Cloud-based Platform Research Articles

Pilot study of radiation oncology peer review in low middle income country (LMIC) through cloud-based platform

ABSTRACT Purpose: Peer review is an essential step in clinical quality assurance for radiation therapy. There are very little data on peer reviews from low-middle-income countries (LMIC). With increasing access to advanced technologies in LMIC also, peer review is becoming more important to ensure quality and standard of care. We evaluated cloud-based e-Peer review in our network of cancer centers in India with an aim to study its feasibility and impact on care. Materials and Methods: Four out of 13 cancer centers across India were selected for this pilot study. All team members were trained on the e-Peer review platform before the initiation of the study. A lead dosimetrist from a centralized planning site was selected to share new cases every week. Cases treated with only definitive intent were selected. The link to the cases was sent through an email to reviewing physicians. The following aspects were reviewed for each case. 1) Work up and staging. 2) Treatment intent and prescription. 3) Target contours. 4) Normal organ at risk contours. 5) Dose–volume–histogram (DVH) with clinical goals attached. Cases were marked as “Not Appropriate,” “Appropriate,” “Appropriate with minor finding,” and “Represent with major revisions” as per volume and plan review. Results: Over a period of 3 months, 100 cases underwent peer review before the start of treatment. Median turnover time was 48 (interquartile range: 24-96) hours. The median time for review was 8 min with time to review cases requiring major and minor changes being 12 and 6 min, respectively (P < 0.001). Of all the cases reviewed, no changes, minor changes, and major changes were suggested for 36%, 48%, and 16% of cases, respectively. The most frequent reason for major changes was contouring corrections (15%). Also, 31.3% of major changes underwent recontouring and replanning before initiation of treatment. Conclusion: Peer review was feasible in our setting through this cloud-based peer review system, with median turnover time and time taken for review being 48 h and 8 min, respectively. Like published data from the Western world, peer review led to changes that could impact patient care delivery and outcome. We plan to implement this across the remaining centers in our network.

دراسة مقارنة التعلم الآلي في التصنيف الفعال للبيانات متعددة الأطياف في الزراعة

يتطلب أن تكون خرائط محاصيل موثوقة ودقيقة لتحقيق الأمن الغذائي من المستوى الإقليمي إلى المستوى العالمي. يؤدي التوفر المتزايد لصور الأقمار الصناعية إلى مشكلة "البيانات الضخمة" أثناء إنتاج خرائط المحاصيل. الآن، اكتسبت المنصات السحابية الكثير من الاهتمام لتصنيف المحاصيل في مناطق واسعة. الهدف الرئيسي من البحث هو تحليل تصنيف المحاصيل باستخدام مختلف التعلم الآلي (ML) مثل آلة دعم المتجهات (SVM)، وتعزيز شجرة التدرج (GTB)، والغابات العشوائية (RF)، وشجرة القرار (DT) بالإضافة إلى التصنيف والتصنيف. أشجار الانحدار (CART) على منصة محرك Goggle Earth. الهدف هو استكشاف كفاءة محرك Google Earth (GEE) عند تصنيف المحاصيل المختلفة باستخدام مجموعات البيانات متعددة الأطياف من Sentinel 2 MSI بالإضافة إلى الأقمار الصناعية Landsat 8 OLI لرسم خرائط المحاصيل في منطقة ماثورا في ولاية أوتار براديش بالهند. تم استخدام أفضل صورة خالية من السحابة (أقل من 5%) لمجموعات بيانات Landsat 8 OLI وSentinel 2 MSI ("2020-12-26"، و"2020-12-30") لتصنيف المحاصيل بمساعدة التصفية التلقائية، أي النسبة المئوية. الملكية السحابية على منصات GEE. علاوة على ذلك، يمكن تنظيم أداء منصة GEE والحصول عليها وتوضيحها وكذلك معالجتها المسبقة لمجموعة بيانات الأقمار الصناعية بقوة كبيرة. تم استخدام النقاط كمساحات مميزة مثل مجموعات بيانات التدريب. علاوة على ذلك، يتم استخدام مصفوفات الارتباك لتقييم الدقة (دقة المنتج والمستخدم) ومعامل كابا. بالإضافة إلى ذلك، قم بمقارنة نتائج مجموعة البيانات (Sentinel 2 MSI وLandsat 8 OLI) على أساس الدقة الإجمالية (OA) ودرجة F1 بالإضافة إلى معامل كابا. تم العثور على أعلى وصول حر باستخدام GTB (86.7%) يليه RF (82.5%)، CART (81.0%)، DT (78.1%) وSVM (66.5%) لصورة Landsat 8 OLI. بالنسبة لصورة Sentinel 2، حققت GTB أعلى وصول وصول بنسبة 84.2% يليها SVM (84%)، RF (82.3%)، DT (75.2%)، وCART (75.0%) على التوالي. على أساس البحث، وجد أن أداء GTB كان جيدًا بين جميع المصنفات في رسم خرائط المحاصيل باستخدام مجموعتي البيانات متعددة الأطياف.

