A study on “impact of artificial intelligence in covid19 diagnosis”
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The study addresses the impact of artificial intelligence (AI) on COVID-19 diagnosis using medical imaging techniques like X-rays and CT scans, emphasizing the urgent need for rapid and reliable diagnostic methods during the pandemic. It proposes a theoretical AI-based system that classifies respiratory disorders, including COVID-19, by analyzing cough sounds and patient symptoms. The research highlights the potential for AI to enhance diagnostic accuracy and efficiency in managing respiratory diseases.
![A study on “Impact of Artificial Intelligence in COVID19
Diagnosis”
G Sadhana [1]
, Dr. C.V. Suresh Babu[2]
[1]
Student, Dept. of Information Technology, Hindustan University
[2]
Professor, Dept. of Information Technology, Hindustan University
ABSTRACT
Although the lungs are one of the most vital organs in the body, they are vulnerable to infection and
injury. COVID-19 has put the entire world in an unprecedented difficult situation, bringing life to a
halt and claiming thousands of lives all across the world. Medical imaging, such as X-rays and
computed tomography (CT), is essential in the global fight against COVID-19, and newly emerging
artificial intelligence (AI) technologies are boosting the power of imaging tools and assisting
medical specialists. AI can improve job efficiency by precisely identifying infections in X-ray and
CT images and allowing further measurement. We focus on the integration of AI with X-ray and
CT, both of which are routinely used in frontline hospitals, to reflect the most recent progress in
medical imaging and radiology combating COVID-19.
Keywords: COVID-19, Computed Topography, Cough Classificication, Cough analysis,
Detection, frequency, Artificial Intelligence, X-ray, Cough characteristics, Energy distribution
INTRODUCTION
Over 3 million cases have been reported worldwide as a result of the COVID-19 epidemic. Early
detection of the disease is critical not only for individual patient care and treatment implementation,
but also for ensuring proper patient isolation and disease containment on a larger public health
scale. There is an unmet need for speedy, reliable, and unsupervised diagnostic assays for SARS-
CoV-2 in the current setting of stress on healthcare resources due to the COVID-19 outbreak,
including a lack of RT–PCR test kits. This work offers a theoretical end-to-end point-of-care system
for classifying and diagnosing various respiratory disorders, including early identification of
COVID-19, that is backed by an artificial intelligence (AI) module. The hardware and software
components of the proposed theoretical system are innovative. Using sensors, the system will be
able to capture patients' or users' symptoms, such as body temperature, cough sound, and
ventilation. The captured data will subsequently be converted to health data and analyzed by a
machine learning module to identify patterns and classify the combined symptoms for various
respiratory disorders, including COVID-19.
BACKGROUND AND MOTIVATION
AI can learn features from a massive volume of healthcare data using complex algorithms, and then
use the findings to aid clinical practice. It could also have learning and self-correcting capabilities to
enhance accuracy depending on the input. Physicians can benefit from AI systems that provide up-
to-date medical information from journals, textbooks, and clinical practices to help them provide
effective patient care. Furthermore, an AI system can aid in the reduction of diagnostic and
treatment errors, which are unavoidable in human clinical practice. Furthermore, an AI system
collects usable data from a huge patient population to aid in developing real-time conclusions for
health risk alerts and predictions.](https://image.slidesharecdn.com/astudyonimpactofartificialintelligenceincovid19diagnosis-210903153644/85/A-study-on-impact-of-artificial-intelligence-in-covid19-diagnosis-1-320.jpg)
