CC BY
6APPLICATION OF ARTIFICIAL INTELLIGENCE IN ECG ANALYSIS: PROBLEMS AND THEIR SOLUTIONS IN
HEALTHCARE
1Mirametov Ali Baxitbayevich, 2Abdullayev Ibroximjon Nigmatilla ugli, 3Nazirov Raximxo'ja Maxmudxujayevich, 4Tashev Bekjigit Jonanbek ugli
1PhD student of Tashkent State Technical University 2,3,4Assistant of Tashkent State Technical University https://doi.org/10.5281/zenodo.10886122
Abstract. To medicine artificial intellect technologies current reach health storage in the world main from trends is one AI and neuron networks whole the world medicine fundamentally hange possible: diagnosis system changes, new drug tools work to exit help gives, in general medical services quality increases and expenses reduces the possibilities of AI in the future almost is infinite.
Keywords: medicine, artificial intellect, neuron network, ECG, diagnostics, doctors, patients.
1. INTRODUCTION
Today's in the day artificial intellect simple tasks with great work performs An example for ,it's on the x-ray foreign body or of pathology existence to determine , as well ascytological in the material cancer cells existence to determine able Various medical information analysis in doing artificial intellect already great the results showing ultrasound and using MRI pathologies to determine accuracy exceeds 90% .This is programming in the language not but simple a person in the language given to questions answer to give able is a supercomputer.
2. MATERIALS AND METHODS
The Watson Health Cloud platform is a robust and secure cloud-based infrastructure developed by IBM Watson Health. It serves as a centralized hub for storing, analyzing, and sharing vast amounts of healthcare data. This platform leverages advanced technologies such as artificial intelligence (AI), machine learning, and natural language processing to derive insights from various sources of healthcare information, including medical records, scientific articles, and diagnostic images.
Materials and methods for utilizing the Watson Health Cloud platform typically involve:
1. Data Collection: Gathering diverse healthcare data sources, including electronic health records (EHRs), medical images, genomic data, and patient-reported outcomes.
2. Data Standardization: Ensuring consistency and compatibility of data formats across different sources through standardization processes to facilitate analysis and integration.
3. Data Security and Privacy: Implementing robust security measures to protect sensitive patient information and comply with healthcare regulations, such as HIPAA (Health Insurance Portability and Accountability Act).
4. Data Analysis: Utilizing advanced analytics techniques, including machine learning algorithms, to extract meaningful insights, identify patterns, and predict outcomes from the collected data.
5. Application Development: Building applications and tools on top of the Watson Health Cloud platform to address specific healthcare challenges, such as disease diagnosis, treatment optimization, and population health management.
6. Collaboration and Integration: Facilitating collaboration among healthcare providers, researchers, and developers to leverage the platform's capabilities and integrate it into existing workflows and systems.
Overall, the Watson Health Cloud platform provides a comprehensive ecosystem for healthcare organizations to harness the power of data-driven insights and AI technologies to improve patient care, drive innovation, and advance medical research.
MedyMatch technology
MedyMatch is a medical technology company that specializes in developing artificial intelligence (AI) solutions for radiology and emergency medicine. Their technology aims to assist healthcare providers in the rapid and accurate interpretation of medical imaging studies, such as CT scans and MRIs, particularly in emergency situations where timely diagnosis is crucial.
MedyMatch's AI algorithms are designed to analyze medical images and provide clinical decision support to radiologists and other healthcare professionals. By leveraging machine learning and deep learning techniques, their software can help identify potential abnormalities, prioritize urgent cases, and improve diagnostic accuracy.
The company's flagship product, named "Insight," is designed to integrate seamlessly into existing hospital workflows, providing real-time assistance to clinicians during the interpretation of imaging studies. MedyMatch's technology has the potential to enhance patient care by expediting the diagnosis of critical conditions such as intracranial hemorrhage and stroke.
Overall, MedyMatch aims to harness the power of AI to augment the capabilities of healthcare providers and improve patient outcomes through more efficient and accurate diagnostic processes.
AliveCor
AliveCor is a medical technology company known for its innovative products in the field of mobile health and cardiology. One of their primary offerings is the KardiaMobile device, which is a portable electrocardiogram (ECG) monitor that can be attached to a smartphone or tablet.
