 2005. The aim of this review is to present a perspective about Iran's chemometrics developments. The role of enthusiastic students, motivated professors, annual workshops, and international collaborations is discussed in this work. After introducing the first papers in each field of chememotrics, the papers published during 2005–2012 were screened, and some of them were introduced. Copyright © 2013 John Wiley & Sons, Ltd.

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Clustering Nuclear Magnetic Resonance: Machine learning assistive rapid two‐dimensional relaxometry mapping

Peng

2021

Engineering Reports

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Low‐field nuclear magnetic resonance (NMR) relaxometry is an attractive approach from point‐of‐care testing medical diagnosis to in situ oil‐gas exploration. One of the problems, however, is the inherently long relaxation time of the (liquid) samples, (and hence low signal‐to‐noise ratio) which causes unnecessarily long repetition time. In this work, a new class of methodology is presented for rapid and accurate object classification using NMR relaxometry with the aid of machine learning. It is demonstrated that the sensitivity and specificity of the classification is substantially improved with higher order of (pseudo)‐dimensionality (e.g., 2D‐ or multi‐dimensional). This new methodology (termed as ‘Clustering NMR’) may be extremely useful for rapid and accurate object classification (in less than a minute) using low‐field NMR.

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Nuclear magnetic resonance-based screening of thalassemia and quantification of some hematological parameters using chemometric methods

Cited by 19 publications

References 32 publications

Diagnosis support systems for rare diseases: a scoping review

Diagnosis support systems for rare diseases: a scoping review

A perspective on the growth of chemometrics in Iran: a glance into activities between 2005 and 2012

Clustering Nuclear Magnetic Resonance: Machine learning assistive rapid two‐dimensional relaxometry mapping

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