International Journal of Telemedicine and Applications最新文献

{"title":"Physicians' Perspective of Telemedicine Regulating Guidelines and Ethical Aspects: A Saudi Experience.","authors":"Dalia Yahia M El Kheir,&nbsp;Sara S Alnufaili,&nbsp;Raghad M Alsaffar,&nbsp;Majd A Assad,&nbsp;Zahra Z Alkhalifah","doi":"10.1155/2022/5068998","DOIUrl":"https://doi.org/10.1155/2022/5068998","url":null,"abstract":"<p><strong>Methods: </strong>This was an observational cross-sectional study conducted among physicians working at the College of Medicine, Imam Abdulrahman Bin Faisal University (IAU), and its affiliated academic tertiary hospital, King Fahad Hospital of the University (KFHU), Eastern province-KSA. Data were collected between August 2019 and March 2020 via a structured, pretested, self-administered questionnaire distributed to 220 eligible physicians at KFHU. A final sample of 101 physicians completed our survey. Collected data was coded and analysed using SPSS, and the results presented as frequencies, percentages, and summary statistics.</p><p><strong>Results: </strong>Among our respondents, 62 (61.4%) were males, 46 (45.5%) were in the age group of 35 to 44 years, and 62 (61.3%) were Saudi. Two-thirds of physicians (58 (57.4%)) use smart devices in healthcare delivery, and 51 (50.5%) are satisfied with this use. A minority (21 (20.8%)) knew about telemedicine guidelines, 8 (7.9%) had encountered legal issues related to telemedicine use, and 52 (51.5%) were doubtful about patients' readiness for virtual care. Regarding physicians' awareness of the ethical aspects regulating the use of social media and medical apps in patient care, only 44 (45.3%) were aware of the proper reporting system if they found accounts sharing unreliable information. Nevertheless, the majority (91 (92.9%)) agreed it is essential for healthcare providers to report such accounts. Concerning physicians' awareness of the rules and regulations of online self-promotion, the majority of our respondents were unsure or unaware of such regulations (46 (45.6%) and 18 (17.8%)), respectively. Nonetheless, 67 (66.3%) of the physicians would not pay SM influencers to advertise for them.</p><p><strong>Conclusion: </strong>Two-thirds of our physicians use smart devices in healthcare delivery, with 1 in 13 having encountered related legal issues. Nonetheless, only a minority was aware of telemedicine use regulating guidelines, including physicians' online self-promotion regulations. These results highlight the necessity of targeted physicians' training on telemedicine use related guidelines, thereby ensuring the safety of both patients and healthcare professionals and the optimum utilization of online health-related interactions.</p>","PeriodicalId":45630,"journal":{"name":"International Journal of Telemedicine and Applications","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581664/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40655555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Telehealth and Transformation of Nursing Care in Saudi Arabia: A Systematic Review.","authors":"Ibrahim Al Baalharith,&nbsp;Mona Al Sherim,&nbsp;Sarah Hamad G Almutairi,&nbsp;Azizah Saleh Alhaggas Albaqami","doi":"10.1155/2022/8426095","DOIUrl":"https://doi.org/10.1155/2022/8426095","url":null,"abstract":"<p><strong>Introduction: </strong>Technological advancements have transformed nursing care, quality, and education across the globe. In the Kingdom of Saudi Arabia (KSA), the inventions and adoption of mobile technologies such as an e-health application (app) called SEHA continue to revolutionize the healthcare system in the country.</p><p><strong>Purpose: </strong>The present systematic review is aimed at examining the technological impact on nursing in Saudi Arabia. The study provides a comprehensive analysis of telehealth and its role in nursing quality, nursing practice, and education.</p><p><strong>Methods: </strong>The present study adopted a literature review methodology by deriving data from journal articles from different databases, for example, Web Science, Google Scholar, CINAHL, MEDLINE, and PubMed databases. Inclusive years for the search ranged from 2016 to 2022. A total of eight articles were found dovetailing to meet the research objectives and answer research questions.</p><p><strong>Result: </strong>After appraising and analyzing the research, the present review found that (Abolfotouh et al., 2019) telehealth in nursing is loosely researched; (Ahmed et al., 2021) telehealth impacts nursing practice and quality by fostering nurse-patient communication promoting positive outcomes, seamless nursing care, and positive experiences; and (Albahri et al., 2021) telehealth and telemedicine is a central tenet of contemporary nursing education and practice.