A study on “impact of artificial intelligence in covid19 diagnosis”
- 1. A study on “Impact of Artificial Intelligence in COVID19 Diagnosis” G Sadhana [1] , Dr. C.V. Suresh Babu[2] [1] Student, Dept. of Information Technology, Hindustan University [2] Professor, Dept. of Information Technology, Hindustan University ABSTRACT Although the lungs are one of the most vital organs in the body, they are vulnerable to infection and injury. COVID-19 has put the entire world in an unprecedented difficult situation, bringing life to a halt and claiming thousands of lives all across the world. Medical imaging, such as X-rays and computed tomography (CT), is essential in the global fight against COVID-19, and newly emerging artificial intelligence (AI) technologies are boosting the power of imaging tools and assisting medical specialists. AI can improve job efficiency by precisely identifying infections in X-ray and CT images and allowing further measurement. We focus on the integration of AI with X-ray and CT, both of which are routinely used in frontline hospitals, to reflect the most recent progress in medical imaging and radiology combating COVID-19. Keywords: COVID-19, Computed Topography, Cough Classificication, Cough analysis, Detection, frequency, Artificial Intelligence, X-ray, Cough characteristics, Energy distribution INTRODUCTION Over 3 million cases have been reported worldwide as a result of the COVID-19 epidemic. Early detection of the disease is critical not only for individual patient care and treatment implementation, but also for ensuring proper patient isolation and disease containment on a larger public health scale. There is an unmet need for speedy, reliable, and unsupervised diagnostic assays for SARS- CoV-2 in the current setting of stress on healthcare resources due to the COVID-19 outbreak, including a lack of RT–PCR test kits. This work offers a theoretical end-to-end point-of-care system for classifying and diagnosing various respiratory disorders, including early identification of COVID-19, that is backed by an artificial intelligence (AI) module. The hardware and software components of the proposed theoretical system are innovative. Using sensors, the system will be able to capture patients' or users' symptoms, such as body temperature, cough sound, and ventilation. The captured data will subsequently be converted to health data and analyzed by a machine learning module to identify patterns and classify the combined symptoms for various respiratory disorders, including COVID-19. BACKGROUND AND MOTIVATION AI can learn features from a massive volume of healthcare data using complex algorithms, and then use the findings to aid clinical practice. It could also have learning and self-correcting capabilities to enhance accuracy depending on the input. Physicians can benefit from AI systems that provide up- to-date medical information from journals, textbooks, and clinical practices to help them provide effective patient care. Furthermore, an AI system can aid in the reduction of diagnostic and treatment errors, which are unavoidable in human clinical practice. Furthermore, an AI system collects usable data from a huge patient population to aid in developing real-time conclusions for health risk alerts and predictions.
- 2. LITERATURE REVIEW Authors/years Methodology Domain Article Type Kang Zhang, Xiaohong Liu, Jun Shen, Tianxin Lin, Weimin Li, Guangyu Wan(2020) AI system that can diagnose COVID-19 pneumonia using CT scan. Prediction of progression to critical illness. Potential to improve performance of junior radiologists to the senior level. Can assist evaluation of drug treatment effects with CT quantification. Computed Tomography Development sabella Castiglioni, Davide Ippolito, Matteo Interlenghi, Caterina Beatrice Monti, Christian , Simone Schiaffino Salvatore, Annalisa Polidori, Davide Gandola, Cristina Messa and Francesco Sardanelli(2020) Artificial Intelligence, Computer Sensitivity and Specificity, X- Rays Neural Networks Experiment Ilker Ozsahin, Boran Sekeroglu , Musa Sani Musa , Mubarak Taiwo Mustapha and Dilber Uzun Ozsahin (2020) Laboratory-based and chest radiography approach Computed Tomography Experiment
