A Review on Recent Advancements in Diagnosis and Classification of Cancers Using Artificial Intelligence

Research output related to artificial intelligence (AI) in vascular diseases has been poorly investigated. The aim of this study was to evaluate scientific publications on AI in non-cardiac vascular diseases. A systematic literature search was conducted using the PubMed database and a combination of keywords and focused on three main vascular diseases (carotid, aortic and peripheral artery diseases). Original articles written in English and published between January 1995 and December 2020 were included. Data extracted included the date of publication, the journal, the identity, number, affiliated country of authors, the topics of research, and the fields of AI. Among 171 articles included, the three most productive countries were USA, China, and United Kingdom. The fields developed within AI included: machine learning (n = 90; 45.0%), vision (n = 45; 22.5%), robotics (n = 42; 21.0%), expert system (n = 15; 7.5%), and natural language processing (n = 8; 4.0%). The applications were mainly new tools for: the treatment (n = 52; 29.1%), prognosis (n = 45; 25.1%), the diagnosis and classification of vascular diseases (n = 38; 21.2%), and imaging segmentation (n = 38; 21.2%). By identifying the main techniques and applications, this study also pointed to the current limitations and may help to better foresee future applications for clinical practice.

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Automated classification of colorectal adenoma-dysplasia-carcinoma in tissue biopsies using artificial intelligence based image evaluation

Zeitschrift für Gastroenterologie ◽

10.1055/s-0032-1312443 ◽

2012 ◽

Vol 50 (05) ◽

Author(s):

B Wichmann ◽

P Ittzés ◽

G Valcz ◽

D Szabó ◽

B Barták ◽

...

Keyword(s):

Artificial Intelligence ◽

Colorectal Adenoma ◽

Automated Classification ◽

Image Evaluation

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Determination of effective parameters for diagnosis and classification of air-conditioning refrigerant noise by logistic regression

Noise Control Engineering Journal ◽

10.3397/1/376635 ◽

2018 ◽

Vol 66 (5) ◽

pp. 415-423 ◽

Cited By ~ 1

Author(s):

Yong-Dae Kim ◽

Kook-Hyun Yoo ◽

Jae-Eung Oh

Keyword(s):

Logistic Regression ◽

Air Conditioning ◽

Effective Parameters ◽

Diagnosis And Classification

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Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405616666200406110547 ◽

2020 ◽

Vol 16 ◽

Cited By ~ 3

Author(s):

Saliha Zahoor ◽

Ikram Ullah Lali ◽

Muhammad Attique Khan ◽

Kashif Javed ◽

Waqar Mehmood

Keyword(s):

Breast Cancer ◽

Computer Aided Diagnosis ◽

Diagnose Breast Cancer ◽

Initial Stage ◽

Computer Aided ◽

Diagnosis And Classification ◽

And Performance ◽

Tools And Techniques ◽

Aided Diagnosis

: Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

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Depression

10.1093/oso/9780198801900.003.0004 ◽

2017 ◽

Author(s):

Philip Cowen

Keyword(s):

International Classification Of Diseases ◽

Behavioural Therapy ◽

Historical Background ◽

Cognitive Behavioural ◽

Classification Of Diseases ◽

Icd 10 ◽

Diagnosis And Classification ◽

Modern Experience ◽

Symptoms And Signs

This chapter discusses the symptomatology, diagnosis, and classification of depression. It begins with a brief historical background on depression, tracing its origins to the classical term ‘melancholia’ that describes symptoms and signs now associated with modern concepts of the condition. It then considers the phenomenology of the modern experience of depression, its diagnosis in the operational scheme of ICD-10 (International Classification of Diseases, tenth edition), and current classificatory schemes. It looks at the symptoms needed to meet the criteria for ‘depressive episode’ in ICD-10, as well as clinical features of depression with ‘melancholic’ features or ‘somatic depression’ in ICD-10. It also presents an outline of the clinical assessment of an episode of depression before concluding with an overview of issues that need to be taken into account when addressing approaches to treatment, including cognitive behavioural therapy and the administration of antidepressants.

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Diagnoses, Dimensions, and DSM-5

The Oxford Handbook of Clinical Psychology ◽

10.1093/oxfordhb/9780195366884.013.0013 ◽

2010 ◽

pp. 254-279

Author(s):

Thomas A. Widiger ◽

Maryanne Edmundson

Keyword(s):

Substance Use ◽

Mental Disorders ◽

Paradigm Shift ◽

Empirical Support ◽

Statistical Manual ◽

Diagnostic And Statistical Manual ◽

Dsm 5 ◽

Dimensional Classification ◽

Diagnosis And Classification

The Diagnostic and Statistical Manual of Mental Disorders, Third Edition (DSM-III) is often said to have provided a significant paradigm shift in how psychopathology is diagnosed. The authors of DSM-5 have the empirical support and the opportunity to lead the field of psychiatry to a comparably bold new future in diagnosis and classification. The purpose of this chapter is to address the validity of the categorical and dimensional models for the classification and diagnosis of psychopathology. Considered in particular will be research concerning substance use disorders, mood disorders, and personality disorders. Limitations and concerns with respect to a dimensional classification of psychopathology are also considered. The chapter concludes with a recommendation for a conversion to a more quantitative, dimensional classification of psychopathology.

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