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Diagnostics
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Computer-Assisted Diagnosis and Treatment of Mental Disorders
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Computer-Assisted Diagnosis and Treatment of Mental Disorders

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 24199

Special Issue Editor

Dr. Hamid Reza Marateb

E-Mail Website
Guest Editor
BIOsignal Analysis for Rehabilitation and Therapy Research Group (BIOART), Universitat Politècnica de Catalunya, BarcelonaTech (UPC), Barcelona, Spain
Interests: biomedical signal processing; cognitive informatics in health and biomedicine; computer-assisted diagnosis and prognosis; medical data mining; neurological diagnostic techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mental disorders (a.k.a., mental illnesses, or psychiatric disorders) are behavioral or mental patterns causing significant distress or impairment of personal functioning. There are nearly 300 mental disorders listed in the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders), including depression, anxiety, post-traumatic stress, bipolar disorders, and schizophrenia [1]. According to the Global Burden of Diseases (GBD 2017) study, mental disorders are among the top disability factors, and their burden is present in both sexes and across all age groups [2]. Mental illness and their associated issues have attracted the attention of professionals in various disciplines and have been considered as a public health concern [3]. The identification of such disorders in the early stage is crucial to prevent them from reaching a severe and irreversible state. 

Computer-assisted diagnosis (CAD) could be used for the diagnosis and prognosis of these disorders. Using data mining methods and signal processing of biopotential recordings (e.g., electroencephalogram) could be used as tools for making their objective diagnosis and prognosis [4-6]. This special issue aims to explore and collect ongoing research activities on the diagnosis and prognosis of mental disorders using CAD.

Dr. Hamid Reza Marateb
Guest Editor

References:

  1. American Psychiatric Association. and American Psychiatric Association. DSM-5 Task Force., Diagnostic and statistical manual of mental disorders : DSM-5. 5th ed. 2013, Washington, D.C.: American Psychiatric Association. xliv, 947 p.
  2. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet, 2018. 392(10159): p. 1789-1858.
  3. Knifton, L. and N. Quinn, Public mental health: global perspectives. 2013: McGraw-Hill Education (UK).
  4. Mumtaz, W., et al., A wavelet-based technique to predict treatment outcome for Major Depressive Disorder. PLOS ONE, 2017. 12(2): p. e0171409.
  5. Wu, C.-T., et al., Depression detection using relative EEG power induced by emotionally positive images and a conformal kernel support vector machine. Applied Sciences, 2018. 8(8): p. 1244.
  6. Alonso, S.G., et al., Data Mining Algorithms and Techniques in Mental Health: A Systematic Review. J Med Syst, 2018. 42(9): p. 161.

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

  • Computer-assisted diagnosis and prognosis
  • Mental disorders
  • Data mining
  • Neurological diagnostic techniques
  • Computer-assisted signal processing
  • Early diagnosis

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Published Papers (5 papers)

