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ORIGINAL RESEARCH article

Front. Radiol.
Sec. Artificial Intelligence in Radiology
Volume 4 - 2024 | doi: 10.3389/fradi.2024.1460889

SenseCare: A Research Platform for Medical Image Informatics and Interactive 3D Visualization

Provisionally accepted
Guotai Wang Guotai Wang 1*Qi Duan Qi Duan 2Tian Shen Tian Shen 2Shaoting Zhang Shaoting Zhang 2
  • 1 University of Electronic Science and Technology of China, Chengdu, China
  • 2 Sense Care Reserach, Shanghai, China

The final, formatted version of the article will be published soon.

    Introduction: Clinical research on smart health has an increasing demand for intelligent and clinic-oriented medical image computing algorithms and platforms that support various applications. However, existing research platforms for medical image informatics have limited support for Artificial Intelligence (AI) algorithms and clinical applications. Methods: To this end, we have developed SenseCare research platform, which is designed to facilitate translational research on intelligent diagnosis and treatment planning in various clinical scenarios. It has several appealing functions and features such as advanced 3D visualization, concurrent and efficient web-based access, fast data synchronization and high data security, multi-center deployment, support for collaborative research, etc. Results and Discussion: SenseCare provides a range of AI toolkits for different tasks, including image segmentation, registration, lesion and landmark detection from various image modalities ranging from radiology to pathology. It also facilitates the data annotation and model training processes, which makes it easier for clinical researchers to develop and deploy customized AI models. In addition, it is clinic-oriented and supports various clinical applications such as diagnosis and surgical planning for lung cancer, liver tumor, coronary artery disease, etc. By simplifying AI-based medical image analysis, SenseCare has a potential to promote clinical research in a wide range of disease diagnosis and treatment applications.

    Keywords: artificial intelligence, medical image informatics, clinical research platform, data annotation, Model Training

    Received: 07 Jul 2024; Accepted: 06 Nov 2024.

    Copyright: © 2024 Wang, Duan, Shen and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Guotai Wang, University of Electronic Science and Technology of China, Chengdu, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.