Advancing Progressive Web Applications to Leverage Medical Imaging for Visualization of Digital Imaging and Communications in Medicine and Multiplanar Reconstruction: Software Development and Validation Study
Notice bibliographique
Résumé
BACKGROUND: In medical imaging, 3D visualization is vital for displaying volumetric organs, enhancing diagnosis and analysis. Multiplanar reconstruction (MPR) improves visual and diagnostic capabilities by transforming 2D images from computed tomography (CT) and magnetic resonance imaging into 3D representations. Web-based Digital Imaging and Communications in Medicine (DICOM) viewers integrated into picture archiving and communication systems facilitate access to pictures and interaction with remote data. However, the adoption of progressive web applications (PWAs) for web-based DICOM and MPR visualization remains limited. This paper addresses this gap by leveraging PWAs for their offline access and enhanced performance. OBJECTIVE: This study aims to evaluate the integration of DICOM and MPR visualization into the web using PWAs, addressing challenges related to cross-platform compatibility, integration capabilities, and high-resolution image reconstruction for medical image visualization. METHODS: Our paper introduces a PWA that uses a modular design for enhancing DICOM and MPR visualization in web-based medical imaging. By integrating React.js and Cornerstone.js, the application offers seamless DICOM image processing, ensures cross-browser compatibility, and delivers a responsive user experience across multiple devices. It uses advanced interpolation techniques to make volume reconstructions more accurate. This makes MPR analysis and visualization better in a web environment, thus promising a substantial advance in medical imaging analysis. RESULTS: In our approach, the performance of DICOM- and MPR-based PWAs for medical image visualization and reconstruction was evaluated through comprehensive experiments. The application excelled in terms of loading time and volume reconstruction, particularly in Google Chrome, whereas Firefox showed superior performance in viewing slices. This study uses a dataset comprising 22 CT scans of peripheral artery patients to demonstrate the application's robust performance, with Google Chrome outperforming other browsers in both the local area network and wide area network settings. In addition, the application's accuracy in MPR reconstructions was validated with an error margin of <0.05 mm and outperformed the state-of-the-art methods by 84% to 98% in loading and volume rendering time. CONCLUSIONS: This paper highlights advancements in DICOM and MPR visualization using PWAs, addressing the gaps in web-based medical imaging. By exploiting PWA features such as offline access and improved performance, we have significantly advanced medical imaging technology, focusing on cross-platform compatibility, integration efficiency, and speed. Our application outperforms existing platforms for handling complex MPR analyses and accurate analysis of medical imaging as validated through peripheral artery CT imaging.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».