Reconfiguring traditional EKG interpretation with artificial intelligence – a reliable, time-saving alternative?

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S Mehta ◽  
M Gibson ◽  
J Avila ◽  
C Villagran ◽  
F Fernandez ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Reading Program ◽  
Key Factors ◽  
Patient Centered ◽  
Time Saving ◽  
Grand Total ◽  
Innovative Methodology ◽  
Work Shifts ◽  
The One ◽  
Telemedical System

Abstract Background Time and accuracy are key factors that may make or break an efficient triage and management in most medical premises, particularly so when expedited diagnosis saves lives - a not so uncommon scenario in the field of cardiology. By studying the different variables involved in cardiologist-EKG interactions that lead to the identification and management of different cardiovascular entities, we delved into the applications of Artificial Intelligence (AI) algorithms in order to improve upon the classic, but dated, EKG methodology. With this study, we pit our algorithm against cardiologists to perform a thorough analysis of the time invested to diagnose an EKG as Normal, as well as an assessment of the accuracy of said label. Purpose To present a faster and reliable AI-guided EKG interpretation methodology that outperforms cardiologists' capabilities in identifying Normal EKG records. Methods The International Telemedical System (ITMS) developed and tested an EKG assessing AI algorithm and incorporated it into the workflow of their Telemedicine Integrated Platform, a digital EKG reading program where cardiologists continuously report their findings remotely in real time. During the month of April 2019; 35 ITMS cardiologists reported a grand total of 61,441 EKG records, later subjecting them to the AI algorithm, implemented through the “One Click Report” process. Through this simple 2-step approach, the algorithm provides a suggestion of “Normal” or “Abnormal” to the cardiologist based on the patterns of the fiducial points included in said EKG reports. A comparison of the time of normal EKG diagnosis is made and the correlation between AI and cardiologists is assessed. Results On average, our AI algorithm discerned a normal EKG within 30.63s (95% CI 26.51s to 34.75s), in solid contrast with cardiologists' interpretations alone, which amounted to 83.54s (95% CI from 69.43s to 97.65s). This accounts for an overall saving of 52.91s (95% CI 42.45s to 63.83s) by implementing this innovative methodology in a cardiologist practice. In addition, this method correctly reported 23,213 Normal EKG records out of the total 25,013 AI output, reaching a 92.8% correlation between man and machine. The total average time saved in normal EKG readings with AI (23,213) would accrue an approximate of 20,470 minutes (ie, 42 8-hours work shifts worth of time dedicated to diagnosing a normal EKG). Conclusions The implementation of automated AI-driven technologies into daily EKG interpretation tasks poses an attractive time-saving alternative for faster and accurate results in a modern cardiology practice. By further expanding on the concept of an intelligent EKG characterization device, a more efficient and patient-centered clinical exercise will ensue. Funding Acknowledgement Type of funding source: None

Moving in sync – concordance betweena artificial intelligence and cardiologist on detecting normal electrocardiograms

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S Mehta ◽  
J Avila ◽  
C Villagran ◽  
F Fernandez ◽  
S Niklitschek ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Reading Program ◽  
Medical Community ◽  
Digital Platforms ◽  
Grand Total ◽  
Life Saving ◽  
The One ◽  
Telemedical System ◽  
Modern Technologies

Abstract Background Merging modern technologies with classic diagnostic tests often results in a sense of insecurity within the medical community, particularly so with potentially life-saving studies such as the electrocardiogram (EKG). In order to provide a greater sense of trust between Artificial Intelligence (AI) and cardiologists, we provide an AI-driven algorithm capable of accurately and reliably characterize an EKG as normal within a highly complex, cardiologist-reviewed EKG database and report the degree of concordance between this machine vs physician scenario. Purpose To provide a dependable and accurate AI algorithm that conducts EKG interpretation in a cardiologist-tier manner. Methods The International Telemedical System (ITMS) developed and tested an EKG assessing AI algorithm and incorporated it into the workflow of their Telemedicine Integrated Platform, a digital EKG reading program where cardiologists continuously report their findings remotely in real-time. During the month of April 2,019; 35 ITMS cardiologists reported a grand total of 61,441 EKG records, later submitting them to the AI algorithm implemented through the “One Click Report” process. Through this simple 2-step approach, the algorithm provides a suggestion of “Normal” or “Abnormal” to the cardiologist based on the patterns of the fiducial points included in said EKG reports. Confirmation of these suggestions by the cardiologists ensued. Results Overall, cardiologists confirmed 23,213 out of 25,013 AI outputs for “Normal” EKGs, demonstrating a concordance of 92.8% for Normal diagnosis. Conclusion Through this methodology, we provide an AI technology that can be reliably applied and trusted in EKG digital platforms to identify and suitably label a normal EKG. Further testing will accrue into a multi label algorithm compatible with abnormal cardiovascular entities, potentially precluding the role of the cardiologist for triaging, particularly in the prehospital setting. We anticipate that this approach will become a promising methodology in modern cardiology practice. Funding Acknowledgement Type of funding source: None

scholarly journals Artificial intelligence methodology: multi-label classification of abnormal EKG records