- 3. Mohammad (Behdad) Jamshidi , Ali Lalbakhsh , (Member, Ieee), Jakub Talla , Zdeněk Peroutka3 , (Member, Ieee), Farimah Hadjilooei , Pedram Lalbakhsh , Morteza Jamshidi , Luigi La Spada, Mirhamed Mirmozafari , (Member, Ieee), Mojgan Dehghani , Asal Sabet, Saeed Roshani, (Member, Ieee), Sobhan Roshani11, Nima Bayat-Makou , (Member, Ieee), Bahare Mohamadzade , (Student Member, Ieee), Zahra Malek , Alireza Jamshidi, Sarah Kiani15, Hamed Hashemi-Dezaki , And Wahab Mohyuddin(Member, IEEE)(2020) Artificial intelligence, big data, bioinformatics, biomedical informatics, deep learning, diagnosis, treatment. machine learning Experiment Xueyan Mei, Hao-Chih Lee, Kai-yue Diao, Mingqian Huang, Bin Lin, Chenyu Liu , Zongyu Xie, Yixuan Ma, Philip M. Robson, Michael Chung , Adam Bernheim, Venkatesh Mani, Claudia Calcagno, Kunwei Li , Shaolin Li, Hong Shan , Jian Lv, Tongtong Zhao, Junli Xia, Qihua Long, Sharon Steinberger, Adam Jacobi, Timothy Deyer, Marta Luksza, Fang Liu , Brent P. Little Zahi A. Fayad and Yang Yang(2020) Study participants, Clinical information, Reader studies, AI models, Convolution neural network model, Statistical analysis Image processing, Machine Learning Development
- 4. Feng Shi , Jun Wang , Member, IEEE, Jun Shi , Ziyan Wu , Qian Wang, Zhenyu Tang, Kelei He , Yinghuan Shi , and Dinggang Shen(2021) artificial intelligence, image acquisition, segmentation, diagnosis Image processing. Experiment PROPOSED METHOD The goal of cough classification is to create an automatic system that can classify many aspects of coughs, such as cough severity, time-frequency, energy distribution, and whether the cough is wet or dry. Varied respiratory disorders, such as Bronchitis, Tuberculosis, and Asthma, can have different effects on the pulmonary system, and hence can be distinguished by differences in cough sound. Asthmatic patients' coughs, for example, exhibit distinct energy signatures than non-asthmatic patients' coughs. Asthmatic coughs, in particular, have more energy in the low-frequency range. The study of cough phases, on the other hand, demonstrates that dry coughs in phase two are less intense than wet coughs. Furthermore, most of the signal strength of wet coughs is found to be between 0 and 750 Hz during this period, whereas that of dry coughs is found to be between 1,500 and 2,250 Hz. As a result, to cover all cough kinds, most cough recording tests have used a sampling frequency of 48,000 to 22,050Hz. Cough Audio Analysis The acoustic sound of a cough is generated by the contractions of the respiratory muscles. The cough, with its typical sound, is the result of the sudden opening of the glottis opening suddenly due to a rapid exhalation of air from the lungs. Cough Segmentation and Detection This procedure involves cleaning the cough sounds dataset by removing any interferences and environmental noise from the audio frames and maintaining only the frames that are related to the cough. The cough noises are extracted as part of the audio separation process. Independent Component Analysis (ICA) and Blind Source Separation are two methodologies for source separation (BSS) and Informed Source Separation (ISS). Feature Selection and Discriminant Analysis Shannon Entropy (SH), Fisher score, Mel-Frequency Cepstral Coefficients, and Zero Crossing Rate are some of the strategies for feature selection (ZCR). The MFCC approach, on the other hand, has acquired popularity as a result of its efficacy in the analysis of speech and sound signals in general and is thus used in the study of cough noises. During the pre-processing of cough sounds, features are chosen for two reasons: first, to reduce dimensionality for feature matrix classification, and second, to extract the most dominating information available in the cough sound. Any noise in the cough sound must be filtered out before feature extraction. Mel-Frequency Spectral Coefficients The adoption of the Mel scale is driven by the fact that the human ear can distinguish elements of an audio source better at low frequencies than at high frequencies. As a result, the Mel scale assures
