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Diagnostics
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Application of Advanced Mathematical Techniques in Medical Diagnosis
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Application of Advanced Mathematical Techniques in Medical Diagnosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 2284

Special Issue Editor

Dr. Ehsan Nazemi

E-Mail Website
Guest Editor
Faculty of Engineering and Physical Sciences, Southampton University, Southampton, UK
Interests: X-ray imaging; radiation; neural network; neutron/gamma radiography; diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

The application of mathematics and engineering to healthcare and medicine is gaining momentum as the mutual benefits of this collaboration become increasingly obvious. The scope of this theme issue is to give a general view of current research in the application of advanced mathematical methods to medicine as well as to show how these techniques can help in such important aspects as understanding, prediction, correlation, diagnosing, treatment, and data processing.

This Special Issue will provide a forum for discussing exciting research on applying various kinds of advanced mathematical techniques, such as neural networks, data mining, feature selection, imaging data processing, correlation analysis, etc. in the mental health, physical healthcare, and medicine fields in a broad sense.

Dr. Ehsan Nazemi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • neural networks
  • computational intelligence
  • data mining
  • correlation
  • feature selection
  • diagnosing
  • treatment
  • imaging data processing
  • mental health
  • physical healthcare
  • medicine
  • prediction
  • recognition
  • imaging systems
  • numerical methods
  • ultrasonic
  • statistical methods
  • biomarker

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

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15 pages, 4766 KiB  
Article
Noise Reduction Using Singular Value Decomposition with Jensen–Shannon Divergence for Coronary Computed Tomography Angiography
by Ryosuke Kasai and Hideki Otsuka
Diagnostics 2023, 13(6), 1111; https://doi.org/10.3390/diagnostics13061111 - 15 Mar 2023
Viewed by 2468
Abstract
Coronary computed tomography angiography (CCTA) is widely used due to its improvements in computed tomography (CT) diagnostic performance. Unlike other CT examinations, CCTA requires shorter rotation times of the X-ray tube, improving the temporal resolution and facilitating the imaging of the beating heart [...] Read more.
Coronary computed tomography angiography (CCTA) is widely used due to its improvements in computed tomography (CT) diagnostic performance. Unlike other CT examinations, CCTA requires shorter rotation times of the X-ray tube, improving the temporal resolution and facilitating the imaging of the beating heart in a stationary state. However, reconstructed CT images, including those of the coronary arteries, contain insufficient X-ray photons and considerable noise. In this study, we introduce an image-processing technique for noise reduction using singular value decomposition (SVD) for CCTA images. The threshold of SVD was determined on the basis of minimization of Jensen–Shannon (JS) divergence. Experiments were performed with various numerical phantoms and varying levels of noise to reduce noise in clinical CCTA images using the determined threshold value. The numerical phantoms produced 10% higher-quality images than the conventional noise reduction method when compared on a quantitative SSIM basis. The threshold value determined by minimizing the JS–divergence was found to be useful for efficient noise reduction in actual clinical images, depending on the level of noise. Full article
(This article belongs to the Special Issue Application of Advanced Mathematical Techniques in Medical Diagnosis)
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8 pages, 232 KiB  
Article
Using Mathematical and Statistical Analysis to Investigate the Correlation between Exacerbation of Chronic Obstructive Pulmonary Disease and Risk of Subclinical Atherosclerosis
by Xiaochun Dai, Jiahui Chen and Lili Shao
Diagnostics 2023, 13(4), 623; https://doi.org/10.3390/diagnostics13040623 - 8 Feb 2023
Viewed by 1283
Abstract
Purpose: As the number of patients with chronic obstructive pulmonary disease continues to increase, it is increasingly important to understand the impact of cardiovascular risk on the progression of chronic obstructive pulmonary disease, which can provide guidance for clinical medication and recommendations for [...] Read more.
Purpose: As the number of patients with chronic obstructive pulmonary disease continues to increase, it is increasingly important to understand the impact of cardiovascular risk on the progression of chronic obstructive pulmonary disease, which can provide guidance for clinical medication and recommendations for patient care and rehabilitation. The purpose of this study was to investigate the relationship between cardiovascular risk and the progression of chronic obstructive pulmonary disease (COPD). Methods: Selected COPD patients admitted to hospital from June 2018 to July 2020 were included in the study for prospective analysis, and patients who showed more than two instances of moderate deterioration or severe deterioration within one year before the consultation were defined as COPD patients, and all participants underwent relevant tests and assessments. Results: Multivariate correction analysis showed that a worsening phenotype improved the risk of carotid artery intima-media thickness exceeding 75% by nearly three times, and it had no relation with the degree of COPD severity and global cardiovascular risk; in addition, the relationship between a worsening phenotype and high carotid intima-media thickness (c-IMT) was more pronounced in patients under 65 years of age. Conclusions: The existence of subclinical atherosclerosis is individually related to the worsening phenotype, and the difference is more obvious in young patients. Therefore, the control of vascular risk factors in these patients should be strengthened. Full article
(This article belongs to the Special Issue Application of Advanced Mathematical Techniques in Medical Diagnosis)
9 pages, 2434 KiB  
Article
Proposing Intelligent Approach to Predicting Air Kerma within Radiation Beams of Medical X-ray Imaging Systems
by Yanjie Lu, Nan Zheng, Mingtao Ye, Yihao Zhu, Guodao Zhang, Ehsan Nazemi and Jie He
Diagnostics 2023, 13(2), 190; https://doi.org/10.3390/diagnostics13020190 - 4 Jan 2023
Cited by 4 | Viewed by 2003
Abstract
The air kerma is a key parameter in medical diagnostic radiology. Radiologists use the air kerma parameter to evaluate organ doses and any associated patient hazards. The air kerma can be simply described as the deposited kinetic energy once a photon passes through [...] Read more.
The air kerma is a key parameter in medical diagnostic radiology. Radiologists use the air kerma parameter to evaluate organ doses and any associated patient hazards. The air kerma can be simply described as the deposited kinetic energy once a photon passes through the air, and it represents the intensity of the radiation beam. Due to the heel effect in the X-ray sources of medical imaging systems, the air kerma is not uniform within the X-ray beam’s field of view. Additionally, the X-ray tube voltage can also affect this nonuniformity. In this investigation, an intelligent technique based on the radial basis function neural network (RBFNN) is presented to predict the air kerma at every point within the fields of view of the X-ray beams of medical diagnostic imaging systems based on discrete and limited measured data. First, a diagnostic imaging system was modeled with the help of the Monte Carlo N Particle X version (MCNPX) code. It should be noted that a tungsten target and beryllium window with a thickness of 1 mm (no extra filter was applied) were used for modeling the X-ray tube. Second, the air kerma was calculated at various discrete positions within the conical X-ray beam for tube voltages of 40 kV, 60 kV, 80 kV, 100 kV, 120 kV, and 140 kV (this range covers most medical X-ray imaging applications) to provide the adequate dataset for training the network. The X-ray tube voltage and location of each point at which the air kerma was calculated were used as the RBFNN inputs. The calculated air kerma was also assigned as the output. The trained RBFNN model was capable of estimating the air kerma at any random position within the X-ray beam’s field of view for X-ray tube voltages within the range of medical diagnostic radiology (20–140 kV). Full article
(This article belongs to the Special Issue Application of Advanced Mathematical Techniques in Medical Diagnosis)
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Review