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S Mehta ◽  
J Avila ◽  
C Villagran ◽  
F Fernandez ◽  
S Niklitschek ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Performance Indicators ◽  
Funding Source ◽  
Training Set ◽  
Average Accuracy ◽  
Cardiac Disorders ◽  
The Mean ◽  
The One ◽  
Telemedical System

Abstract Background Our previous experience with Artificial Intelligence (AI)-conducted EKG characterization displayed outstanding results in fast and reliable identification of Normal EKGs within the International Telemedical System (ITMS)'s massive record repository. By expanding the array of recognizable cardiovascular entities, we upgraded our methodology to accurately discriminate an anomaly amongst a highly complex database of EKG records. Purpose To present a feasible AI-guided filter that can accurately discriminate and classify Normal and Abnormal EKG records within a multilabeled cardiologist-annotated EKG database. Methods ITMS developed and tested the “One Click”' process, a “Normal/Abnormal” EKG assessing AI algorithm, by incorporating it into their digital EKG reading platform where cardiologists continuously report their findings remotely in real time. To ameliorate the diagnostic range of the algorithm, a separate dataset of 121,641 12-lead EKG records was consolidated from the ITMS database from October 2011 to January 2019. Only de-identified data was used. Preprocessing: The first 2s of each short lead and 9s of the long lead were considered. Limb leads I, II and III; and precordial leads V1, V2, V3, and V5 were used. The mean was removed from each lead. AI models/Classification: Two models were created and tested independently based on the method of EKG acquisition (69,852 records transtelephonic [TTP]; 52,259 mobile transmission [MOB]). Each record is categorized into six disjoint classes based on the most common types of cardiac disorders (Low/null co-occurrence pathologies in these datasets were grouped into analogous groups). Training/Testing: Distribution of both sets per transmission type was performed through a greedy algorithm, which identified multiple diagnoses per EKG record and labeled it separately to the corresponding group, ensuring sufficient samples per class. Detailed class distribution is shown below. An inception convolutional neural network was implemented; “Normal” or “Abnormal” labels were assigned to each EKG record independently and were compared to cardiologists' reports; performance indicators were calculated for each model and group. Results MOB model accrued an average accuracy of 86.7%; sensitivity of 90.5%; and specificity of 83.9%. TTP model yielded an average accuracy of 77.2%; sensitivity of 91.1%; and specificity of 69.4% (Lower values were attributed to the “Ventricular Complexes” group, which challenged the algorithm by having a smaller ratio of abnormal exams). Detailed results of each training set are shown below. Conclusion Providing an effective and reliable multilabel-capable EKG triaging tool remains a challenging but attainable goal. Continuous systematic enhancement of our AI-driven methodology has led us to satisfactory, yet imperfect results which compel us to further study and improve our efforts to provide a trustworthy cardiologist-friendly triage device. Funding Acknowledgement Type of funding source: None

Co@N-CNT/MXene in situ grown on carbon nanotube film for multifunctional sensors and flexible supercapacitor

Nanoscale ◽  
2021 ◽  
Author(s):  
Qiufan Wang ◽  
Jiaheng Liu ◽  
Guofu Tian ◽  
Daohong Zhang
Keyword(s):  
Artificial Intelligence ◽  
Carbon Nanotube ◽  
Energy Storage ◽  
Storage Systems ◽  
Rapid Development ◽  
Human Machine Interface ◽  
Key Factors ◽  
Flexible Supercapacitor ◽  
Machine Interface

The rapid development of human-machine interface and artificial intelligence is dependent on flexible and wearable soft devices such as sensors and energy storage systems. One of the key factors for...

Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs

2021 ◽  
pp. 20200172
Author(s):  
Münevver Coruh Kılıc ◽  
Ibrahim Sevki Bayrakdar ◽  
Özer Çelik ◽  
Elif Bilgir ◽  
Kaan Orhan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Confusion Matrix ◽  
Forensic Identification ◽  
Deciduous Teeth ◽  
Time Saving ◽  
Panoramic Radiographs ◽  
Intelligence System ◽  
Promising Tool ◽  
Panoramic Images

Objective: This study evaluated the use of a deep-learning approach for automated detection and numbering of deciduous teeth in children as depicted on panoramic radiographs. Methods and materials: An artificial intelligence (AI) algorithm (CranioCatch, Eskisehir-Turkey) using Faster R-CNN Inception v2 (COCO) models were developed to automatically detect and number deciduous teeth as seen on pediatric panoramic radiographs. The algorithm was trained and tested on a total of 421 panoramic images. System performance was assessed using a confusion matrix. Results: The AI system was successful in detecting and numbering the deciduous teeth of children as depicted on panoramic radiographs. The sensitivity and precision rates were high. The estimated sensitivity, precision, and F1 score were 0.9804, 0.9571, and 0.9686, respectively. Conclusion: Deep-learning-based AI models are a promising tool for the automated charting of panoramic dental radiographs from children. In addition to serving as a time-saving measure and an aid to clinicians, AI plays a valuable role in forensic identification.

scholarly journals A Survey of Techniques for Constructing Chinese Knowledge Graphs and Their Applications

2018 ◽  
Vol 10 (9) ◽  
pp. 3245 ◽  
Author(s):  
Tianxing Wu ◽  
Guilin Qi ◽  
Cheng Li ◽  
Meng Wang
Keyword(s):  
Artificial Intelligence ◽  
Question Answering ◽  
Knowledge Representation And Reasoning ◽  
Knowledge Graph ◽  
Development History ◽  
One Belt One Road ◽  
History Of ◽  
Knowledge Graphs ◽  
The One ◽  
The Impact

With the continuous development of intelligent technologies, knowledge graph, the backbone of artificial intelligence, has attracted much attention from both academic and industrial communities due to its powerful capability of knowledge representation and reasoning. In recent years, knowledge graph has been widely applied in different kinds of applications, such as semantic search, question answering, knowledge management and so on. Techniques for building Chinese knowledge graphs are also developing rapidly and different Chinese knowledge graphs have been constructed to support various applications. Under the background of the “One Belt One Road (OBOR)” initiative, cooperating with the countries along OBOR on studying knowledge graph techniques and applications will greatly promote the development of artificial intelligence. At the same time, the accumulated experience of China in developing knowledge graphs is also a good reference to develop non-English knowledge graphs. In this paper, we aim to introduce the techniques of constructing Chinese knowledge graphs and their applications, as well as analyse the impact of knowledge graph on OBOR. We first describe the background of OBOR, and then introduce the concept and development history of knowledge graph and typical Chinese knowledge graphs. Afterwards, we present the details of techniques for constructing Chinese knowledge graphs, and demonstrate several applications of Chinese knowledge graphs. Finally, we list some examples to explain the potential impacts of knowledge graph on OBOR.

scholarly journals An Overview on Patient-Centered Clinical Services

2021 ◽  
Vol 4 (2) ◽  
pp. 157-170
Author(s):  
Gaurav Joshi ◽  
Atul Kabra ◽  
Nishant Goutam ◽  
Alka Sharma
Keyword(s):  
Patient Outcomes ◽  
Clinical Pharmacist ◽  
Healthcare Systems ◽  
Clinical Services ◽  
Drug Related Problems ◽  
Patient Centered ◽  
Health Quality ◽  
Health Quality Of Life ◽  
The One

Drug-related problems (DRPs) had often been a concern in the system that needed to be detected, avoided, and addressed as soon as possible. The need for a clinical pharmacist becomes even more important. He is the one who can not only share the load but also be an important part of the system by providing required advice. They fill out the patient's pharmacotherapy reporting form and notify the medical team's head off any drug-related issues. General practitioners register severe adverse drug reactions (ADRs) yearly. As a result of all of this, a clinical pharmacist working in and around the healthcare system is expected to advance the pharmacy industry. Its therapy and drugs can improve one's health quality of life by curing, preventing, or diagnosing a disease, sign, or symptom. The sideshows, on the other hand, do much harm. Because of the services they offer, clinical pharmacy has grown in popularity. To determine the overall effect and benefits of the emergency department (ED) clinical pharmacist, a systematic review of clinical practice and patient outcomes will be needed. A clinical pharmacist's anatomy, toxicology, pharmacology, and medicinal chemistry expertise significantly improves a patient's therapy enforcement. It is now important to examine the failure points of healthcare systems as well as the individuals involved.

scholarly journals El sesgo de la máquina en la toma de decisiones en el proceso penal.

