Discovery Viewer (DV): Web-Based Medical AI Model Development Platform and Deployment Hub.
Discovery Viewer (DV): Web-Based Medical AI Model Development Platform and Deployment Hub.
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DOI：
10.3390/bioengineering10121396
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发表时间：
2023-12-06
影响因子：
4.6
通讯作者：
Wu, Yunfeng
中科院分区：
文献类型：
--
作者：
Fauveau, Valentin;Sun, Sean;Liu, Zelong;Mei, Xueyan;Grant, James;Sullivan, Mikey;Greenspan, Hayit;Feng, Li;Fayad, Zahi A.;Wu, Yunfeng
过去几年，人工智能（AI）在医学领域的迅速崛起凸显了开发更大、更好的数据和模型共享系统的重要性。然而，医疗数据中受保护的健康信息（PHI）的存在对共享提出了挑战。降低PHI违规风险的一个潜在解决方案是专门共享使用私有数据集开发的预训练模型。尽管这些预先训练的网络是可用的，但仍然需要一个适应性强的环境来测试和微调为临床任务量身定制的特定模型。这个环境应该对同行测试、反馈和持续的模型改进开放，允许动态模型更新，这在疾病和扫描技术快速发展的医学领域尤为重要。在这种背景下，Discovery Viewer（DV）平台是在西奈山生物医学工程和成像研究所（BMEII）内部开发的，以促进尖端医疗人工智能模型的创建和分发，这些模型在开发后仍然可以访问。这个一体化平台为非AI专家提供了一个独特的环境，让他们学习、开发和分享自己的深度学习（DL）概念。本文介绍了该平台的各种用例，其主要目标是展示DV如何能够使没有人工智能专业知识的个人能够创建高性能的DL模型。我们让三位非AI专家开发不同的肌肉骨骼AI项目，包括分割、回归和分类任务。在每个项目中，80%的样本都提供了这些样本的一个子集，以帮助志愿者理解预期的注释任务。随后，他们负责注释剩余的样本，并通过平台的“训练模块”训练他们的模型。然后在单独的20%延迟数据集上测试所得模型以评估其性能。分类模型的准确度为0.94，灵敏度为0.92，特异性为1。回归模型产生的平均绝对误差为14.27像素。分割模型的Dice评分为0.93，灵敏度为0.9，特异度为0.99。该计划旨在扩大医疗人工智能模型开发人员的社区，并使所有利益相关者都能获得这项技术。最终目标是促进医疗AI模型从研究到临床的过渡。
The rapid rise of artificial intelligence (AI) in medicine in the last few years highlights the importance of developing bigger and better systems for data and model sharing. However, the presence of Protected Health Information (PHI) in medical data poses a challenge when it comes to sharing. One potential solution to mitigate the risk of PHI breaches is to exclusively share pre-trained models developed using private datasets. Despite the availability of these pre-trained networks, there remains a need for an adaptable environment to test and fine-tune specific models tailored for clinical tasks. This environment should be open for peer testing, feedback, and continuous model refinement, allowing dynamic model updates that are especially important in the medical field, where diseases and scanning techniques evolve rapidly. In this context, the Discovery Viewer (DV) platform was developed in-house at the Biomedical Engineering and Imaging Institute at Mount Sinai (BMEII) to facilitate the creation and distribution of cutting-edge medical AI models that remain accessible after their development. The all-in-one platform offers a unique environment for non-AI experts to learn, develop, and share their own deep learning (DL) concepts. This paper presents various use cases of the platform, with its primary goal being to demonstrate how DV holds the potential to empower individuals without expertise in AI to create high-performing DL models. We tasked three non-AI experts to develop different musculoskeletal AI projects that encompassed segmentation, regression, and classification tasks. In each project, 80% of the samples were provided with a subset of these samples annotated to aid the volunteers in understanding the expected annotation task. Subsequently, they were responsible for annotating the remaining samples and training their models through the platform’s “Training Module”. The resulting models were then tested on the separate 20% hold-off dataset to assess their performance. The classification model achieved an accuracy of 0.94, a sensitivity of 0.92, and a specificity of 1. The regression model yielded a mean absolute error of 14.27 pixels. And the segmentation model attained a Dice Score of 0.93, with a sensitivity of 0.9 and a specificity of 0.99. This initiative seeks to broaden the community of medical AI model developers and democratize the access of this technology to all stakeholders. The ultimate goal is to facilitate the transition of medical AI models from research to clinical settings.
