Acquaintance to Artificial Neural Networks and use of artificial intelligence as a diagnostic tool for tuberculosis: A review

Dande, Payal; Samant, Purva

doi:10.1016/j.tube.2017.09.006

Cited by 88 publications

(45 citation statements)

References 33 publications

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“…LTB cannot spread to others; it is usually diagnosed when a person tests positive for TB skin test or TB blood test [48]. Table 1 gives a brief description of the current TB tests, their inference and shortcomings [49].…”

Section: The Conventional Methods Of Diagnosing Tbmentioning

confidence: 99%

Artificial Intelligence in Diagnosing Tuberculosis: A Review

Meraj¹,

Yaakob²,

Azman³

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Tuberculosis (TB) is among top ten causes of deaths worldwide. It is the single most cause of deaths by an infectious disease and is ranked 2nd only after the HIV/AIDS. In third world countries, the diagnosis of TB is done through conventional methods. To diagnostic results are obtain from conventional methods such as blood, culture, sputum and biopsies. They are tedious as well as take long time like 1-2 weeks or maybe evenmore. Therefore, to lower the detection time and raise the accuracy of diagnosis several researches have been carried out. In the past fifty years, due to the advanced and sophisticated technologies, in medical as well as computer science fields, have paved a way to utilize the essence of both the areas. In Artificial Intelligence (AI) various Machine Learning (ML) algorithms have furthered the interests in Computer-aided Detection (CADe) and Diagnosis (CADx) methods. These methodologies assist in medical field for diagnosing the diseases through clinical signs and symptoms as well as radiological images of the patient. They have been implemented for the diagnosis of TB. Advances in AI algorithms, has unveiled great promises in identifying the presence and absence of TB. As of late, many attempts have been made to formulate the strategies to increase the classification accuracy of TB diagnosis using the AI and machine learning approach. This review paper, aims to describes the diverse AI approaches employed in the diagnosis of TB.

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Section: The Conventional Methods Of Diagnosing Tbmentioning

confidence: 99%

Artificial Intelligence in Diagnosing Tuberculosis: A Review

Meraj¹,

Yaakob²,

Azman³

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…TB is normally caused by airborne bacterial infection from Mycobacterium tuberculosis (Mtb) [3]. This infection normally targets the lungs (pulmonary TB), but it could also attack the kidney, brain, and…”

Section: Introductionmentioning

confidence: 99%

Surface Enhanced CdSe/ZnS QD/SiNP Electrochemical Immunosensor for the Detection of Mycobacterium Tuberculosis by Combination of CFP10-ESAT6 for Better Diagnostic Specificity

et al. 2019

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In this study, an electrochemical immunosensor was introduced for the detection of tuberculosis (TB) via utilization of a modified electrode containing a quantum dot (CdSe/ZnS QD) and functionalized silica nanoparticles (SiNPs) on screen-printed carbon electrode (SPCE) CdSe/ZnS QD/SiNPs/SPCE, by employing indirect enzyme-linked immunosorbent assay (ELISA). Here, the fabricated electrode was linked to the biocatalytic action of enzyme catalase through antigen–antibody binding for the detection of the antigen (CFP10–ESAT6) by means of producing a differential pulse voltammetry (DPV) current. The characterization and cyclic voltammetry (CV) of the modified electrode showed good electrochemical behavior and enhanced high electron transfer between the electrode and analyte. Moreover, the active surface area was 4.14-fold higher than the bare SPCE. The developed method showed high selectivity towards CFP10–ESAT6 compared with the other TB proteins. The detection of CFP10–ESAT6 also showed a linear response towards different concentrations of CFP10–ESAT6 with R2 = 0.9937, yielding a limit of detection (LOD) of as low as 1.5 × 10−10 g/mL for a linear range of 40 to 100 ng/mL of CFP10–ESAT6 concentration. The proposed method showed good reproducibility of target analyte with a relative standard deviation of 1.45%.

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“…Artificial Intelligence is a hot topic of research, with a vast area of applications [21][22][23][24][25]. Genetic Algorithms (GA) is a metaheuristic which belongs to the larger class Evolutionary Algorithms (EA).…”

Section: Introductionmentioning

confidence: 99%

A Closed Loop Inverse Kinematics Solver Intended for Offline Calculation Optimized with GA

Bjørlykhaug

2018

Robotics

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This paper presents a simple approach to building a robotic control system. Instead of a conventional control system which solves the inverse kinematics in real-time as the robot moves, an alternative approach where the inverse kinematics is calculated ahead of time is presented. This approach reduces the complexity and code necessary for the control system. Robot control systems are usually implemented in low level programming language. This new approach enables the use of high level programming for the complex inverse kinematics problem. For our approach, we implement a program to solve the inverse kinematics, called the Inverse Kinematics Solver (IKS), in Java, with a simple graphical user interface (GUI) to load a file with desired end effector poses and edit the configuration of the robot using the Denavit-Hartenberg (DH) convention. The program uses the closed-loop inverse kinematics (CLIK) algorithm to solve the inverse kinematics problem. As an example, the IKS was set up to solve the kinematics for a custom built serial link robot. The kinematics for the custom robot is presented, and an example of input and output files is also presented. Additionally, the gain of the loop in the IKS is optimized using a GA, resulting in almost a 50% decrease in computational time.

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Acquaintance to Artificial Neural Networks and use of artificial intelligence as a diagnostic tool for tuberculosis: A review

Cited by 88 publications

References 33 publications

Artificial Intelligence in Diagnosing Tuberculosis: A Review

Artificial Intelligence in Diagnosing Tuberculosis: A Review

Surface Enhanced CdSe/ZnS QD/SiNP Electrochemical Immunosensor for the Detection of Mycobacterium Tuberculosis by Combination of CFP10-ESAT6 for Better Diagnostic Specificity

A Closed Loop Inverse Kinematics Solver Intended for Offline Calculation Optimized with GA

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