</p><p><strong>Conclusion: </strong>From these findings, this analysis informed three key recommendations: the need to integrate telehealth into the nursing curriculum, telehealth training, and reskilling among healthcare workers (HCWs) in KSA and further primary studies focusing predominantly on telenursing. Overall, telehealth remains a fundamental transformation of nursing practice that forms a central ideology in the contemporary nursing process.</p>","PeriodicalId":45630,"journal":{"name":"International Journal of Telemedicine and Applications","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2022-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9553846/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33516263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multifactor Authentication for Smart Emergency Medical Response Transporters.","authors":"Turki Alghamdi,&nbsp;Fayez Gebali,&nbsp;Fares Salem","doi":"10.1155/2022/5394942","DOIUrl":"https://doi.org/10.1155/2022/5394942","url":null,"abstract":"<p><p>Securing telehealth IoT infrastructure is essential to provide high-level medical care and prevent cyberattacks. A vulnerable stage in IoT telehealth is while the patient is being transported to a healthcare facility, the transporter could be an ambulance or an air ambulance. In this paper, we propose a multifactor authentication scheme to secure the system when the patient is in transit to the healthcare facility. We apply this scheme to an ambulance, using physical unclonable functions (PUFs) embedded in the ambulance to facilitate authentication and secure key exchange. We validated the security of the proposed scheme using formal and informal security analysis. The analysis supports our claim that the proposed scheme protects against many types of attacks.</p>","PeriodicalId":45630,"journal":{"name":"International Journal of Telemedicine and Applications","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9525787/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33486776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smartphone Application for Celiac Patients: Assessing Its Effect on Gastrointestinal Symptoms in a Randomized Controlled Clinical Trial.","authors":"Zeinab Nikniaz, Zahra Akbari Namvar, Masood Shirmohammadi, Elham Maserat","doi":"10.1155/2022/8027532","DOIUrl":"10.1155/2022/8027532","url":null,"abstract":"<p><strong>Introduction: </strong>Considering the lack of inclusive Persian application for celiac patients that covers all aspects of the GFD, we developed a Persian-language application for patients with CD and assessed the effectiveness of a three-month educational intervention delivered via smartphone application compared with standard care on gastrointestinal symptom rating scale (GSRS) score in patients with celiac disease.</p><p><strong>Methods: </strong>In the present parallel randomized controlled clinical trial, 60 patients with CD were assigned randomly to receive education through a smartphone application (<i>n</i> = 30) or conventional clinical education (<i>n</i> = 30). The patients were asked to use it for getting the required information for three months. We assessed the gastrointestinal symptoms using the gastrointestinal symptom rating scale (GSRS) questionnaire at baseline and three months after interventions. The GSRS total score, celiac disease GSRS (CD-GSRS) score, abdominal pain, reflux, diarrhea, constipation, and indigestion scores were calculated.</p><p><strong>Results: </strong>Out of 60 randomized patients, 58 patients completed the study. In comparison to baseline, the mean score of CD-GSRS score (<i>p</i> = 0.001), and indigestion subscore (<i>p</i> < 0.001) were significantly decreased in the intervention group. The results of the between-group comparisons showed that there was a significant difference between the two groups only in the mean score of indigestion (<i>p</i> = 0.002).</p><p><strong>Conclusion: </strong>According to the results, using a smartphone application for providing information to patients with celiac disease had a significant positive effect on indigestion symptoms compared with routine clinic education. <i>Trial Registration</i>. This trial is registered with the Iranian registry of clinical trials (IRCT code: IRCT20170117032004N2; trial registry date: 2019.6.26).