Kocaoğlu Sıtkı
Biomedical Engineering Department, Ankara Yıldırım Beyazıt University, Ankara, TÜRKİYE, Turkey.
Sci Rep. 2025 Jul 1;15(1):21854. doi: 10.1038/s41598-025-08593-z.
Ankylosing Spondylitis (AS), commonly known as Bechterew's disease, is a complex, potentially disabling disease that develops slowly over time and progresses to radiographic sacroiliitis. The etiology of this disease is poorly understood, making it difficult to diagnose. Therefore, treatment is also delayed. This study aims to diagnose AS with an automated system that classifies axial magnetic resonance imaging (MRI) sequences of AS patients. Recently, the application of deep learning neural networks (DLNNs) for MRI classification has become widespread. The implementation of this process on computer-independent end devices is advantageous due to its high computational power and low latency requirements. In this research, an MRI dataset containing images from 527 individuals was used. A deep learning architecture on a Field Programmable Gate Array (FPGA) card was implemented and analyzed. The results show that the classification performed on FPGA in AS diagnosis yields successful results close to the classification performed on CPU.
强直性脊柱炎（AS），通常被称为别赫捷列夫病，是一种复杂的、可能导致残疾的疾病，它会随着时间的推移而缓慢发展，并进展为放射学骶髂关节炎。这种疾病的病因尚不清楚，难以诊断。因此，治疗也会延迟。本研究旨在通过一个对AS患者的轴向磁共振成像（MRI）序列进行分类的自动化系统来诊断AS。近年来，深度学习神经网络（DLNNs）在MRI分类中的应用已变得广泛。由于其高计算能力和低延迟要求，在独立于计算机的终端设备上实现这个过程具有优势。在本研究中，使用了一个包含527个人图像的MRI数据集。在现场可编程门阵列（FPGA）卡上实现并分析了一种深度学习架构。结果表明，在AS诊断中在FPGA上进行的分类产生了与在CPU上进行的分类相近的成功结果。
