scholarly journals Requirement of artificial intelligence technology awareness for thoracic surgeons

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Anshuman Darbari ◽  
Krishan Kumar ◽  
Shubhankar Darbari ◽  
Prashant L. Patil
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Robots ◽  
Healthcare Delivery ◽  
Data Interpretation ◽  
Review Article ◽  
Physiological Data ◽  
Main Body ◽  
Thoracic Imaging ◽  
Thoracic Surgical Procedures ◽  
Artificial Intelligence Technology

Abstract Background We have recently witnessed incredible interest in computer-based, internet web-dependent mechanisms and artificial intelligence (AI)-dependent technique emergence in our day-to-day lives. In the recent era of COVID-19 pandemic, this nonhuman, machine-based technology has gained a lot of momentum. Main body of the abstract The supercomputers and robotics with AI technology have shown the potential to equal or even surpass human experts’ accuracy in some tasks in the future. Artificial intelligence (AI) is prompting massive data interweaving with elements from many digital sources such as medical imaging sorting, electronic health records, and transforming healthcare delivery. But in thoracic surgical and our counterpart pulmonary medical field, AI’s main applications are still for interpretation of thoracic imaging, lung histopathological slide evaluation, physiological data interpretation, and biosignal testing only. The query arises whether AI-enabled technology-based or autonomous robots could ever do or provide better thoracic surgical procedures than current surgeons but it seems like an impossibility now. Short conclusion This review article aims to provide information pertinent to the use of AI to thoracic surgical specialists. In this review article, we described AI and related terminologies, current utilisation, challenges, potential, and current need for awareness of this technology.

Applications of artificial intelligence and machine learning in respiratory medicine

Thorax ◽  
2020 ◽  
Vol 75 (8) ◽  
pp. 695-701 ◽  
Author(s):  
Sherif Gonem ◽  
Wim Janssens ◽  
Nilakash Das ◽  
Marko Topalovic
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Input Data ◽  
Pulmonary Function Tests ◽  
Machine Learning Techniques ◽  
Respiratory Medicine ◽  
Physiological Data ◽  
Lung Pathology ◽  
Thoracic Imaging ◽  
The Past

The past 5 years have seen an explosion of interest in the use of artificial intelligence (AI) and machine learning techniques in medicine. This has been driven by the development of deep neural networks (DNNs)—complex networks residing in silico but loosely modelled on the human brain—that can process complex input data such as a chest radiograph image and output a classification such as ‘normal’ or ‘abnormal’. DNNs are ‘trained’ using large banks of images or other input data that have been assigned the correct labels. DNNs have shown the potential to equal or even surpass the accuracy of human experts in pattern recognition tasks such as interpreting medical images or biosignals. Within respiratory medicine, the main applications of AI and machine learning thus far have been the interpretation of thoracic imaging, lung pathology slides and physiological data such as pulmonary function tests. This article surveys progress in this area over the past 5 years, as well as highlighting the current limitations of AI and machine learning and the potential for future developments.

Current uses, emerging applications, and clinical integration of artificial intelligence in neuroradiology

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Brian Fiani ◽  
Kory B. Dylan Pasko ◽  
Kasra Sarhadi ◽  
Claudia Covarrubias
Keyword(s):  
Artificial Intelligence ◽  
Healthcare Delivery ◽  
Data Interpretation ◽  
Medical Data ◽  
Human Cognition ◽  
Scientific Validity ◽  
Clinical Integration ◽  
Management Diagnostics ◽  
Accurate Imaging

Abstract Artificial intelligence (AI) is a branch of computer science with a variety of subfields and techniques, exploited to serve as a deductive tool that performs tasks originally requiring human cognition. AI tools and its subdomains are being incorporated into healthcare delivery for the improvement of medical data interpretation encompassing clinical management, diagnostics, and prognostic outcomes. In the field of neuroradiology, AI manifested through deep machine learning and connected neural networks (CNNs) has demonstrated incredible accuracy in identifying pathology and aiding in diagnosis and prognostication in several areas of neurology and neurosurgery. In this literature review, we survey the available clinical data highlighting the utilization of AI in the field of neuroradiology across multiple neurological and neurosurgical subspecialties. In addition, we discuss the emerging role of AI in neuroradiology, its strengths and limitations, as well as future needs in strengthening its role in clinical practice. Our review evaluated data across several subspecialties of neurology and neurosurgery including vascular neurology, spinal pathology, traumatic brain injury (TBI), neuro-oncology, multiple sclerosis, Alzheimer’s disease, and epilepsy. AI has established a strong presence within the realm of neuroradiology as a successful and largely supportive technology aiding in the interpretation, diagnosis, and even prognostication of various pathologies. More research is warranted to establish its full scientific validity and determine its maximum potential to aid in optimizing and providing the most accurate imaging interpretation.

