Cloud-based Platform Research Articles

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.

A comparison of global flood models using Sentinel-1 and a change detection approach

Advances in numerical algorithms, improvement of computational power and progress in remote sensing have led to the development of global flood models (GFMs), which promise to be a useful tool for large-scale flood risk management. However, performance and reliability of GFMs, especially in data-scarce regions, is still uncertain, as they are difficult to validate. Here we aim at contributing to develop alternative, more flexible, and consistent methods for GFM validation by applying a change detection analysis on synthetic aperture radar (CD-SAR) imagery obtained from the Sentinel-1 imagery, on a cloud-based geospatial analysis platform. The study addresses two main objectives. First, to validate four widely adopted GFMs with flood maps generated through the proposed CD-SAR approach. This exercise was conducted for eight different large river basins on four continents, to account for a diverse range of hydro-climatic environments. Second, to compare CD-SAR-derived flood maps with those obtained from alternative remote sensing sources. These comparative results offer valuable insights into the reliability of CD-SAR data as a validation tool, more specifically how it stacks up against flood maps generated by other remote sensing techniques.

Phenomena observed in electron EBRT using a pulse-by-pulse radioluminescence dosimetry system with cloud-based analytics

The pulse-by-pulse characteristics of electron beams from a clinical linear accelerator (linac) at high dose rates are crucial for ensuring precise dose delivery in radiation therapy. In this research, we employ a radioluminescence scintillator made from Ge-doped silica optical fiber to analyse electron beams at standard therapy dose rates 600 cGy/min with energy of 15 MeV. The scintillator is combined with a photomultiplier tube (PMT) and a photon-counting setup to achieve microsecond (μs) gating times, providing high temporal resolution. For sub-microsecond (sub-μs) temporal resolution, we utilize a multi-pixel photon counter and a rapid digital oscilloscope. Our study examines the peak time and the time it takes for pulses to return to baseline, uncovering the effects of afterglow at elevated dose rates and its potential implications for dosimetry precision. Our results indicate that monitor chambers, which have lower temporal resolution than the radioluminescence system outlined in this study, may struggle to accurately track high-rate dose delivery, emphasizing the necessity for advanced monitoring methods in FLASH radiotherapy. The dosimetric data was further analysed for deeper analytics on a cloud-based system. The dosimetric data underwent advanced analysis on a cloud-based platform, employing a prototype analytics software system comprised of Dosi-Client and Dosi-Cloud Backend components for real-time data acquisition and processing. This innovative approach revealed significant insights into radiation dosimetry, including the detection of anomalies through a reconstruction convolutional autoencoder, enhancing the precision and reliability of dosimetric measurements.

Smart Cattle Care: An IOT Based Monitoring and Management System

The "Smart Cattle Care" system, which utilizes Internet of Things (IoT) technology to alter the way cattle are monitored and cared for in agricultural settings, marks a ground-breaking innovation in livestock management. This ground-breaking system combines a network of specialized sensors, wearables, and communication modules to gather and transmit real-time data on important aspects like location, environmental conditions, and health measurements. After that, using cloud-based platforms and cutting-edge analytics and artificial intelligence algorithms, the data is processed and examined. This all-encompassing strategy equips farmers with timely knowledge about the health and behavior of their cattle, allowing for proactive disease detection, improved breeding management, and increased overall productivity. The technology also allows for automatic regulation of feeding and climate, ensuring the provision of ideal circumstances for living. Smart Cattle Care greatly improves operational efficiency, lowers costs, and ultimately promotes enhanced animal care and agricultural sustainability by providing a user-friendly dashboard interface and rapid notifications. This study provides a thorough description of the Smart Cattle Care system, emphasizing its essential features, advantages, and prospective effects on contemporary livestock management techniques.

