Heart Failure Artificial Intelligence-Based Computer Aided Diagnosis Telecare System

Spitzoid lesions may be largely categorized into Spitz Nevus, Atypical Spitz Tumors, and Spitz Melanomas. Classifying a lesion precisely as Atypical Spitz Tumors or AST is challenging and often requires the integration of clinical, histological, and immunohistochemical features to differentiate AST from regular Spitz Nevus and malignant Spitz Melanomas. Specifically, this paper aims to test several artificial intelligence techniques so as to build a computer-aided diagnosis system. A proposed three-phase approach is being implemented. In Phase 1, collected data are preprocessed with an effective SMOTE-based method being implemented to treat the imbalance data problem. Then, a feature selection mechanism using genetic algorithm (GA) is applied in Phase 2. Finally, in Phase 3, a 10-fold cross-validation method is used to compare the performance of seven machine-learning algorithms for classification. Results obtained with SMOTE-Multilayer Perceptron with GA-based 14 features show the highest classification accuracy, specificity (0.98), and a sensitivity of 0.99.

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Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine

Radiologia Brasileira ◽

10.1590/0100-3984.2019.0049 ◽

2019 ◽

Vol 52 (6) ◽

pp. 387-396 ◽

Cited By ~ 9

Author(s):

Marcel Koenigkam Santos ◽

José Raniery Ferreira Júnior ◽

Danilo Tadao Wada ◽

Ariane Priscilla Magalhães Tenório ◽

Marcello Henrique Nogueira Barbosa ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Diagnostic Imaging ◽

Precision Medicine ◽

Quantitative Information ◽

Computer Aided Diagnosis ◽

Rapid Expansion ◽

Computer Aided ◽

The Impact ◽

Aided Diagnosis

Abstract The discipline of radiology and diagnostic imaging has evolved greatly in recent years. We have observed an exponential increase in the number of exams performed, subspecialization of medical fields, and increases in accuracy of the various imaging methods, making it a challenge for the radiologist to “know everything about all exams and regions”. In addition, imaging exams are no longer only qualitative and diagnostic, providing now quantitative information on disease severity, as well as identifying biomarkers of prognosis and treatment response. In view of this, computer-aided diagnosis systems have been developed with the objective of complementing diagnostic imaging and helping the therapeutic decision-making process. With the advent of artificial intelligence, “big data”, and machine learning, we are moving toward the rapid expansion of the use of these tools in daily life of physicians, making each patient unique, as well as leading radiology toward the concept of multidisciplinary approach and precision medicine. In this article, we will present the main aspects of the computational tools currently available for analysis of images and the principles of such analysis, together with the main terms and concepts involved, as well as examining the impact that the development of artificial intelligence has had on radiology and diagnostic imaging.

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COMPUTER-AIDED DIAGNOSIS (CAD) BASED ON CONVOLUTIONAL NEURAL NETWORK (CNN) SYSTEM USING ARTIFICIAL INTELLIGENCE (AI) FOR COLORECTAL POLYP CLASSIFICATION

10.1055/s-0039-1681181 ◽

2019 ◽

Author(s):

Y Komeda ◽

H Handa ◽

R Matsui ◽

H Kashida ◽

T Watanabe ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Convolutional Neural Network ◽

Colorectal Polyp ◽

Computer Aided Diagnosis ◽

Computer Aided ◽

Aided Diagnosis

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Computer-Aided Diagnosis and Clinical Trials of Cardiovascular Diseases Based on Artificial Intelligence Technologies for Risk-Early Warning Model

Journal of Medical Systems ◽

10.1007/s10916-019-1346-x ◽

2019 ◽

Vol 43 (7) ◽

Cited By ~ 2

Author(s):

Bin Li ◽

Shuai Ding ◽

Guolei Song ◽

Jiajia Li ◽

Qian Zhang

Keyword(s):

Artificial Intelligence ◽

Clinical Trials ◽

Cardiovascular Diseases ◽

Early Warning ◽

Computer Aided Diagnosis ◽

Early Warning Model ◽

Computer Aided ◽

Warning Model ◽

Aided Diagnosis

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Computer-Aided Diagnosis and Artificial Intelligence in Clinical Imaging

Seminars in Nuclear Medicine ◽

10.1053/j.semnuclmed.2011.06.004 ◽

2011 ◽

Vol 41 (6) ◽

pp. 449-462 ◽

Cited By ~ 75

Author(s):

Junji Shiraishi ◽

Qiang Li ◽

Daniel Appelbaum ◽

Kunio Doi

Keyword(s):

Artificial Intelligence ◽

Computer Aided Diagnosis ◽

Clinical Imaging ◽

Computer Aided ◽

Aided Diagnosis

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SPHINX — A System for Computer-Aided Diagnosis

Methods of Information in Medicine ◽

10.1055/s-0038-1635404 ◽

1982 ◽

Vol 21 (03) ◽

pp. 143-148 ◽

Cited By ~ 10

Author(s):

M. Fieschi ◽

M. Joubert ◽

D. Fieschi ◽

M. Roux

Keyword(s):

Artificial Intelligence ◽

Pattern Matching ◽

Epigastric Pain ◽

Computer Aided Diagnosis ◽

Computer Aided ◽

Aided Diagnosis ◽

Made In ◽

Fuzzy Heuristics

This paper presents a system for computer-aided diagnosis, the SPHINX system, based on methods of inference and pattern matching used in artificial intelligence and on various heuristic features: fuzzy heuristics in relation to the suggestion power of the signs and heuristics based on the costs of complementary investigations. The first application was made in the diagnosis of epigastric pain. Its results are presented and discussed.

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