scholarly journals A review of the use of artificial intelligence in orthodontics

2021 ◽  
Vol 38 (SI-2) ◽  
pp. 157-162
Author(s):  
Serdar AKDENİZ ◽  
Muhammet Emir TOSUN
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Current Literature ◽  
Treatment Process ◽  
Review Article ◽  
Clinical Use ◽  
Efficient Treatment ◽  
Artificial Intelligence Technology ◽  
Near Future

The clinical use of artificial intelligence technology in orthodontics has increased significantly in recent years. Artificial intelligence can be utilized in almost every part of orthodontic workflow. It is an important decision making aid as well as a tool for building more efficient treatment mechanics. The use of artificial intelligence reduces costs, speeds up the diagnosis and treatment process and reduces or even eliminates the need for manpower. This review article evaluates the current literature on artificial intelligence and machine learning in the field of orthodontics. The areas that the artificial intelligence is still lacking were also discussed in detail. Despite its shortcomings, artificial intelligence is considered to have an integral part of orthodontic practice in the near future.

scholarly journals Diabetic retinopathy classification for supervised machine learning algorithms

2022 ◽  
Vol 8 (1) ◽  
Author(s):  
Luis Filipe Nakayama ◽  
Lucas Zago Ribeiro ◽  
Mariana Batista Gonçalves ◽  
Daniel A. Ferraz ◽  
Helen Nazareth Veloso dos Santos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Fundamental Problem ◽  
Ground Truth ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Main Body ◽  
Workflow Optimization ◽  
Artificial Intelligence Technology ◽  
Automated Technology

Abstract Background Artificial intelligence and automated technology were first reported more than 70 years ago and nowadays provide unprecedented diagnostic accuracy, screening capacity, risk stratification, and workflow optimization. Diabetic retinopathy is an important cause of preventable blindness worldwide, and artificial intelligence technology provides precocious diagnosis, monitoring, and guide treatment. High-quality exams are fundamental in supervised artificial intelligence algorithms, but the lack of ground truth standards in retinal exams datasets is a problem. Main body In this article, ETDRS, NHS, ICDR, SDGS diabetic retinopathy grading, and manual annotation are described and compared in publicly available datasets. The various DR labeling systems generate a fundamental problem for AI datasets. Possible solutions are standardization of DR classification and direct retinal-finding identifications. Conclusion Reliable labeling methods also need to be considered in datasets with more trustworthy labeling.

scholarly journals A Study of Automatic Judgment of Food Color and Cooking Conditions with Artificial Intelligence Technology

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1128
Author(s):  
Chern-Sheng Lin ◽  
Yu-Ching Pan ◽  
Yu-Xin Kuo ◽  
Ching-Kun Chen ◽  
Chuen-Lin Tien
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Color Space ◽  
Color Difference ◽  
Color Space Conversion ◽  
Before And After ◽  
Artificial Intelligence Technology ◽  
Artificial Intelligence Algorithm ◽  
Time Image ◽  
Cooking Conditions

In this study, the machine vision and artificial intelligence algorithms were used to rapidly check the degree of cooking of foods and avoid the over-cooking of foods. Using a smart induction cooker for heating, the image processing program automatically recognizes the color of the food before and after cooking. The new cooking parameters were used to identify the cooking conditions of the food when it is undercooked, cooked, and overcooked. In the research, the camera was used in combination with the software for development, and the real-time image processing technology was used to obtain the information of the color of the food, and through calculation parameters, the cooking status of the food was monitored. In the second year, using the color space conversion, a novel algorithm, and artificial intelligence, the foreground segmentation was used to separate the vegetables from the background, and the cooking ripeness, cooking unevenness, oil glossiness, and sauce absorption were calculated. The image color difference and the distribution were used to judge the cooking conditions of the food, so that the cooking system can identify whether or not to adopt partial tumbling, or to end a cooking operation. A novel artificial intelligence algorithm is used in the relative field, and the error rate can be reduced to 3%. This work will significantly help researchers working in the advanced cooking devices.

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