TAIMWAS: A Trustworthy AI Model-based Intelligent Chronic Wound Analysis System

该项目将开发用于移动/平板电脑的智能软件/应用程序，可以作为全自动伤口分析系统的端到端解决方案。该软件将部署多个**稳健**和**可验证**深度学习(DL)模型，以分析2D/3D图像并提取有关伤口各方面的信息，以实现智能和高效的管理。DL模型将通过联合学习(FL)方法进行训练/更新，以提高数据多样性、保护用户隐私并将安全风险降至最低。我们还将使用深度生成模型来验证训练的模型的准确性。因此，模型的健壮性和可验证性将使我们的解决方案**TAIMWAS**值得信赖。这是第一个**值得信赖的AI系统**，使用FL提供端到端的伤口管理服务，使用多站点和多样化的图像数据。此外，该解决方案的准确非接触式测量、自动化设施报告和分析将提高临床生产率，降低临床变异性和高质量的患者结果，并将风险和责任降至最低。

The project will develop intelligent software/app for mobile/tablet that can work as an end-to-end solution for a fully automated wound analysis system. This software will deploy multiple **robust** and **verifiable** deep learning (DL) models to analyse 2D/3D images and extract information about various aspects of wounds for intelligent and efficient management. The DL models will be trained/updated through a federated learning (FL) approach to improve data diversity, preserve users' privacy, and minimise security risk. We will also use a deep generative model to verify the accuracy of the models trained. Thus, the robustness and verifiability of the models will make our solution **TAIMWAS** trustworthy. And this is the first **trustworthy AI system** that offers end-to-end wound management services using FL using multi-site and diverse image data. In addition, the solution's accurate non-contact measurements, automated facility reporting, and analytics will enhance clinical productivity, reduce clinical variability and quality patient outcomes, and minimise risk & liability.