</p>","PeriodicalId":45630,"journal":{"name":"International Journal of Telemedicine and Applications","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9286948/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40531879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosis-Based Hybridization of Multimedical Tests and Sociodemographic Characteristics of Autism Spectrum Disorder Using Artificial Intelligence and Machine Learning Techniques: A Systematic Review.","authors":"M E Alqaysi, A S Albahri, Rula A Hamid","doi":"10.1155/2022/3551528","DOIUrl":"10.1155/2022/3551528","url":null,"abstract":"<p><p>Autism spectrum disorder (ASD) is a complex neurobehavioral condition that begins in childhood and continues throughout life, affecting communication and verbal and behavioral skills. It is challenging to discover autism in the early stages of life, which prompted researchers to intensify efforts to reach the best solutions to treat this challenge by introducing artificial intelligence (AI) techniques and machine learning (ML) algorithms, which played an essential role in greatly assisting the medical and healthcare staff and trying to obtain the highest predictive results for autism spectrum disorder. This study is aimed at systematically reviewing the literature related to the criteria, including multimedical tests and sociodemographic characteristics in AI techniques and ML contributions. Accordingly, this study checked the Web of Science (WoS), Science Direct (SD), IEEE Xplore digital library, and Scopus databases. A set of 944 articles from 2017 to 2021 is collected to reveal a clear picture and better understand all the academic literature through a definitive collection of 40 articles based on our inclusion and exclusion criteria. The selected articles were divided based on similarity, objective, and aim evidence across studies. They are divided into two main categories: the first category is \"diagnosis of ASD based on questionnaires and sociodemographic features\" (<i>n</i> = 39). This category contains a subsection that consists of three categories: (a) early diagnosis of ASD towards analysis, (b) diagnosis of ASD towards prediction, and (c) diagnosis of ASD based on resampling techniques. The second category consists of \"diagnosis ASD based on medical and family characteristic features\" (<i>n</i> = 1). This multidisciplinary systematic review revealed the taxonomy, motivations, recommendations, and challenges of diagnosis ASD research in utilizing AI techniques and ML algorithms that need synergistic attention. Thus, this systematic review performs a comprehensive science mapping analysis and identifies the open issues that help accomplish the recommended solution of diagnosis ASD research. Finally, this study critically reviews the literature and attempts to address the diagnosis ASD research gaps in knowledge and highlights the available ASD datasets, AI techniques and ML algorithms, and the feature selection methods that have been collected from the final set of articles.</p>","PeriodicalId":45630,"journal":{"name":"International Journal of Telemedicine and Applications","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9270139/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40605262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors Associated with Arkansans' First Use of Telehealth during the COVID-19 Pandemic.","authors":"Jennifer A Andersen,&nbsp;Holly C Felix,&nbsp;Dejun Su,&nbsp;James P Selig,&nbsp;Shawn Ratcliff,&nbsp;Pearl A McElfish","doi":"10.1155/2022/5953027","DOIUrl":"https://doi.org/10.1155/2022/5953027","url":null,"abstract":"<p><strong>Objective: </strong>To examine the factors associated with the first use of telehealth during the COVID-19 pandemic using Andersen's Model of Healthcare Utilization. Andersen's Model of Healthcare Utilization allowed the categorization of the independent variables into the following: (1) predisposing factors, including sociodemographic variables and health beliefs; (2) enabling factors, including socioeconomic status and access to care; and (3) need for care, including preexisting or newly diagnosed conditions and reasons to seek out care or to utilize a new mode of care.</p><p><strong>Methods: </strong>Potential respondents (<i>n</i> = 4,077) were identified for recruitment from a volunteer registry in Arkansas. Recruitment emails provided a study description, the opportunity to verify meeting the study's inclusion criteria and to consent for participation, and a link to follow to complete the survey online. The online survey responses were collected between July and August of 2020 (<i>n</i> = 1,137).</p><p><strong>Results: </strong>Telehealth utilization included two categories: (1) <i>utilizers</i> reported the first use of telehealth services during the pandemic, and (2) <i>nonutilizers</i> reported they had never used telehealth. Lower odds of reporting telehealth utilization during the pandemic were associated with race (Black; OR = 0.57, CI [0.33, 0.96]) and education (high School or less; OR = 0.45, CI [0.25, 0.83]). Higher odds of reporting telehealth utilization included having more than one provider (OR = 2.33, CI [1.30, 4.18]), more physical (OR = 1.12, CI [1.00, 1.25]) and mental (OR 1.53, CI [1.24, 1.88]) health conditions, and changes in healthcare delivery during the pandemic (OR = 3.49, CI [2.78, 4.38]).