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12 pages, 5225 KiB  
Review
Clinical Applications of B-Flow Ultrasound: A Scoping Review of the Literature
by Amun G. Hofmann, Irene Mlekusch, Georg Wickenhauser, Afshin Assadian and Fadi Taher
Diagnostics 2023, 13(3), 397; https://doi.org/10.3390/diagnostics13030397 - 21 Jan 2023
Cited by 10 | Viewed by 3144
Abstract
Coded excitation ultrasound investigations have the potential to augment the resolution, increase the efficiency, and expand the possibilities of noninvasive diagnostic imaging. B-Flow ultrasound, a type of digitally encoded imaging, was developed more than 20 years ago with the aim to optimize the [...] Read more.
Coded excitation ultrasound investigations have the potential to augment the resolution, increase the efficiency, and expand the possibilities of noninvasive diagnostic imaging. B-Flow ultrasound, a type of digitally encoded imaging, was developed more than 20 years ago with the aim to optimize the visualization of blood flow. It has been investigated for a plethora of applications so far. A scoping review regarding its clinical applications was conducted based on a systematic literature research. B-Flow has been investigated in various anatomic locations and pathologies. However, previous research is limited by small sample sizes, the rare occurrence of elaborate study designs, the reliance on subjective reports and qualitative data, as well as several potential biases. While results are in general promising, it should therefore still be considered an emerging technology. Nevertheless, the limitations can be addressed in future research and the potential to expand its applications make B-Flow an interesting candidate for further investigations. Full article
(This article belongs to the Special Issue Application of Advanced Mathematical Techniques in Medical Diagnosis)
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