Imaging Service, Baltimore VA Medical Center, Baltimore, Maryland; Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland; and Co-chair, 3D Printing Registry Committee, American College of Radiology.
3D Innovations Lab, Rady Children's Hospital, San Diego, California; and Department of Neurological Surgery, University of California San Diego Health, San Diego, California.
J Am Coll Radiol. 2024 Nov;21(11):1781-1791. doi: 10.1016/j.jacr.2024.07.019. Epub 2024 Aug 6.
The aim of this study was to report data from the first 3 years of operation of the RSNA-ACR 3D Printing Registry.
Data from June 2020 to June 2023 were extracted, including demographics, indications, workflow, and user assessments. Clinical indications were stratified by 12 organ systems. Imaging modalities, printing technologies, and numbers of parts per case were assessed. Effort data were analyzed, dividing staff members into provider and nonprovider categories. The opinions of clinical users were evaluated using a Likert scale questionnaire, and estimates of procedure time saved were collected.
A total of 20 sites and 2,637 cases were included, consisting of 1,863 anatomic models and 774 anatomic guides. Mean patient ages for models and guides were 42.4 ± 24.5 years and 56.3 ± 18.5 years, respectively. Cardiac models were the most common type of model (27.2%), and neurologic guides were the most common type of guide (42.4%). Material jetting, vat photopolymerization, and material extrusion were the most common printing technologies used overall (85.6% of all cases). On average, providers spent 92.4 min and nonproviders spent 335.0 min per case. Providers spent most time on consultation (33.6 min), while nonproviders focused most on segmentation (148.0 min). Confidence in treatment plans increased after using 3-D printing (P < .001). Estimated procedure time savings for 155 cases was 40.5 ± 26.1 min.
Three-dimensional printing is performed at health care facilities for many clinical indications. The registry provides insight into the technologies and workflows used to create anatomic models and guides, and the data show clinical benefits from 3-D printing.
本研究旨在报告 RSNA-ACR 3D 打印注册中心运营头 3 年的数据。
提取 2020 年 6 月至 2023 年 6 月的数据，包括人口统计学、适应证、工作流程和用户评估。临床适应证按 12 个器官系统分层。评估成像方式、打印技术和每个病例的零件数量。对工作人员进行分类，分为提供者和非提供者，分析工作量数据。使用李克特量表问卷调查评估临床用户的意见，并收集节省的程序时间估计值。
共纳入 20 个站点和 2637 例，包括 1863 个解剖模型和 774 个解剖引导器。模型和引导器的患者平均年龄分别为 42.4 ± 24.5 岁和 56.3 ± 18.5 岁。心脏模型是最常见的模型类型（27.2%），神经引导器是最常见的引导器类型（42.4%）。整体上，材料喷射、光聚合和材料挤出是使用最广泛的打印技术（占所有病例的 85.6%）。平均而言，提供者每例花费 92.4 分钟，非提供者每例花费 335.0 分钟。提供者将大部分时间用于咨询（33.6 分钟），而非提供者则主要专注于分割（148.0 分钟）。使用 3D 打印后，对治疗计划的信心增加（P <.001）。对 155 例病例的估计手术时间节省为 40.5 ± 26.1 分钟。
3D 打印在医疗机构中用于多种临床适应证。该注册中心提供了有关创建解剖模型和引导器所使用的技术和工作流程的深入了解，并显示了 3D 打印的临床益处。
