Robot-Assisted Surgery Adopting Artificial Intelligence Procedures

Author(s):  
Abhay Patil

Abstract: Surgery is a methodology done in current medication to distinguish, keep away from and fix any approaching affliction which could genuinely influence the presence of any living being. Henceforth medical procedures structure a basic piece of people/creatures in guaranteeing life or improvement in the current condition to lead a cheerful and sound life. The utilization of Artificial Intelligence as a piece of choice decision supportive networks (AI) to work on the exhibition of explicit undertakings (by clinical robots) is standing out enough to be noticed as a piece of mechanical mediation in medical services. This paper endeavours to feature the advancement, restriction, openings and difficulties in utilizing AI-based innovations in robot-assisted medical procedures. We additionally propose an AI-based system for abnormality discovery and situating of the careful apparatus dependent on the information got from the processed pictures. Keywords: Artificial Intelligence, Medical Robot, Medical Image Processing, Surgery

Author(s):  
Umesh V

Surgery, is a procedure done in modern medicine to identify, avoid and cure any impending ailment which could seriously affect the existence of any living being. Hence surgeries form a critical part of humans/animal in ensuring life or improvement in the current condition to lead a happy and a healthy life. Use of Artificial Intelligence as a part of decision support systems (AI) in order to improve the performance of specific tasks (by medical robots) is getting due attention as a part of technological intervention in health care. This paper attempts to highlight the evolution, limitation, opportunities and challenges in using AI based technologies in robot assisted surgeries. We also propose an AI based framework for anomaly detection and positioning of the surgical tool based on the data obtained from the processed images.


2020 ◽  
pp. 1-14
Author(s):  
Zhen Huang ◽  
Qiang Li ◽  
Ju Lu ◽  
Junlin Feng ◽  
Jiajia Hu ◽  
...  

<b><i>Background:</i></b> Application and development of the artificial intelligence technology have generated a profound impact in the field of medical imaging. It helps medical personnel to make an early and more accurate diagnosis. Recently, the deep convolution neural network is emerging as a principal machine learning method in computer vision and has received significant attention in medical imaging. <b><i>Key Message:</i></b> In this paper, we will review recent advances in artificial intelligence, machine learning, and deep convolution neural network, focusing on their applications in medical image processing. To illustrate with a concrete example, we discuss in detail the architecture of a convolution neural network through visualization to help understand its internal working mechanism. <b><i>Summary:</i></b> This review discusses several open questions, current trends, and critical challenges faced by medical image processing and artificial intelligence technology.


1998 ◽  
Vol 15 (2) ◽  
pp. 187-192
Author(s):  
Tianmiao Wang ◽  
Mengdong Chen ◽  
Yi Zhang ◽  
M. Fadda

2012 ◽  
Vol 605-607 ◽  
pp. 1453-1459
Author(s):  
Ying Yu Cao ◽  
Bo Jin Qi ◽  
Shao Xian Wang ◽  
Wei Tao Cui

The navigation and positioning of medical robot was the precondition of taking the medical robot-assisted surgery. The navigation and positioning method of ultrasound-guided robot for liver cancer coagulation therapy was introduced. This paper presented the positioning method of different functions of robot based on the characteristics of active plus passive robot. The homologous point least squares matching method was employed for the robot 3D platform and the space vector calculation method was used for the robot RCM structure arm. The experimental results show that the robot positioning error is 1.32 ± 0.83mm, and the average positioning time is 5.5s, which can meet the requirements on the positioning accuracy, positioning time and operating flexibility of the liver cancer coagulation therapy.


The aim of the study is to compare, assess the optimum tools as well as the techniques and advanced features focused on prediction of diabetes diagnosis based on machine learning tactics and diabetic retinopathy using Artificial Intelligence. The literature on data science, Artificial Intelligence (AI) contains important knowledge and understanding of AI entities such as Data science, machine learning, deep learning, Medical image processing, feature extraction, classification techniques, etc. Diabetes diagnosis is a phenomenon that impacts individuals around the globe. Now, with diabetes impacting people from children to the elderly, the out-dated approaches to diabetes diagnosis should be replaced with new, time-saving technologies. There's several studies carried out by researchers to recognise and predict diabetes. Here plenty of classifiers in machine learning can be used, such as KNN, Random Tree, etc.They can save time and get more precise outcome when using these techniques to predict diabetes. Diabetic retinopathy (DR) is a typical disorder of diabetic disease that induces vision-impacting lesions in the retina. It also can turn to visual impairment if it is not addressed early. DR therapy only helps vision. Deep learning has in recent times being one of the most widely used approaches that has accomplished higher outcomes in so many fields, especially in the analysing and identification of medical image classification. In medical image processing, convolutional neural networks (CNN) using transfer learning are commonly used as a deep learning approach and they are incredibly beneficial. Key words: Diab


Author(s):  
Kavitha A

Nowadays Artificial intelligence makes our life easy and comfortable that is hard to imagine that to survive our life without AI technology. We all know that Artificial Intelligence (AI) is a precious gift to human being. Recently it is used in robotics, education, agriculture, computer vision, cyber security, face recognition, speech recognition, self driving cars, medical image processing, biometrics, bioinformatics, satellite control, disease detection, drugs development, network developments, manufacturing, business, healthcare and medicine. In the digital era AI provides the best results in all most all the domains. This article helps to understand the emerging aspects of AI in various fields.


2020 ◽  
Vol 8 (8) ◽  
pp. 494-506
Author(s):  
Hyunjo Kim ◽  
Gyoonhee Han ◽  
Jae-Hoon Song

The aim of the present study is to discuss the various aspects of modern technology used to fight against COVID-19 outbreak crisis at different scales, including medical image processing, disease tracking, prediction outcomes, computational biology and medicines. A progressive search of the database related to modern technology towards COVID-19 pandemic is made. Further, a brief review is done on the extracted information by assessing the various aspects of modern technologies for tackling COVID-19 pandemic. We provide a window of thoughts on review of the technology advances used to decrease and smother the substantial impact of the outburst. However, there are still constrained applications and contributions of technology in this fight although different studies relating to modern technology towards COVID-19 pandemic have come up yet. The modern technology on-going progress has contributed in improving people’s lives. Hence, there is a solid conviction that validated research plans including artificial intelligence will be of significant advantage in helping people to fight this infection.    Key words: COVID-19 pandemic, artificial intelligence (AI), molecular modeling, drug repurposing, vaccines, health infrastructure systems (HIS)


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