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Research

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27 pages, 3379 KiB  
Article
Towards Automated Eye Diagnosis: An Improved Retinal Vessel Segmentation Framework Using Ensemble Block Matching 3D Filter
by Khuram Naveed, Faizan Abdullah, Hussain Ahmad Madni, Mohammad A.U. Khan, Tariq M. Khan and Syed Saud Naqvi
Diagnostics 2021, 11(1), 114; https://doi.org/10.3390/diagnostics11010114 - 12 Jan 2021
Cited by 37 | Viewed by 3586
Abstract
Automated detection of vision threatening eye disease based on high resolution retinal fundus images requires accurate segmentation of the blood vessels. In this regard, detection and segmentation of finer vessels, which are obscured by a considerable degree of noise and poor illumination, is [...] Read more.
Automated detection of vision threatening eye disease based on high resolution retinal fundus images requires accurate segmentation of the blood vessels. In this regard, detection and segmentation of finer vessels, which are obscured by a considerable degree of noise and poor illumination, is particularly challenging. These noises include (systematic) additive noise and multiplicative (speckle) noise, which arise due to various practical limitations of the fundus imaging systems. To address this inherent issue, we present an efficient unsupervised vessel segmentation strategy as a step towards accurate classification of eye diseases from the noisy fundus images. To that end, an ensemble block matching 3D (BM3D) speckle filter is proposed for removal of unwanted noise leading to improved detection. The BM3D-speckle filter, despite its ability to recover finer details (i.e., vessels in fundus images), yields a pattern of checkerboard artifacts in the aftermath of multiplicative (speckle) noise removal. These artifacts are generally ignored in the case of satellite images; however, in the case of fundus images, these artifacts have a degenerating effect on the segmentation or detection of fine vessels. To counter that, an ensemble of BM3D-speckle filter is proposed to suppress these artifacts while further sharpening the recovered vessels. This is subsequently used to devise an improved unsupervised segmentation strategy that can detect fine vessels even in the presence of dominant noise and yields an overall much improved accuracy. Testing was carried out on three publicly available databases namely Structured Analysis of the Retina (STARE), Digital Retinal Images for Vessel Extraction (DRIVE) and CHASE_DB1. We have achieved a sensitivity of 82.88, 81.41 and 82.03 on DRIVE, SATARE, and CHASE_DB1, respectively. The accuracy is also boosted to 95.41, 95.70 and 95.61 on DRIVE, SATARE, and CHASE_DB1, respectively. The performance of the proposed methods on images with pathologies was observed to be more convincing than the performance of similar state-of-the-art methods. Full article
(This article belongs to the Special Issue Computer-Assisted Diagnosis and Treatment of Mental Disorders)
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22 pages, 504 KiB  
Article
Accurate Diagnosis of Suicide Ideation/Behavior Using Robust Ensemble Machine Learning: A University Student Population in the Middle East and North Africa (MENA) Region
by Azam Naghavi, Tobias Teismann, Zahra Asgari, Mohammad Reza Mohebbian, Marjan Mansourian and Miguel Ángel Mañanas
Diagnostics 2020, 10(11), 956; https://doi.org/10.3390/diagnostics10110956 - 16 Nov 2020
Cited by 16 | Viewed by 4096
Abstract
Suicide is one of the most critical public health concerns in the world and the second cause of death among young people in many countries. However, to date, no study can diagnose suicide ideation/behavior among university students in the Middle East and North [...] Read more.
Suicide is one of the most critical public health concerns in the world and the second cause of death among young people in many countries. However, to date, no study can diagnose suicide ideation/behavior among university students in the Middle East and North Africa (MENA) region using a machine learning approach. Therefore, stability feature selection and stacked ensembled decision trees were employed in this classification problem. A total of 573 university students responded to a battery of questionnaires. Three-fold cross-validation with a variety of performance indices was sued. The proposed diagnostic system had excellent balanced diagnosis accuracy (AUC = 0.90 [CI 95%: 0.86–0.93]) with a high correlation between predicted and observed class labels, fair discriminant power, and excellent class labeling agreement rate. Results showed that 23 items out of all items could accurately diagnose suicide ideation/behavior. These items were psychological problems and how to experience trauma, from the demographic variables, nine items from Post-Traumatic Stress Disorder Checklist (PCL-5), two items from Post Traumatic Growth (PTG), two items from the Patient Health Questionnaire (PHQ), six items from the Positive Mental Health (PMH) questionnaire, and one item related to social support. Such features could be used as a screening tool to identify young adults who are at risk of suicide ideation/behavior. Full article
(This article belongs to the Special Issue Computer-Assisted Diagnosis and Treatment of Mental Disorders)
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12 pages, 465 KiB  
Article
Elaboration of Screening Scales for Mental Development Problems Detection in Russian Preschool Children: Psychometric Approach
by Andrey Nasledov, Sergey Miroshnikov, Liubov Tkacheva and Vadim Goncharov
Diagnostics 2020, 10(9), 646; https://doi.org/10.3390/diagnostics10090646 - 28 Aug 2020
Cited by 4 | Viewed by 3126
Abstract
Background: computer-based screenings are usually used for early detection of a child’s mental development problems. However, there are no such screenings in Russia yet. This study aimed to elaborate scales for rapid monitoring of mental development of 3-year-olds. Methods: 863 children took part [...] Read more.
Background: computer-based screenings are usually used for early detection of a child’s mental development problems. However, there are no such screenings in Russia yet. This study aimed to elaborate scales for rapid monitoring of mental development of 3-year-olds. Methods: 863 children took part in the study, among them 814 children of the group Norm, 49 children with developmental delay (DD), including 23 children with symptoms of autistic spectrum disorder (ASD). The multifactor study of mental development tool was used as a part of a software complex for longitudinal research for data collection. This study used a set of 233 tasks that were adequate for 3-year-olds. Exploratory and confirmatory factor analysis was used for the elaboration and factor validation of the scales. The structure of the relationship between scales and age was refined using structural equation modeling. Results: as a result of the research, screening scales were elaborated: “Logical reasoning”, “Motor skills”, “General awareness”, “Executive functions”. The factor validity and reliability of scales were proved. The high discriminability of the scales in distinguishing the “Norm” and “DD” samples was revealed. The developed test norms take into account the child’s age in days and allow identifying a “risk group” with an expected forecast accuracy of at least 90%. The obtained scales meet psychometric requirements for their application and allow creating an online screening system for wide application. Full article
(This article belongs to the Special Issue Computer-Assisted Diagnosis and Treatment of Mental Disorders)
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26 pages, 3008 KiB  
Article
An Effective Mental Stress State Detection and Evaluation System Using Minimum Number of Frontal Brain Electrodes
by Omneya Attallah
Diagnostics 2020, 10(5), 292; https://doi.org/10.3390/diagnostics10050292 - 9 May 2020
Cited by 87 | Viewed by 6305
Abstract
Currently, mental stress is a common social problem affecting people. Stress reduces human functionality during routine work and may lead to severe health defects. Detecting stress is important in education and industry to determine the efficiency of teaching, to improve education, and to [...] Read more.
Currently, mental stress is a common social problem affecting people. Stress reduces human functionality during routine work and may lead to severe health defects. Detecting stress is important in education and industry to determine the efficiency of teaching, to improve education, and to reduce risks from human errors that might occur due to workers’ stressful situations. Therefore, the early detection of mental stress using machine learning (ML) techniques is essential to prevent illness and health problems, improve quality of education, and improve industrial safety. The human brain is the main target of mental stress. For this reason, an ML system is proposed which investigates electroencephalogram (EEG) signal for thirty-six participants. Extracting useful features is essential for an efficient mental stress detection (MSD) system. Thus, this framework introduces a hybrid feature-set that feeds five ML classifiers to detect stress and non-stress states, and classify stress levels. To produce a reliable, practical, and efficient MSD system with a reduced number of electrodes, the proposed MSD scheme investigates the electrodes placements on different sites on the scalp and selects that site which has the higher impact on the accuracy of the system. Principal Component analysis is employed also, to reduce the features extracted from such electrodes to lower model complexity, where the optimal number of principal components is examined using sequential forward procedure. Furthermore, it examines the minimum number of electrodes placed on the site which has greater impact on stress detection and evaluation. To test the effectiveness of the proposed system, the results are compared with other feature extraction methods shown in literature. They are also compared with state-of-the-art techniques recorded for stress detection. The highest accuracies achieved in this study are 99.9%(sd = 0.015) and 99.26% (sd = 0.08) for identifying stress and non-stress states, and distinguishing between stress levels, respectively, using only two frontal brain electrodes for detecting stress and non-stress, and three frontal electrodes for evaluating stress levels respectively. The results show that the proposed system is reliable as the sensitivity is 99.9(0.064), 98.35(0.27), specificity is 99.94(0.02), 99.6(0.05), precision is 99.94(0.06), 98.9(0.23), and the diagnostics odd ratio (DOR) is ≥ 100 for detecting stress and non-stress, and evaluating stress levels respectively. This shows that the proposed framework has compelling performance and can be employed for stress detection and evaluation in medical, educational and industrial fields. Finally, the results verified the efficiency and reliability of the proposed system in predicting stress and non-stress on new patients, as the accuracy achieved 98.48% (sd = 1.12), sensitivity = 97.78% (sd = 1.84), specificity = 97.75% (sd = 2.05), precision = 99.26% (sd = 0.67), and DOR ≥ 100 using only two frontal electrodes. Full article
(This article belongs to the Special Issue Computer-Assisted Diagnosis and Treatment of Mental Disorders)
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Review