Early Detection System for Gas Leakage and Fire in Smart Home Using IOT

Gas leakage is an important environmental, commercial and residential problem that threatens human health, safety and the environment. Traditional gas detection methods often lack the ability to track time and do not provide timely information, leading to accidents and damage. In this context, Internet of Things (IoT) technology has been found to provide a reliable solution for continuous monitoring and remote management of gas detection systems. This paper presents an IoT-based gas leak detection system to improve safety and reduce the risk of gas leakage. leak. The proposed system integrates gas sensors, microcontrollers, communication modules and cloud computing infrastructure to enable real-time monitoring, data analysis and remote monitoring. Gas detectors used in designated areas indicated the presence of hazardous gases, and data was collected directly from the 's central processing unit for analysis. The system provides rapid rescue and intervention by sending instant notifications via mobile applications in case of a gas leak. The main features of the proposed system are scalability, flexibility and interoperability, allowing integration with existing infrastructure and integration of different protocols. Additionally, the use of a cloud-based platform facilitates data storage, analysis and visualization, allowing partners to gain insight intogasleaks, processes and possible consequences

A Comparison of Pixel Based and Object Based Image Classification for Cropland Area Estimation

Pixel-based (PB) and object-based (OB) Land Use–Land Cover (LULC) classification techniques can be applied in Google Earth Engine (GEE), a flexible cloud-based platform, because of its numerous state-of-the-art functions that comprise several Machine Learning (ML) methods. Adding some texture measure, any measure to a classification usually improves the accuracy. Object segmentation and object textural analysis are two OB methods that are still uncommon in the GEE environment. Object based image classification is difficult because it can be challenging to concatenate the correct functions and adjust various parameters in order to get past the computational limitations of GEE. The goal of this work is to develop and test an OB classification approach that combines the ML algorithm Random Forest (RF) to perform the final classification, the Gray-Level Co-occurrence Matrix (GLCM) to calculate cluster textural indices and the Simple Non-Iterative Clustering (SNIC) algorithm to identify spatial clusters. The primary seven GLCM indices are subjected to a Principal Components Analysis (PCA) in order to combine the textural data needed for the OB classification into a single band. The proposed methodology was broadly tested in a 304 km2 study area, located in the Telangana state (India), using Sentinel 2 (S2).

Dummy run for planning of isotoxic dose-escalated radiation therapy for glioblastoma used in the PRIDE trial (NOA-28; ARO-2024-01; AG-NRO-06)

BackgroundThe PRIDE trial (NOA-28; ARO-2024-01; AG-NRO-06; NCT05871021) is designed to determine whether a dose escalation with 75.0 Gy in 30 fractions can enhance the median overall survival (OS) in patients with methylguanine methyltransferase (MGMT) promotor unmethylated glioblastoma compared to historical median OS rates, while being isotoxic to historical cohorts through the addition of concurrent bevacizumab (BEV). To ensure protocol-compliant irradiation planning with all study centers, a dummy run was planned and the plan quality was evaluated. MethodsA suitable patient case was selected and the computed tomography (CT), magnetic resonance imaging (MRI) and O-(2-[18F]fluoroethyl)-L-tyrosine (FET) positron emission tomography (PET) contours were made available. Participants at the various intended study sites performed radiation planning according to the PRIDE clinical trial protocol. The treatment plans and dose grids were uploaded as Digital Imaging and Communications in Medicine (DICOM) files to a cloud-based platform. Plan quality and protocol adherence were analyzed using a standardized checklist, scorecards and indices such as Dice Score (DSC) and Hausdorff Distance (HD). ResultsMedian DSC was 0.89, 0.90, 0.88 for PTV60, PTV60ex (planning target volume receiving 60.0 Gy for the standard and the experimental plan, respectively) and PTV75 (PTV receiving 75.0 Gy in the experimental plan), respectively. Median HD values were 17.0 mm, 13.9 mm and 12.1 mm, respectively. These differences were also evident in the volumes: The PTV60 had a volume range of 219.1–391.3 cc (median: 261.9 cc) for the standard plans, while the PTV75 volumes for the experimental plans ranged from 71.5–142.7 cc (median: 92.3 cc). The structures with the largest deviations in Dice score were the pituitary gland (median 0.37, range 0.00–0.69) and the right lacrimal gland (median 0.59, range 0.42–0.78). ConclusionsThe deviations revealed the necessity of systematic trainings with appropriate feedback before the start of clinical trials in radiation oncology and the constant monitoring of protocol compliance throw-out the study. Trial registrationNCT05871021