Leveraging AI with Databricks and Azure Data Lake Storage

The integration of artificial intelligence (AI) with cloud-based analytics platforms has revolutionized data processing and decision-making. This research article explores the synergies between AI, Databricks, and Azure Data Lake Storage (ADLS), showcasing how organizations can harness AI capabilities to enhance data analytics workflows. Through a comprehensive analysis of real- world use cases, scalability assessments, performance optimizations, and cost efficiency evaluations, we demonstrate the transformative impact of AI-driven analytics on business outcomes.

The Implications of Artificial Intelligence in Pedodontics: A Scoping Review of Evidence-Based Literature.

Artificial intelligence (AI) has emerged as a revolutionary technology with several applications across different dental fields, including pedodontics. This systematic review has the objective to catalog and explore the various uses of artificial intelligence in pediatric dentistry. A thorough exploration of scientific databases was carried out to identify studies addressing the usage of AI in pediatric dentistry until December 2023 in the Embase, Scopus, PubMed, and Web of Science databases by two researchers, S.L.R. and A.L.G. From a pool of 1301 articles, only 64 met the predefined criteria and were considered for inclusion in this review. From the data retrieved, it was possible to provide a narrative discussion of the potential implications of AI in the specialized area of pediatric dentistry. The use of AI algorithms and machine learning techniques has shown promising results in several applications of daily dental pediatric practice, including the following: (1) assisting the diagnostic and recognizing processes of early signs of dental pathologies, (2) enhancing orthodontic diagnosis by automating cephalometric tracing and estimating growth and development, (3) assisting and educating children to develop appropriate behavior for dental hygiene. AI holds significant potential in transforming clinical practice, improving patient outcomes, and elevating the standards of care in pediatric patients. Future directions may involve developing cloud-based platforms for data integration and sharing, leveraging large datasets for improved predictive results, and expanding AI applications for the pediatric population.

Application of a cloud platform that identifies patient-ventilator asynchrony and enables continuous monitoring of mechanical ventilation in intensive care unit