2020 ◽  
Vol 6 (2) ◽  
pp. 54-71
Author(s):  
Raquel Borges Blázquez
Keyword(s):  
Artificial Intelligence ◽  
The Other ◽  
Human Being ◽  
Criminal Process ◽  
Ethical Problems ◽  
Intelligent Machines ◽  
Other Hand ◽  
Toma De Decisiones ◽  
Legal Thinking ◽  
The One

Artificial intelligence has countless advantages in our lives. On the one hand, computer’s capacity to store and connect data is far superior to human capacity. On the other hand, its “intelligence” also involves deep ethical problems that the law must respond to. I say “intelligence” because nowadays machines are not intelligent. Machines only use the data that a human being has previously offered as true. The truth is relative and the data will have the same biases and prejudices as the human who programs the machine. In other words, machines will be racist, sexist and classist if their programmers are. Furthermore, we are facing a new problem: the difficulty to understand the algorithm of those who apply the law.This situation forces us to rethink the criminal process, including artificial intelligence and spinning very thinly indicating how, when, why and under what assumptions we can make use of artificial intelligence and, above all, who is going to program it. At the end of the day, as Silvia Barona indicates, perhaps the question should be: who is going to control global legal thinking?

scholarly journals The Legal Aspects of Artificial Intelligence based on the EU Experience

Law and World ◽  
2021 ◽  
Vol 7 (5) ◽  
pp. 8-13
Keyword(s):  
Artificial Intelligence ◽  
Legal Framework ◽  
Legal Aspects ◽  
Public And Private ◽  
Legal Questions ◽  
Technological Advances ◽  
Clear Vision ◽  
Routine Tasks ◽  
The One ◽  
The Eu

In the digital era, technological advances have brought innovative opportunities. Artificial intelligence is a real instrument to provide automatic routine tasks in different fields (healthcare, education, the justice system, foreign and security policies, etc.). AI is evolving very fast. More precisely, robots as re-programmable multi-purpose devices designed for the handling of materials and tools for the processing of parts or specialized devices utilizing varying programmed movements to complete a variety of tasks.1 Regardless of opportunities, artificial intelligence may pose some risks and challenges for us. Because of the nature of AI ethical and legal questions can be pondered especially in terms of protecting human rights. The power of artificial intelligence means using it more effectively in the process of analyzing big data than a human being. On the one hand, it causes loss of traditional jobs and, on the other hand, it promotes the creation of digital equivalents of workers with automatic routine task capabilities. “Artificial intelligence must serve people, and therefore artificial intelligence must always comply with people’s rights,” said Ursula von der Leyen, President of the European Commission.2 The EU has a clear vision of the development of the legal framework for AI. In the light of the above, the article aims to explore the legal aspects of artificial intelligence based on the European experience. Furthermore, it is essential in the context of Georgia’s European integration. Analyzing legal approaches of the EU will promote an approximation of the Georgian legislation to the EU standards in this field. Also, it will facilitate to define AI’s role in the effective digital transformation of public and private sectors in Georgia.

scholarly journals Artificial intelligence and copyright

2021 ◽  
pp. 15-22
Author(s):  
Daryna Prylypko
Keyword(s):  
Artificial Intelligence ◽  
Computer Program ◽  
Key Words ◽  
Automatic Generation ◽  
The Other ◽  
International Level ◽  
Human Agent ◽  
Foreign Countries ◽  
The One ◽  
Justice Practice

Key words: copyright, work, artificial intelligence, computer program In the article, the problemsof legislation of Ukraine regarding the issues of copyright on works created due to artificialintelligence were analyzed. Particularly, who is the owner of copyright ofworks created due to artificial intelligence. On the one hand, it could be a developer ofa computer program, from the other hand, it could be a client or an employer. Because,it could happen that there is a situation when robots created something newand original, e.g., how it happened with the project “New Rembrandt”. In this case,computers created a unique portrait of Rembrandt. And here is a question, where isin this portrait original and intellectual works of developers of these computers andprograms. In the contrast, this portrait could be created without people who developedspecial machines, programs, and computers. The article’s author proposes to addinto Ukrainian legislation with following norm: the owner of the copyright createddue to artificial intelligence should be a natural person who uses artificial intelligencefor these purposes within the official relationship or on the basis of a contract. In caseof automatic generation of such work by artificial intelligence, the owner of copyrightshould be the developer.Also, another question arises, particularly, who will be responsible for the damagecaused by the artificial intelligence. As an example, of the solution for this issue Resolution2015/2103 (INL) was given, where is mentioned that human agent could be responsiblefor the caused damage. Because, it is not always a developer is responsiblefor the damage.Also, the legislation and justice practice of foreign countries was explored. Theways of overcoming mentioned problems in legislation of Ukraine were proposed.Such as changing our legislation and giving the exact explanation in who is the ownerof copyright on works created due to artificial intelligence and in which cases this personcould become an owner of the copyright. However, probably, these issues shouldbe resolved at international level regarding globalization.

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