- 5. that signal properties are more closely aligned with what humans hear. The Mel scale can be calculated using the following formula: M(f) = 1125 ln (1 + f 700 ) …(1) M−1 (m) = 700(exp m 1125 ) – 1 … (2) Cough Classification and Machine Learning Gradient boosted decision trees (XGBoost), Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Fuzzy Deep Neural Network are the most commonly used classifiers (FDNN). Data Analysis The COVID-19 pandemic has demonstrated that now is the moment for everyone to contribute to the construction of public cough datasets that can contain all varieties of cough for each respiratory ailment. Encourage individuals, hospitals, and healthcare organizations to donate their medical records to science to speed up the process. RESULT Following that, the collected data will be translated to health data and evaluated by a machine learning module to find patterns and classify the combined symptoms of several respiratory illnesses, including COVID-19. CONCLUSION The above-proposed method can be very helpful in the early detection of not only COVID 19 but also other lungs and respiratory system-related diseases. FUTURE WORKS For future works, we would like to develop the device app in accordance with the above-mentioned theory. ACKNOWLEDGMENT We thank all the Faculty members of our department, our classmates, and other anonymous reviewers for the valuable comments on our draft paper. DISCLOSURE STATEMENT No potential conflict of interest was reported by the authors. REFERENCES Kang Zhang, Xiaohong Liu, Jun Shen, Tianxin Lin, Weimin Li, Guangyu Wan(2020) “Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography”, Cell 181, 1423–1433, June 11, 2020, Elsevier Inc. sabella Castiglioni, Davide Ippolito, Matteo Interlenghi, Caterina Beatrice Monti, Christian , Simone Schiaffino Salvatore, Annalisa Polidori, Davide Gandola, Cristina Messa and Francesco Sardanelli(2020), “Artificial intelligence applied on chest X-ray can aid in the diagnosis of COVID- 19 infection: a first experience from Lombardy, Italy”.
- 6. Ilker Ozsahin, Boran Sekeroglu , Musa Sani Musa , Mubarak Taiwo Mustapha and Dilber Uzun Ozsahin (2020), “Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence”, Hindawi Computational and Mathematical Methods in Medicine. Mohammad (Behdad) Jamshidi , Ali Lalbakhsh , (Member, Ieee), Jakub Talla , Zdeněk Peroutka3 , (Member, Ieee), Farimah Hadjilooei , Pedram Lalbakhsh , Morteza Jamshidi , Luigi La Spada, Mirhamed Mirmozafari , (Member, Ieee), Mojgan Dehghani , Asal Sabet, Saeed Roshani, (Member, Ieee), Sobhan Roshani11, Nima Bayat-Makou , (Member, Ieee), Bahare Mohamadzade , (Student Member, Ieee), Zahra Malek , Alireza Jamshidi, Sarah Kiani15, Hamed Hashemi-Dezaki , And Wahab Mohyuddin(Member, IEEE)(2020), “Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment”, SPECIAL SECTION ON EMERGING DEEP LEARNING THEORIES AND METHODS FOR BIOMEDICAL ENGINEERING Xueyan Mei, Hao-Chih Lee, Kai-yue Diao, Mingqian Huang, Bin Lin, Chenyu Liu , Zongyu Xie, Yixuan Ma, Philip M. Robson, Michael Chung , Adam Bernheim, Venkatesh Mani, Claudia Calcagno, Kunwei Li , Shaolin Li, Hong Shan , Jian Lv, Tongtong Zhao, Junli Xia, Qihua Long, Sharon Steinberger, Adam Jacobi, Timothy Deyer, Marta Luksza, Fang Liu , Brent P. Little Zahi A. Fayad and Yang Yang(2020), “Artificial intelligence–enabled rapid diagnosis of patients with COVID-19”, Nature Medicine. Feng Shi , Jun Wang , Member, IEEE, Jun Shi , Ziyan Wu , Qian Wang, Zhenyu Tang, Kelei He , Yinghuan Shi , and Dinggang Shen(2021), “Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19”, IEEE REVIEWS IN BIOMEDICAL ENGINEERING