BackgroundPatient-ventilator asynchrony (PVA) frequently occurs in mechanically ventilated patients within the ICU and has the potential for harm. Depending solely on the health care team cannot accurately and promptly identify PVA. To address this issue, our team has developed a cloud-based platform for monitoring mechanical ventilation (MV), comprising the PVA-RemoteMonitor system and the 24-h MV analysis report. We conducted a survey to evaluate physicians' satisfaction and acceptance of the platform in 14 ICUs. MethodsData from medical records, clinical information systems, and ventilators were uploaded to the cloud platform and underwent data processing. The data were analyzed to monitor PVA and displayed in the front-end. The 24-h analysis report for MV was generated for clinical reference. Critical care physicians in 14 hospitals' ICUs that involved in the platform participated in a questionnaire survey, among whom 10 physicians were interviewed to investigate physicians’ acceptance and opinions of this system. ResultsThe PVA-RemoteMonitor system exhibited a high level of specificity in detecting flow insufficiency, premature cycle, delayed cycle, reverse trigger, auto trigger, and overshoot, with sensitivities of 90.31 %, 98.76 %, 99.75 %, 99.97 %, 100 %, and 99.69 %, respectively. The 24-h analysis report supplied essential data about PVA and respiratory mechanics. 86.2 % (75/87) of physicians supported the application of this platform. ConclusionsThe PVA-RemoteMonitor system accurately identified PVA, and the MV analysis report provided guidance in controlling PVA. Our platform can effectively assist ICU physicians in the management of ventilated patients.

Smart Home and Thief Detection using AI

In our world increasingly interconnected, demand for smart home solutions that prioritizing security a convenient ever-growing. This abstract presents an inventive approach to home security an automation through integration of ESP32-CAM, ultrasonic sensor, a Firebase technology. Our system offering real-time theft detecting a remote controlling of home appliances via mobile app. By leveraging AI-drive theft detection a Firebase's cloud-based platform, users can monitor a managing their homes from anywhere, enhancing both security a convenient. This abstract outline component, workflow, features, a potential future enhancement of our Smart Home and Theft Detect system, offering a glimpse into the future of home automation.

A Cloud-Based WEB Platform for Fall Risk Assessment Using a Therapist-Centered User Interface Which Enables Patients’ Tracking Remotely

A Cloud-Based WEB Platform for Fall Risk Assessment Using a Therapist-Centered User Interface Which Enables Patients’ Tracking Remotely

Valuing Modern Technologies to Boost the Circular Economy in the Fashion Industry

Abstract In the context of solving pollution problems, the circular economy appears to be a mandatory practice because it emphasises the principle of reuse - repair - reconditioning - recycle. In this context, the paper aims to analyse how modern technologies facilitate the implementation of circular economy principles through better traceability of goods produced and used in the fashion industry. In this way, our research is exploratory and the information on which it is based was gathered using the Google search tool, which identified brands who use Big Data, RFID and QR code tools, as well as online tools (cloud-based platforms) that offer integrated garment tracking services. Following detailed analysis of EU data, it was found that major companies have integrated QR Code technology into their products and cloud-based platforms are increasingly used.

Evaluation of Acute Myeloid Leukemia Genomes using Genomic Proximity Mapping.

Cytogenetic analysis encompasses a suite of standard-of-care diagnostic testing methods that is routinely applied in cases of acute myeloid leukemia (AML) to assess chromosomal changes that are clinically relevant for risk classification and treatment decisions. In this study, we assess the use of Genomic Proximity Mapping (GPM) for cytogenomic analysis of AML diagnostic specimens for detection of cytogenetic risk variants included in the European Leukemia Network (ELN) risk stratification guidelines. Archival patient samples (N=48) from the Fred Hutchinson Cancer Center leukemia bank with historical clinical cytogenetic data were processed for GPM and analyzed with the CytoTerra® cloud-based analysis platform. GPM showed 100% concordance for all specific variants that have associated impacts on risk stratification as defined by ELN 2022 criteria, and a 72% concordance rate when considering all variants reported by the FH cytogenetic lab. GPM identified 39 additional variants, including variants of known clinical impact, not observed by cytogenetics. GPM is an effective solution for the evaluation of known AML-associated risk variants and a source for biomarker discovery.