</p><p><strong>Conclusions: </strong>The results illustrate that disparities exist in Arkansans' utilization of telehealth services during the pandemic. Future research should explore the disparities in telehealth utilization and how telehealth may be used to address disparities in care for Black Arkansans and those with low socioeconomic status.</p>","PeriodicalId":45630,"journal":{"name":"International Journal of Telemedicine and Applications","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9237707/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40461847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Enhanced Posture Prediction-Bayesian Network Algorithm for Sleep Posture Recognition in Wireless Body Area Networks","authors":"A. Roshini, K. Kiran","doi":"10.1155/2022/3102545","DOIUrl":"https://doi.org/10.1155/2022/3102545","url":null,"abstract":"Wireless body area networks have taken their unique recognition in providing consistent facilities in health monitoring. Several studies influence physiological signal monitoring through a centralized approach using star topology in regular activities like standing, walking, sitting, and running which are considered active postures. Unlike regular activities like walking, standing, sitting, and running, the in-bed sleep posture monitoring of a subject is highly necessary for those who have undergone surgery, victims of breathing problems, and victims of COVID-19 for whom oxygen imbalance is a major issue as the mortality rate in sleep is high due to unattended patients. Suggestions from the medical field state that the patients with the above-mentioned issues are highly suggested to follow the prone sleep posture that enables them to maintain the oxygen level in the human body. A distributed model of communication is used where mesh topology is used for the data packets to be carried in a relay fashion to the sink. Heartbeat rate (HBR) and image monitoring of the subject during sleep are closely monitored and taken as input to the proposed posture prediction-Bayesian network (PP-BN) to predict the consecutive postures to increase the accuracy rate of posture recognition. The accuracy rate of the model outperforms the existing classification and prediction algorithms which take the cleaned dataset as input for better prediction results.","PeriodicalId":45630,"journal":{"name":"International Journal of Telemedicine and Applications","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49615983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficacy of Telemedicine Utilization for Cardiac Outpatients' Care during the Pandemic of COVID-19: A Large Center Experience in the Wave of the Pandemic.","authors":"Wesam A Alhejily","doi":"10.1155/2022/4156436","DOIUrl":"10.1155/2022/4156436","url":null,"abstract":"<p><strong>Introduction: </strong>Synchronous telemedicine using teleconferencing may play a role in clinical care. In a tertiary care center, video conferencing-enhanced virtual clinics were established via a new application platform. They were introduced during COVID-19 pandemic to connect patients to trained health-care providers via a secured line. While maintaining patients' privacy, they were theorized to offer effective communications and continuous clinical care. In this cross-sectional cohort study, we ought to examine the impact of service and the assistance they may offer to cardiac patients in the outpatient setup.</p><p><strong>Methods: </strong>A prospective cohort study looking at all video-conferencing virtual clinics' visits during the pandemic with primary focus on cardiac outpatient clinic, addressing primary endpoints of need for admission or emergency visits from cardiac demises during the pandemic and secondary endpoint of patient satisfaction based on patients' experiences.</p><p><strong>Results: </strong>A total of 6000 live care video-based chats were made over 10 months period from March 10^th, 2020, to January 30^th, 2021, among which 277 patients were evaluated in the virtual cardiac clinic, of these 193 (69.7%) were males, with mean age of 48 ± 15.60 (22.3%), patients were requested to present to clinic for further evaluation and testing, 20 (7.2%) patients were asked to visit the emergency room, of whom 8 (2.8%) patients were hospitalized. All 8 were admitted for high-risk findings that require immediate medical attention, 4/8 underwent cardiac catheterization, mean duration of admission was 2 ± 1 days. When compared to regular walk-in care, there was a statistical difference in admission rate and emergency visits <i>p</i> = 0.001 and <i>p</i> = 0.0001, respectively, both were statistically higher in the virtual clinic. The satisfaction rate in a 5-scale grading system was 97% of 4 and above recommending the continuation of the service beyond pandemic time.