RAIVEN: A novel framework for translation of AI tools to the radiology environment
Notice bibliographique
Résumé
1183 Introduction: Artificial intelligence (AI) is finding an increasing number of applications in nuclear medicine and radiology. Deep learning models have been used to improve image quality, automate image segmentation, and aid in disease classification. Models that combine learned features, radiomic features, and clinical information can be used to predict disease progression, treatment outcome and survival. However, translation of such models/algorithms into clinical trials or standard of care applications is often nontrivial due to the lack of an appropriate framework for model deployment. The aim of this project is to develop such a framework that satisfies the following design specifications: 1) Allows easy combination of different algorithms to create pipelines with multistep workflows, 2) Works with existing workstations available to physicians/researchers, 3) Allows the deployment of models developed in any programming language without the need for cumbersome and time-consuming configuration, 4) Is easy to deploy and use. Our framework, RAIVEN (Radiology AI Virtual ENvironment), represents our vision of a new radiology environment that incorporates AI tools to better treat and diagnose disease. Methods: The application encompasses a central API service and five auxiliary modules, including a database, worker daemons, a messaging queue, a frontend, and a DICOM service. The API, developed using the asynchronous Python framework FastAPI, is the main component of the application. It controls all communication between the five auxiliary components. The DICOM service, implemented using the Python package pynetdicom, governs both the input and output of DICOM images. RAIVEN conforms to all DICOM networking standards; it can be integrated seamlessly with all DICOM enabled software and equipment present in nuclear medicine and radiology departments. Our framework allows users to build processing pipelines using a visual interface by connecting different algorithms developed by researchers. These algorithms are added to RAIVEN as Docker containers. With Docker as the underlying mechanism for our application, tools are easy to update, language agnostic, and maintained in separate virtual environments. The client-side, developed in VueJs, provides an accessible user interface running on all modern web browsers. Conforming to material design principles, the interface delivers a simple, yet informative user experience. The web application is also mobile-friendly, allowing users to easily create, edit, and run pipelines from a variety of devices. Results: We present a framework to enable imaging pipeline creation. Users can upload containerized algorithms, use drag and drop to connect containers, move received DICOM images through pipelines, and download/export resulting DICOM images or other generated filetypes. Our application differs from existing solutions published by providing a unique interface for users to visually connect tools to build medical image processing pipelines. We have deployed the application as a webservice hosted internally at our institution. RAIVEN has been tested with image processing workflows including algorithms for image anonymization, and simple image processing algorithms such as down sampling and pixel manipulation of PET/CT and SPECT/CT images. Researchers can easily add more complex image processing methods. The source code of the application is publicly available at github.com/qurit/raiven. Conclusions: RAIVEN is an open-source framework that aims to facilitate translation of AI research tools into the clinical environment. The ease of constructing a pipeline encourages users to create many workflows to test their developed algorithms, facilitating new discoveries and quicker diagnosis. We envision RAIVEN, given its various aforementioned capabilities, to speed up the clinical translation of image processing algorithms at our functional imaging center and at other centres around the world.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,004 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,007 | 0,011 |
| Intégrité de la recherche | 0,002 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,020 | 0,010 |
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 source (Gemma direct ou Codex distillé), 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 ».