Using Cloud Healthcare to Improve Home Learning Efficacy in Congenital Heart Disease Infants

During the COVID-19 pandemic, visitation restrictions in line with infection control policies curtailed opportunities for family members to learn essential caregiving skills in the intensive care unit. This limitation decreased satisfaction among family members, possibly indicating their increased difficulties in care due to the lack of face-to-face guidance. Thus, increasing family member understanding of and ability to apply learning content without direct interaction presents a significant and urgent challenge. Moreover, because of lack of caregiving confidence, some family members may be reluctant to facilitate the discharge of critically ill patients, causing delays in discharge planning. These challenges underscore the obstacles faced by nursing health education during the pandemic. This study was designed to utilize cloud technology to enhance the knowledge and skills of families caring for infants with congenital heart disease at home and to assess their satisfaction with the associated homecare learning platform. Based on our hospital's cloud-based health education platform, a series of personalized instructional video materials was developed for families of infants with congenital heart disease. These materials cover comprehensively the entire treatment process, from diagnosis to post-discharge home care skills, for these patients. To facilitate autonomous learning, the videos in this series were made accessible to the families anytime, anywhere via personal devices such as smartphones and tablets. Concurrently, a chatbot tool was integrated to provide guidance on inpatient care for infants with congenital heart disease, including fundamental aspects of newborn care, with the aim of equipping parents and caregivers with the knowledge and skills necessary to provide basic post-discharge care. To ensure the families acquired personalized care skills, after completing the learning modules, practical bedside training sessions incorporating knowledge and skills assessments were organized for family members. After project implementation, the average knowledge score for family members increased significantly from 79.1 to 100 (perfect score). The proportion of family members proficient in executing caregiving techniques autonomously also rose impressively from 30% to 95%. Furthermore, average overall satisfaction with cloud-based technology-assisted caregiving learning among the family members rose 31.4% from 3.5 to 4.6. This project represents a viable solution to providing clinical nursing guidance independent of the constraints of time and location, and effectively enhances homecare-skill-related learning outcomes in family members, especially with regard to caring for infants with congenital heart disease.

Integration of a machine learning model-generated surgical risk score into the EHR and preoperative workflow.

11163 Background: We previously developed a machine learning model (MLM) that accurately predicts postoperative complications (POC) for cancer inpatients. As laborious manual entry of patient data is a barrier for risk calculator use, and risk constantly changes depending on clinical status, we sought to integrate our MLM into the electronic health record (EHR) and preoperative workflow as a readily available surgical risk score (SRS) that is generated using real-time data. Here, we report on the integration process and survey results regarding its functionality and impact on patient care. Methods: A MLM that predicts severe POC (CD Grade ≥3) for cancer inpatients undergoing same-hospitalization operations was previously developed using EHR data. To integrate our MLM into the Epic EHR, we developed a real-time infrastructure (RTI) using the cloud-based platform Azure. EHR data is inputted to RTI and processed in real-time to generate model outputs. These outputs are then sent back to the EHR through the Epic Cognitive Computing Platform to generate each patient’s specific SRS. The SRS is dichotomized as “high” or “low” risk based upon a previously determined threshold and is displayed as a “Best Practice Advisory (BPA)” when a surgical case request is entered. In addition to the automated BPA, the SRS is sent to the primary surgeon as an InBasket Case Message. To elicit feedback of our EHR-integrated SRS, a survey was distributed to surgical providers at our institution. Results: After integration of our MLM into the EHR preoperative workflow, 185 surgeries were completed between November 16, 2021 to July 31, 2023. The 30-day severe POC rate was 27.6%, and our MLM had precision of 44.7% and recall of 74.5%, resulting in AUROC of 0.78 and AUPRC of 0.53. 35 surgical providers (40% primary surgeons, 34% surgical fellows, 26% advanced practice providers) from the divisions of surgical oncology, colorectal surgery, and gynecologic oncology completed our survey in July 2022 and July 2023. 28 (80%) of providers stated that they have interacted with the SRS through either BPA or InBasket Case Messages. Of these providers, 21 (75%) strongly agreed or agreed that the SRS was visible at appropriate times, and 18 (64%) strongly agreed or agreed that the SRS contains relevant information to assess surgical risk. 8 (29%) strongly agreed or agreed that the SRS was used to facilitate preoperative discussion. The 2023 survey additionally asked whether the SRS impacted the decision to operate; among providers who had interacted with the SRS, 4 (36%) strongly agreed or agreed with this statement. Conclusions: Integration of a real-time, MLM-generated SRS into the EHR and preoperative workflow can be successfully performed. While our integrated SRS is visible to surgeons and contains risk assessment felt relevant to the operation, the impact of the SRS on clinical care and decision making remains unclear.

