Palm Viktoria, Norajitra Tobias, von Stackelberg Oyunbileg, Heussel Claus P, Skornitzke Stephan, Weinheimer Oliver, Kopytova Taisiya, Klein Andre, Almeida Silvia D, Baumgartner Michael, Bounias Dimitrios, Scherer Jonas, Kades Klaus, Gao Hanno, Jäger Paul, Nolden Marco, Tong Elizabeth, Eckl Kira, Nattenmüller Johanna, Nonnenmacher Tobias, Naas Omar, Reuter Julia, Bischoff Arved, Kroschke Jonas, Rengier Fabian, Schlamp Kai, Debic Manuel, Kauczor Hans-Ulrich, Maier-Hein Klaus, Wielpütz Mark O
Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany.
Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany.
Healthcare (Basel). 2022 Oct 29;10(11):2166. doi: 10.3390/healthcare10112166.
Automated image analysis plays an increasing role in radiology in detecting and quantifying image features outside of the perception of human eyes. Common AI-based approaches address a single medical problem, although patients often present with multiple interacting, frequently subclinical medical conditions. A holistic imaging diagnostics tool based on artificial intelligence (AI) has the potential of providing an overview of multi-system comorbidities within a single workflow. An interdisciplinary, multicentric team of medical experts and computer scientists designed a pipeline, comprising AI-based tools for the automated detection, quantification and characterization of the most common pulmonary, metabolic, cardiovascular and musculoskeletal comorbidities in chest computed tomography (CT). To provide a comprehensive evaluation of each patient, a multidimensional workflow was established with algorithms operating synchronously on a decentralized Joined Imaging Platform (JIP). The results of each patient are transferred to a dedicated database and summarized as a structured report with reference to available reference values and annotated sample images of detected pathologies. Hence, this tool allows for the comprehensive, large-scale analysis of imaging-biomarkers of comorbidities in chest CT, first in science and then in clinical routine. Moreover, this tool accommodates the quantitative analysis and classification of each pathology, providing integral diagnostic and prognostic value, and subsequently leading to improved preventive patient care and further possibilities for future studies.
自动图像分析在放射学中发挥着越来越重要的作用，可检测和量化人眼无法感知的图像特征。基于人工智能的常见方法解决的是单一医学问题，然而患者常常呈现出多种相互作用、通常为亚临床的病症。基于人工智能（AI）的整体成像诊断工具有可能在单个工作流程中提供多系统合并症的概述。一个由医学专家和计算机科学家组成的跨学科、多中心团队设计了一个流程，其中包括基于人工智能的工具，用于在胸部计算机断层扫描（CT）中自动检测、量化和表征最常见的肺部、代谢、心血管和肌肉骨骼合并症。为了对每位患者进行全面评估，建立了一个多维工作流程，算法在去中心化的联合成像平台（JIP）上同步运行。每位患者的结果被传输到一个专用数据库，并作为一份结构化报告进行总结，同时参考可用的参考值和检测到的病变的注释样本图像。因此，该工具首先在科学领域，然后在临床常规中，能够对胸部CT合并症的成像生物标志物进行全面、大规模的分析。此外，该工具适用于对每种病变进行定量分析和分类，提供完整的诊断和预后价值，进而改善预防性患者护理，并为未来研究提供更多可能性。
