Hembroff Guy, Klochko Chad, Craig Joseph, Changarnkothapeecherikkal Harikrishnan, Loi Richard Q
Department of Applied Computing, Michigan Technological University, 1400 Townsend Drive, Houghton, MI, 49931, USA.
Department of Radiology, Division of Musculoskeletal Radiology, Henry Ford Hospital, 2799 West Grand Boulevard, Detroit, MI, 48202, USA.
J Imaging Inform Med. 2025 Apr;38(2):838-849. doi: 10.1007/s10278-024-01220-9. Epub 2024 Aug 26.
Radiographic quality control is an integral component of the radiology workflow. In this study, we developed a convolutional neural network model tailored for automated quality control, specifically designed to detect and classify key attributes of wrist radiographs including projection, laterality (based on the right/left marker), and the presence of hardware and/or casts. The model's primary objective was to ensure the congruence of results with image requisition metadata to pass the quality assessment. Using a dataset of 6283 wrist radiographs from 2591 patients, our multitask-capable deep learning model based on DenseNet 121 architecture achieved high accuracy in classifying projections (F1 Score of 97.23%), detecting casts (F1 Score of 97.70%), and identifying surgical hardware (F1 Score of 92.27%). The model's performance in laterality marker detection was lower (F1 Score of 82.52%), particularly for partially visible or cut-off markers. This paper presents a comprehensive evaluation of our model's performance, highlighting its strengths, limitations, and the challenges encountered during its development and implementation. Furthermore, we outline planned future research directions aimed at refining and expanding the model's capabilities for improved clinical utility and patient care in radiographic quality control.
放射影像质量控制是放射学工作流程中不可或缺的一部分。在本研究中，我们开发了一种专门用于自动质量控制的卷积神经网络模型，该模型特别设计用于检测和分类腕部X光片的关键属性，包括投照角度、左右侧（基于左右标记）以及是否存在硬件植入物和/或石膏。该模型的主要目标是确保结果与图像申请元数据一致，以通过质量评估。使用来自2591名患者的6283张腕部X光片数据集，我们基于DenseNet 121架构的多任务深度学习模型在投照角度分类（F1分数为97.23%）、石膏检测（F1分数为97.70%）和手术硬件识别（F1分数为92.27%）方面取得了高精度。该模型在左右标记检测方面的性能较低（F1分数为82.52%），特别是对于部分可见或截断的标记。本文全面评估了我们模型的性能，突出了其优势、局限性以及在开发和实施过程中遇到的挑战。此外，我们概述了计划中的未来研究方向，旨在改进和扩展模型的能力，以提高放射影像质量控制中的临床实用性和患者护理水平。