Arduino Based IOT Smart Parking System

Abstract: The idea of "smart cities" was very popular in the beginning. This paper presents an in-depth exploration of an Arduino-based Internet of Things (IoT) Smart Parking System designed to address the growing challenges of urban parking management. Leveraging the capabilities of Arduino microcontrollers and IoT technology, the proposed system offers an intelligent and efficient solution for optimizing parking space utilization. The key components of the system consist of sensor nodes that are positioned thoughtfully in parking spots and send occupancy data in real time to a centralized Arduino microcontroller. After processing this data, the microcontroller sends status updates to a cloud-based platform via wireless communication protocols. The cloud platform is interfaced with an intuitive mobile application that gives drivers instant access to information about parking availability and promotes a smooth parking experience.

Novel Approach for Vehicle Accident Tracking using IoT

Abstract: In recent years, there has been significant interestin integrating Internet of Things (IoT) technology into vehicle safety systems to enhance emergency response capabilities and road safety. This paper introduces a comprehensive Vehicle Accident Tracking System (VATS) that utilizes IoT principles. The proposed system incorporates various sensors and communication devices installed within vehicles to monitor theirstatus and detect potential accidents in real-time. When anaccident is detected, the system automatically triggers an alert mechanism, notifying relevant stakeholders such as emergency services and fleet managers. Key components of the VATSinclude accelerometers, GPS modules, and onboard communication units, which work together to provide precise accident geospatial data and vehicle condition data. The systemutilizes cloud-based platforms for data storage and processing, ensuring seamless integration with existing infrastructure and scalability. By continuously monitoring and analyzing vehicle telemetry data, the VATS facilitates proactive accident prevention measures and post-accident analysis. Additionally, it offers benefits such as reduced emergency response times, improved accident documentation for insurance claims, and enhanced overall road safety. Privacy and security concerns areaddressed through encryption protocols and access control mechanisms to protect sensitive information. Overall, the Vehicle Accident Tracking System presented in this paper represents a significant advancement in leveraging IoT technology to mitigate the impact of accidents on roadways, thereby contributing to a safer and more efficient transportation ecosystem.

Impact of a Mobile, Cloud-Based Care-Coordination Platform on Door-to-Balloon Time in Patients With STEMI: Initial Results

Care-coordination platforms may optimize ST-elevation myocardial infarction (STEMI) treatment delays. This study aimed to assess the impact of Stenoa use on treatment delays in STEMI patients. We conducted a retrospective cohort study on local STEMI cases for the period between September 2020 and March 2023, comparing the times from first medical contact to device, before vs after the implementation of the Stenoa platform by the catheterization laboratory (cath-lab) and emergency department. A total of 180 patients were included. Significant reductions were found in times from first medical contact to electrocardiogram, from electrocardiogram to cath-lab activation, and from cath-lab activation to cath-lab arrival (P = 0.02, P = 0.04, and P = 0.02, respectively), after the platform was implemented. These findings suggest that use of Stenoa reduces STEMI door-to-balloon–time components.