Abstract PO4-16-12: Patient initiated centricity towards improved communication, side effect management and follow-up care by Real World Evidence Survey from patients for patients – Preview of the Breast BRIDGister Platform

Abstract Background Treatment adherence is crucial for outcome improvement. Still up to 50% of patients interrupt e.g. endocrine treatment or other effective treatment choices due to side effects or miscommunication. Reasons may include limited time spent for patient information in daily routine and the intrinsic barriers to frankly tell the truth to the attending physicians. Therefore, a patient initiated survey has been compiled to comprehensively address all clinical, diagnostic and critical aspects of importance for patients by decentral data capture from patients at home via smart devices in a pseudonymized & interactive fashion to refocus on challenges regarding patients needs, fears and wishes to support evidence based statistical analysis of Real World Data. Materials and Methods Establishing a cloud-based IT Platform enabling nationwide, decentral data entry using Climedo clinical trial software tools without the need of any commercial app utilities and therefore obeying GDPR conform standards to create an interconnected online platform integrating 36 interconnected surveys comprising >1.200 singular questions has been set up between 2020 and 2022 based on patients content elaboration & wishes. The questionnaires address all Real World experiences e.g. at initial diagnosis, during primary treatment and all subsequent lines as well as side effect management with complementary medicine and follow up care realities. After 100 patients a preplanned statistical analysis has been performed to evaluate the feasibility of the patient oriented ePRO approach and demonstrate relevance of proof-of-concept results before entering international trial expansion. Descriptive analysis by Excel as well as Correlation analysis; Chi square and Partitioning tests using the SAS JMP® 9.0.0 software were performed. Results Despite in depth complexity > 85% of patients completed the whole survey. Age distribution of the pilot cohort revealed comparably younger patient age (28% between 30-39 years, 40% between 40-49 years, 24% between 50-59 and 10% being above 60 years) and dominance of Triple Negative Patients (~39%) participating the online survey in the pilot cohort. 97% of patients were treated in certified breast centers (50% in larger centers of bigger towns,15% in central clinics, 23% in university sites, 5% in local hospitals and 7% at office based doctors). Interestingly, 25% of patients did not or only partially understand their diagnosis and upcoming treatment. Time spent for communicating initial breast cancer diagnosis was less than 10 minutes for 25% of patients, while 13% did not remember. Exclusively one treatment option was discussed with 86% of patients. 78% have been informed about their initial breast cancer diagnosis in personal meetings, while 21% were informed by phone calls. 37% of patients had been informed about their patient rights. At preplanned interim analysis 38% of patients were lymphnode positive and 6% metastasized at initial diagnosis. 56% of patients were satisfied with their guideline conform follow-up-care, while 42% did pay for additional follow up care on their own. 85% of patients wished to have a risk-adopted, more individualized follow-up care. Conclusion Despite complexity and lengthiness > 85% of patients did complete the survey. There is an apparent need to have more time for initial confrontation with diagnosis and treatment options. Patients are frequently not informed about alternative treatment options as well as patient rights. Patients frequently feel under pressure in their life threatening situation and suffer substantial lack of information as well as appropriate personal communication. Patient centricity should focus on elaborated communication and information systems that support treatment decision acceptance and adherence. Further details analysis will become available in upcoming subanalysis of the Breast BRIDGister platform survey. Citation Format: Cornelia Stuewe, Ralph Wirtz, Christa Gress, Lena Schmidt, Veronika Schweighart, Sebastian Eidt, Claudia Schumacher, Julia Habraschka, Heidi Lehmann, Katrin Schuere. Patient initiated centricity towards improved communication, side effect management and follow-up care by Real World Evidence Survey from patients for patients – Preview of the Breast BRIDGister Platform [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-16-12.