Jan Rudolph, Johannes Rueckel, Jörg Döpfert, Wen Xin Ling, Jens Opalka, Christian Brem, Nina Hesse, Maria Ingenerf, Vanessa Koliogiannis, Olga Solyanik, Boj F Hoppe, Hanna Zimmermann, Wilhelm Flatz, Robert Forbrig, Maximilian Patzig, Boris-Stephan Rauchmann, Robert Perneczky, Oliver Peters, Josef Priller, Anja Schneider, Klaus Fliessbach, Andreas Hermann, Jens Wiltfang, Frank Jessen, Emrah Düzel, Katharina Buerger, Stefan Teipel, Christoph Laske, Matthis Synofzik, Annika Spottke, Michael Ewers, Peter Dechent, John-Dylan Haynes, Johannes Levin, Thomas Liebig, Jens Ricke, Michael Ingrisch, Sophia Stoecklein
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引用次数: 0
Abstract
Introduction: This study evaluates the clinical value of a deep learning-based artificial intelligence (AI) system that performs rapid brain volumetry with automatic lobe segmentation and age- and sex-adjusted percentile comparisons.
Methods: Fifty-five patients-17 with Alzheimer's disease (AD), 18 with frontotemporal dementia (FTD), and 20 healthy controls-underwent cranial magnetic resonance imaging scans. Two board-certified neuroradiologists (BCNR), two board-certified radiologists (BCR), and three radiology residents (RR) assessed the scans twice: first without AI support and then with AI assistance.
Results: AI significantly improved diagnostic accuracy for AD (area under the curve -AI: 0.800, +AI: 0.926, p < 0.05), with increased correct diagnoses (p < 0.01) and reduced errors (p < 0.03). BCR and RR showed notable performance gains (BCR: p < 0.04; RR: p < 0.02). For the diagnosis FTD, overall consensus (p < 0.01), BCNR (p < 0.02), and BCR (p < 0.05) recorded significantly more correct diagnoses.
Discussion: AI-assisted volumetry improves diagnostic performance in differentiating AD and FTD, benefiting all reader groups, including BCNR.
Highlights: Artificial intelligence (AI)-supported brain volumetry significantly improved the diagnostic accuracy for Alzheimer's disease (AD) and frontotemporal dementia (FTD), with notable performance gains across radiologists of varying expertise levels.The presented AI tool is readily clinically available and reduces brain volumetry processing time from 12 to 24 hours to under 5 minutes, with full integration into picture archiving and communication systems, streamlining the workflow and facilitating real-time clinical decision making.AI-supported rapid brain volumetry has the potential to improve early diagnosis and to improve patient management.
本研究评估了基于深度学习的人工智能（AI）系统的临床价值，该系统通过自动脑叶分割和年龄和性别调整的百分位数比较进行快速脑容量测量。方法：55例患者（17例阿尔茨海默病（AD）， 18例额颞叶痴呆（FTD）， 20例健康对照）进行了颅脑磁共振成像扫描。两名委员会认证的神经放射科医生（BCNR）、两名委员会认证的放射科医生（BCR）和三名放射科住院医生（RR）对扫描进行了两次评估：第一次没有人工智能支持，然后在人工智能的帮助下。结果：人工智能显著提高了AD的诊断准确率（曲线下面积-AI: 0.800, +AI: 0.926, p p p p p p p p p p p p p p p）讨论：人工智能辅助容积法提高了AD和FTD的诊断性能，使包括BCNR在内的所有读者群体受益。亮点：人工智能（AI）支持的脑容量测量显着提高了阿尔茨海默病（AD）和额颞叶痴呆（FTD）的诊断准确性，不同专业水平的放射科医生的表现都有显著提高。所介绍的人工智能工具易于临床使用，可将脑容量测量处理时间从12至24小时缩短至5分钟以下，与图像存档和通信系统完全集成，简化了工作流程，促进了实时临床决策。人工智能支持的快速脑容量测量具有改善早期诊断和改善患者管理的潜力。
期刊介绍：
Alzheimer''s & Dementia: Diagnosis, Assessment & Disease Monitoring (DADM) is an open access, peer-reviewed, journal from the Alzheimer''s Association® that will publish new research that reports the discovery, development and validation of instruments, technologies, algorithms, and innovative processes. Papers will cover a range of topics interested in the early and accurate detection of individuals with memory complaints and/or among asymptomatic individuals at elevated risk for various forms of memory disorders. The expectation for published papers will be to translate fundamental knowledge about the neurobiology of the disease into practical reports that describe both the conceptual and methodological aspects of the submitted scientific inquiry. Published topics will explore the development of biomarkers, surrogate markers, and conceptual/methodological challenges. Publication priority will be given to papers that 1) describe putative surrogate markers that accurately track disease progression, 2) biomarkers that fulfill international regulatory requirements, 3) reports from large, well-characterized population-based cohorts that comprise the heterogeneity and diversity of asymptomatic individuals and 4) algorithmic development that considers multi-marker arrays (e.g., integrated-omics, genetics, biofluids, imaging, etc.) and advanced computational analytics and technologies.
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