Containerized Microservices for Mobile Applications Deployed on Cloud Systems

This study explores the transformative role of containerized microservices in the sphere of mobile application development, especially within public cloud ecosystems. It focuses on how technologies such as Docker and Kubernetes contribute to improving deployment, scalability, and overall management of mobile applications, with an emphasis on containerizing backend services. We analyze their efficiency in streamlining deployment processes, focusing on how they improve the application’s performance and reliability. Additionally, we examine various alternative deployment strategies, such as blue-green, rolling, and canary releases, to emphasize their effectiveness in minimizing risks and facilitating smooth transitions in dynamic cloud environments. The study takes a comprehensive approach to achieve this goal, which includes a systematic review of existing literature, a thorough examination of relevant use cases, and an assessment of open-source technologies. Our findings reveal not only the practical benefits of these strategies but also their strategic application, offering important insights for software engineers and decision-makers. This study emphasizes the significance of integrating and optimizing containerized microservices in mobile app development to achieve more efficient, scalable, and manageable application lifecycles on cloud-based platforms.

Novel AI-based tool for primary tooth segmentation on CBCT using convolutional neural networks: A validation study.

Primary teeth segmentation on cone beam computed tomography (CBCT) scans is essential for paediatric treatment planning. Conventional methods, however, are time-consuming and necessitate advanced expertise. The aim of this study was to validate an artificial intelligence (AI) cloud-based platform for automated segmentation (AS) of primary teeth on CBCT. Its accuracy, time efficiency, and consistency were compared with manual segmentation (MS). A dataset comprising 402 primary teeth (37 CBCT scans) was retrospectively retrieved from two CBCT devices. Primary teeth were manually segmented using a cloud-based platform representing the ground truth, whereas AS was performed on the same platform. To assess the AI tool's performance, voxel- and surface-based metrics were employed to compare MS and AS methods. Additionally, segmentation time was recorded for each method, and intra-class correlation coefficient (ICC) assessed consistency between them. AS revealed high performance in segmenting primary teeth with high accuracy (98 ± 1%) and dice similarity coefficient (DSC; 95 ± 2%). Moreover, it was 35 times faster than the manual approach with an average time of 24 s. Both MS and AS demonstrated excellent consistency (ICC = 0.99 and 1, respectively). The platform demonstrated expert-level accuracy, and time-efficient and consistent segmentation of primary teeth on CBCT scans, serving treatment planning in children.

Smart Glasses for Visually Impaired People Using IoT

The visually handicapped have a difficult time getting by in a world that is mostly intended for sighted people. Even seemingly simple things like locating obstacles, traversing new places, and keeping spatial awareness can be difficult for them and frequently impede their freedom and movement. Their sense of dependence and loneliness are exacerbated by the absence of real-time support and systems catered to their individual needs, which exacerbates these challenges. Smart glasses are made especially for those who are blind or visually impaired to address these issues. These smart glasses use technologies to improve the wearer's movement and independence while offering real-time help. It seeks to remove the obstacles that visually impaired people encounter when attempting to safely and successfully navigate their environment by utilizing modern sensor technology and creative IoT solutions. [3] Its primary goal is to create a wearable assistive technology with features including proximity alarms, object detection, distance measuring, and location mapping. It seeks to provide a complete and user-friendly system that caters to the unique needs of visually impaired people by integrating a variety of sensors, communication modules, and cloud-based platforms. The goal of the glasses is to let people to live more independently and confidently by removing the limits imposed by visual impairment, which will ultimately improve their overall quality of life.

AnVILWorkflow: A runnable workflow package for Cloud-implemented bioinformatics analysis pipelines.