The KardiaMobile device enables users to record their ECG anytime, anywhere, by simply placing their fingers on the sensors of the device. The recorded ECG data is then transmitted wirelessly to the accompanying Kardia app, where it can be analyzed for signs of atrial fibrillation (AFib), bradycardia, tachycardia, or normal sinus rhythm.
AliveCor's technology is designed to empower individuals to take a proactive approach to their heart health by monitoring their heart rhythm regularly and detecting potential irregularities early on. The Kardia app provides users with instant feedback on their ECG recordings and allows them to share the data with their healthcare providers for further evaluation.
In addition to the KardiaMobile device, AliveCor also offers other products and services aimed at improving cardiovascular care, including the KardiaBand, which is an ECG-enabled smartwatch band for the Apple Watch, and the KardiaPro platform, which is designed for healthcare professionals to remotely monitor and manage patients' cardiac health.
Mendel.ai
Mendel.ai is a healthcare technology company that specializes in using artificial intelligence (AI) to assist medical professionals in diagnosing and treating diseases, particularly
cancer. The company's AI platform analyzes medical records, including pathology reports, imaging studies, and genetic data, to provide clinicians with personalized treatment recommendations and insights.
Mendel.ai's technology leverages machine learning algorithms to sift through vast amounts of medical data and identify relevant information that can aid in diagnosis and treatment planning. By automating time-consuming tasks such as data extraction and analysis, the platform aims to help healthcare providers make more informed decisions and improve patient outcomes.
One of the key features of Mendel.ai's platform is its ability to synthesize information from disparate sources and present it in a clear and actionable format for clinicians. This can help reduce diagnostic errors, streamline the treatment process, and ensure that patients receive the most appropriate care based on their individual characteristics and medical history.
3. RESULTS AND DISCUSSION
Hospital management for
Health storage AI clinic in the field in management significant level help to give can Today's in the day of this for special work developed projects there is:
The healthcare technology company that specializes in virtual care solutions, particularly asynchronous telehealth visits. Their flagship product, SmartExam, is an asynchronous telemedicine platform that allows patients to receive care remotely from healthcare providers.
With SmartExam, patients can access virtual visits through a secure online portal or mobile app, where they can complete a structured questionnaire about their symptoms and medical history. The platform then uses artificial intelligence (AI) to analyze the patient's responses and generate a comprehensive clinical note for the provider.
Healthcare providers can review the patient's information at their convenience and respond with a diagnosis, treatment plan, and any necessary prescriptions or recommendations. This asynchronous approach allows for efficient care delivery without the need for real-time interaction between the patient and provider.
Bright.md's platform is designed to streamline the virtual care process, reduce administrative burden, and improve access to healthcare for patients. By leveraging technology to automate routine tasks and facilitate communication between patients and providers, they aim to enhance the overall patient experience and outcomes.
In medicine artificial intellect
"In medicine, the use of artificial intelligence (AI) is becoming increasingly prevalent in many countries. Of course, the progress in technology is mostly seen in the USA and Asia, but Europe is also making significant advancements in utilizing AI for healthcare. AI in medicine is currently being employed through various methods, such as utilizing medical records, analyzing images, speech recognition, and online disease diagnosis via the internet.
In 2017, the development of AI systems for diagnosing images, including X-rays, ultrasounds, and MRIs, began. This enables healthcare professionals to assess the health conditions of patients based on images, including those taken at home. Additionally, AI systems provide the opportunity for oncologists to seek advice and accurately determine cancer cells by collaborating with their colleagues through the TeleMD system.
In order to implement these advancements, it is necessary to establish appropriate legal frameworks and regulations in the field. Artificial intelligence in medicine requires the attention
and participation not only from investors, doctors, and patients but also from lawmakers and producers who must address the demands and challenges posed by these new solutions."
ECG Deep Neural Network Diagnosis
Figure 1. Comparison of existing models for automatic diagnosis of ECG abnormalities (A) Two-stage traditional methods using feature engineering; (B) end-to-end deep learning
methods.
Existing models for automatic diagnosis of ECG abnormalities vary in terms of their architecture, performance, and intended use cases. Here's a comparison of some common approaches:
1. Traditional Rule-Based Systems:
- Architecture: Rule-based systems use predefined algorithms and heuristics to detect specific ECG abnormalities based on criteria such as waveform morphology, duration, and intervals.
- Performance: These systems can be effective for detecting well-defined abnormalities but may struggle with subtle or complex patterns. They often require manual tuning and validation.