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35 pages, 5223 KiB  
Review
A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining
by Mahsa Mansourian, Sadaf Khademi and Hamid Reza Marateb
Diagnostics 2021, 11(3), 393; https://doi.org/10.3390/diagnostics11030393 - 25 Feb 2021
Cited by 12 | Viewed by 5756
Abstract
The World Health Organization (WHO) suggests that mental disorders, neurological disorders, and suicide are growing causes of morbidity. Depressive disorders, schizophrenia, bipolar disorder, Alzheimer’s disease, and other dementias account for 1.84%, 0.60%, 0.33%, and 1.00% of total Disability Adjusted Life Years (DALYs). Furthermore, [...] Read more.
The World Health Organization (WHO) suggests that mental disorders, neurological disorders, and suicide are growing causes of morbidity. Depressive disorders, schizophrenia, bipolar disorder, Alzheimer’s disease, and other dementias account for 1.84%, 0.60%, 0.33%, and 1.00% of total Disability Adjusted Life Years (DALYs). Furthermore, suicide, the 15th leading cause of death worldwide, could be linked to mental disorders. More than 68 computer-aided diagnosis (CAD) methods published in peer-reviewed journals from 2016 to 2021 were analyzed, among which 75% were published in the year 2018 or later. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was adopted to select the relevant studies. In addition to the gold standard, the sample size, neuroimaging techniques or biomarkers, validation frameworks, the classifiers, and the performance indices were analyzed. We further discussed how various performance indices are essential based on the biostatistical and data mining perspective. Moreover, critical information related to the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines was analyzed. We discussed how balancing the dataset and not using external validation could hinder the generalization of the CAD methods. We provided the list of the critical issues to consider in such studies. Full article
(This article belongs to the Special Issue Computer-Assisted Diagnosis and Treatment of Mental Disorders)
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