</p><p><strong>Conclusions: </strong>Video-enhanced virtual cardiology clinic works safely and efficiently during COVID-19 pandemic with a difference in admission rate and ER visits when compared to walk-in clinic. It can be used during nonpandemic time to reduce load on hospital and clinic crowdedness. It also decreases the chances of COVID-19 transmission indirectly by reenforcing physical distance.</p>","PeriodicalId":45630,"journal":{"name":"International Journal of Telemedicine and Applications","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9115730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43580260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Smart Chatbot for Interactive Management in Beta Thalassemia Patients","authors":"Alma Mohammed Alturaiki, H. Banjar, A. Barefah, Salwa A. Alnajjar, S. Hindawi","doi":"10.1155/2022/9734518","DOIUrl":"https://doi.org/10.1155/2022/9734518","url":null,"abstract":"Background β-thalassemia is an inherited blood disorder that affects the production of hemoglobin molecules owing to the reduction or absence of beta chains. Transfusion therapy has had a key role in extending the lifespan of β-thalassemia patients. This life-saving therapy is linked to numerous assessments and complications that now comprise most thalassemia management considerations. Consequently, many patients do not receive adequate information about the required assessments, as indicated by evidence-based medical guidelines. Patients with β-thalassemia may benefit from chatbots that follow up on their condition and that provide the required assessment information. Self-management will hopefully have a positive impact on health outcomes. Objectives This study aims to develop a chatbot that can assist in the management of β-thalassemia by providing the assessment information required to monitor patients' statuses. Methods The chatbot operated as a messaging system. A question/answer system was created based on knowledge pertaining to β-thalassemia assembled from experts, medical guidelines, and articles. Recommendations regarding the patient's follow-up assessment are made based on the answers. Results A prototype was implemented to demonstrate how the chatbots could dynamically and flexibly provide the assessment information required to follow up on and monitor patients. A small sample of adults with β-thalassemia used the chatbot to examine the system's usability and perceived utility. A system usability scale and utility scale were implemented to complete a post-test survey. The chatbots were considered by 34 patients, of whom the majority (72%) found them easy to use, while more than 90% of patients considered their use beneficial. Most of the participants agreed that the chatbots could improve their knowledge about their β-thalassemia assessments. Conclusion Our findings suggest that chatbots can be beneficial to the development of recommended tests and management related to the assessment of β-thalassemia.","PeriodicalId":45630,"journal":{"name":"International Journal of Telemedicine and Applications","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48238043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Malaria Diagnosis Using a Lightweight Deep Convolutional Neural Network","authors":"Varun Magotra, Mukesh Kumar Rohil","doi":"10.1155/2022/4176982","DOIUrl":"https://doi.org/10.1155/2022/4176982","url":null,"abstract":"The applications of AI in the healthcare sector are increasing day by day. The application of convolutional neural network (CNN) and mask-region-based CNN (Mask-RCCN) to the medical domain has really revolutionized medical image analysis. CNNs have been prominently used for identification, classification, and feature extraction tasks, and they have delivered a great performance at these tasks. In our study, we propose a lightweight CNN, which requires less time to train, for identifying malaria parasitic red blood cells and distinguishing them from healthy red blood cells. To compare the accuracy of our model, we used transfer learning on two models, namely, the VGG-19 and the Inception v3. We train our model in three different configurations depending on the proportion of data being fed to the model for training. For all three configurations, our proposed model is able to achieve an accuracy of around 96%, which is higher than both the other models that we trained for the same three configurations. It shows that our model is able to perform better along with low computational requirements. Therefore, it can be used more efficiently and can be easily deployed for detecting malaria cells.","PeriodicalId":45630,"journal":{"name":"International Journal of Telemedicine and Applications","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44299229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}