Advancements in sequencing technologies and the development of new data collection methods produce large volumes of biological data. The Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) provides a cloud-based platform for democratizing access to large-scale genomics data and analysis tools. However, utilizing the full capabilities of AnVIL can be challenging for researchers without extensive bioinformatics expertise, especially for executing complex workflows. Here we present the AnVILWorkflow R package, which enables the convenient execution of bioinformatics workflows hosted on AnVIL directly from an R environment. AnVILWorkflowsimplifies the setup of the cloud computing environment, input data formatting, workflow submission, and retrieval of results through intuitive functions. We demonstrate the utility of AnVILWorkflowfor three use cases: bulk RNA-seq analysis with Salmon, metagenomics analysis with bioBakery, and digital pathology image processing with PathML. The key features of AnVILWorkflow include user-friendly browsing of available data and workflows, seamless integration of R and non-R tools within a reproducible analysis pipeline, and accessibility to scalable computing resources without direct management overhead. While some limitations exist around workflow customization, AnVILWorkflowlowers the barrier to taking advantage of AnVIL's resources, especially for exploratory analyses or bulk processing with established workflows. This empowers a broader community of researchers to leverage the latest genomics tools and datasets using familiar R syntax. This package is distributed through the Bioconductor project (https://bioconductor.org/packages/AnVILWorkflow), and the source code is available through GitHub (https://github.com/shbrief/AnVILWorkflow).

Deep convolutional neural network based synthetic minority over sampling technique: a forfending model for fraudulent credit card transactions in financial institution

Fraudulent credit card transactions are committed by unauthorized individuals and organizations employing methods such as phishing and social engineering fraud tactics. Researchers propose several Machine Learning (ML) techniques to deter the challenges of credit card fraud. However, the ML approaches are endorsed with some challenges, which makes the detection of credit card fraud extremely difficult. This study proposes a Deep Convolutional Neural Network (DCNN) with Synthetic Minority Oversampling Techniques (SMOTE) as an ideal solution. Kaggle datasets with 284,807 records and 31 features were exploited. Implementation was performed on the Google Colab cloud-based platform, embedding a Jupyter notebook setting with Graphical Processing Units (GPUs). Two experiments were conducted; the first was probed to determine suitable models among baseline models: Logistic Regression (LR), Random Forest (RF), Isolation Forest, and a single Deep Learning (DL) model of Multiple Layer Perceptron (MLP). The baseline models yielded an overfitting accuracy score, with recall, specificity, precision, and F1-score all presenting 1.00% respectively. This outcome is not sufficient in establishing findings on imbalanced data distribution as it's biased. This led to the construction of a new ML model incorporating Light Gradient Boosting Machine (LGBM), with Artificial Neural Network (ANN) and the proposed DCNN+SMOTE for the second experimental phase alongside baseline models. Experimental results via simulation show the proposed DCNN+SMOTE yielded awesome superclass performance across the board, displaying 1.00% results respectively. Its Error Rate (ER) and Null Error Rate (NER) are 0.00% distinctly. Meanwhile, the False Positive Rate (FPR) yields a 0.001% result, lesser and better than the baseline models.

A teleradiology network for the improvement of healthcare and patient management in the developing countries of the African continent

IntroductionAfrica is the second-largest continent on Earth in terms of both size and population. However, inaccessibility and shortfall of trained radiologists impede the delivery of adequate healthcare to such a large population. Teleradiology holds considerable potential in improving patient outcomes and healthcare delivery in African nations by furnishing timely interpretation of radiological examinations, particularly in those areas where there is a particular scarcity of radiologists. The aim of the present study was to assess the impact of teleradiology in the improvement of healthcare and patient management in the developing countries of the African continent. MethodsIn this retrospective study, from January 2017 and December 2022, the scans of a cohort of patients from eight African countries were uploaded to the teleradiology cloud server and interpreted by board certified radiologists empanelled by a teleradiology service provider. ResultsThe telehealth model proposed in the study was seen to provide timely and quality reporting of 58,223 scans of 39,513 patients with a mean turn-around-time (TAT) of 2.46 h 95 % CI (2.44–2.48). DiscussionA dedicated teleradiology model designed in this study allowed the interpretation and analysis of the scans of the cohort of patients from hospitals in African countries by teleradiologists via high quality DICOM-image transfer over a cloud-based platform. The outcomes of our investigation reflect that teleradiology provides an effective solution for early diagnosis/interpretation of examinations performed in Africa. Further, the currently proposed teleradiology model may be used for other developing countries across the world to improve quality of care.