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影响因子：
82.9
作者：
Dayan, Ittai;Roth, Holger R.;Zhong, Aoxiao;Harouni, Ahmed;Gentili, Amilcare;Abidin, Anas Z.;Liu, Andrew;Costa, Anthony Beardsworth;Wood, Bradford J.;Tsai, Chien-Sung;Wang, Chih-Hung;Hsu, Chun-Nan;Lee, C. K.;Ruan, Peiying;Xu, Daguang;Wu, Dufan;Huang, Eddie;Kitamura, Felipe Campos;Lacey, Griffin;de Antonio Corradi, Gustavo Cesar;Nino, Gustavo;Shin, Hao-Hsin;Obinata, Hirofumi;Ren, Hui;Crane, Jason C.;Tetreault, Jesse;Guan, Jiahui;Garrett, John W.;Kaggie, Joshua D.;Park, Jung Gil;Dreyer, Keith;Juluru, Krishna;Kersten, Kristopher;Rockenbach, Marcio Aloisio Bezerra Cavalcanti;Linguraru, Marius George;Haider, Masoom A.;AbdelMaseeh, Meena;Rieke, Nicola;Damasceno, Pablo F.;Silva, Pedro Mario Cruz E.;Wang, Pochuan;Xu, Sheng;Kawano, Shuichi;Sriswasdi, Sira;Park, Soo Young;Grist, Thomas M.;Buch, Varun;Jantarabenjakul, Watsamon;Wang, Weichung;Tak, Won Young;Li, Xiang;Lin, Xihong;Kwon, Young Joon;Quraini, Abood;Feng, Andrew;Priest, Andrew N.;Turkbey, Baris;Glicksberg, Benjamin;Bizzo, Bernardo;Kim, Byung Seok;Tor-Diez, Carlos;Lee, Chia-Cheng;Hsu, Chia-Jung;Lin, Chin;Lai, Chiu-Ling;Hess, Christopher P.;Compas, Colin;Bhatia, Deepeksha;Oermann, Eric K.;Leibovitz, Evan;Sasaki, Hisashi;Mori, Hitoshi;Yang, Isaac;Sohn, Jae Ho;Murthy, Krishna Nand Keshava;Fu, Li-Chen;Furtado de Mendonca, Matheus Ribeiro;Fralick, Mike;Kang, Min Kyu;Adil, Mohammad;Gangai, Natalie;Vateekul, Peerapon;Elnajjar, Pierre;Hickman, Sarah;Majumdar, Sharmila;McLeod, Shelley L.;Reed, Sheridan;Graf, Stefan;Harmon, Stephanie;Kodama, Tatsuya;Puthanakit, Thanyawee;Mazzulli, Tony;de Lavor, Vitor Lima;Rakvongthai, Yothin;Lee, Yu Rim;Wen, Yuhong;Gilbert, Fiona J.;Flores, Mona G.;Li, Quanzheng
通讯作者：
Li, Quanzheng
影响因子：
16.6
作者：
Mei, Xueyan;Liu, Zelong;Singh, Ayushi;Lange, Marcia;Boddu, Priyanka;Gong, Jingqi Q. X.;Lee, Justine;DeMarco, Cody;Cao, Chendi;Platt, Samantha;Sivakumar, Ganesh;Gross, Benjamin;Huang, Mingqian;Masseaux, Joy;Dua, Sakshi;Bernheim, Adam;Chung, Michael C.;Deyer, Timothy;Jacobi, Adam;Padilla, Maria;Fayad, Zahi A. A.;Yang, Yang
通讯作者：
Yang, Yang
影响因子：
4.6
作者：
通讯作者：
--
DOI：
10.3390/bioengineering10070815
发表时间：
2023-07-08
期刊：
Bioengineering (Basel, Switzerland)
影响因子：
--
作者：
通讯作者：
--
影响因子：
2.5
作者：
Fauveau, Valentin;Jacobi, Adam;Bernheim, Adam;Chung, Michael;Benkert, Thomas;Fayad, Zahi A.;Feng, Li
通讯作者：
Feng, Li