- Intended Use: Rule-based systems are suitable for basic screening and initial triage but may lack the flexibility to adapt to diverse datasets and evolving diagnostic criteria.
2. Deep Learning Models:
- Architecture: Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), learn hierarchical representations of ECG signals directly from raw data.
- Performance: Deep learning models have demonstrated state-of-the-art performance in ECG interpretation tasks, achieving high accuracy and generalization across diverse populations and pathologies.
- Intended Use: Deep learning models are well-suited for complex diagnostic tasks, including multi-class classification of ECG abnormalities, rhythm analysis, and risk stratification.
"AI is highly effective when dealing with large volumes of data, as it significantly increases the accuracy and efficiency of diagnosis. Specialists would need many millions of medical records
and years of learning to achieve similar results, while the computer can accomplish this in a short amount of time. By taking over routine tasks, AI allows doctors to focus on saving lives and prioritizing patient health. AI programs are available to assist with tasks such as equipment searches, selecting appropriate wards, and monitoring good health. This helps reduce the number of medical errors. In today's world, artificial intelligence is often used for diagnosis with high accuracy. When doctors and AI work together, the probability of errors decreases, approaching statistical levels [2].
"Medical data problems
"AI studies for patients involve using medical records. The data in these records may be incomplete, making it impossible to obtain all the necessary information. There are various uncertainties and mistakes inherent in the documents that patients provide about their life features, conditions, habits (including harmful ones), and other important data that may not be included. Currently, efficient mechanisms for collecting this information do not exist. However, it should be noted that if the data contains uncertainties and mistakes, the quality of the systems used for processing and analyzing the data may decrease [4]."
Transparent decisions algorithm
"Artificial intelligence (AI) systems operate based on the 'black box' principle. This means that the program's operator cannot determine how the AI came to a particular decision, which can lead to incorrect solutions. It is almost impossible to determine the exact reasons behind a wrong decision made by an AI system.
Creating and implementing AI systems require substantial financial resources. The high cost is mainly due to program development and the collection of relevant information in medical institutions. Furthermore, the maintenance of AI systems requires a qualified and dedicated team. AI systems deliver efficient and fast performance, but their implementation in medical institutions is not simple. If the computer network of an institution is connected to the outside, it increases the probability of corruption and interruption of work by hackers.
The effectiveness of AI in the medical field depends on how well the system is adapted and the acceptance of its decisions by healthcare professionals. The health and lives of people directly depend on the accuracy of the system's decisions [1-8]."
4. CONCLUSION
"Despite the current challenges faced by AI systems, there are encouraging prospects for overcoming these difficulties and finding solutions. Highly qualified experts, talented researchers, excellent mathematicians, doctors, representatives from pharmaceutical enterprises, and others from different corners of the world have come together to work constantly on the development of this field. However, even with the advancements in AI, the role of human beings as leaders in the field of healthcare remains crucial for ensuring the storage and protection of human health."
REFERENCES
1. Nematov SQ, Kamolova YM, Abdullayev IN. Modern algorithmic methods for the analysis of speech disorders after a stroke . Science and Education. 2023;4(6):452-456.
2. Flyverbom, M.: Transparency: Mediation and the management of visibilities. International Journal of Communication 10(1) (2016) 110-122
3. Nematov SQ, Kamolova YM, Abdullayev IN. Speech therapy for adults. Science and Education. 2022;3(10):48-53.
4. Chouldechova, A.: Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data 5(2) (2017) 153-163
5. Dongdong Zhang, Samuel Yang, Xiaohui Yuan, Ping Zhang. "Interpretable deep learning for automatic diagnosis of 12-lead electrocardiogram". iScience 24, 102373, April 23, 2021. DOI:https://doi.org/10.1016/j.isci.2021.102373
6. "Алгоритм анализа и обработки временных параметров сигнала экг для диагностики опасных аритмий" "SCIENCE AND INNOVATION" international scientific journal volume 1 issue 7,Uif-2022: 8.2 | issn: 2181-3337
7. S Hamrokulov, G Kushiev, B Isakov, Z Umarkhodjaeva DIGITAL MICROSCOPE Analysis of chemically cleaned silicon surface Science and innovation, 2023. Т.2 №5 Pp.138-142
8. Абдурахманов Б.А., Исамов С.Б., Кушиев Г.А. Автоматизированная установка определения параметров полупроводников методом Ван Дер Пау. Приборы, 2022 №2. С.14-18.
