FPGA implementation of deep learning architecture for ankylosing spondylitis detection from MRI
Abstract 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, treatmen...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-08593-z |
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| author | Sıtkı Kocaoğlu |
| author_facet | Sıtkı Kocaoğlu |
| author_sort | Sıtkı Kocaoğlu |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-89abe01c6f844064bf71550bc6abf7fe |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-89abe01c6f844064bf71550bc6abf7fe2025-08-20T03:45:23ZengNature PortfolioScientific Reports2045-23222025-07-011511810.1038/s41598-025-08593-zFPGA implementation of deep learning architecture for ankylosing spondylitis detection from MRISıtkı Kocaoğlu0Biomedical Engineering Department, Ankara Yıldırım Beyazıt UniversityAbstract 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.https://doi.org/10.1038/s41598-025-08593-zAnkylosing spondilitisFPGADLNNDiagnosis |
| spellingShingle | Sıtkı Kocaoğlu FPGA implementation of deep learning architecture for ankylosing spondylitis detection from MRI Scientific Reports Ankylosing spondilitis FPGA DLNN Diagnosis |
| title | FPGA implementation of deep learning architecture for ankylosing spondylitis detection from MRI |
| title_full | FPGA implementation of deep learning architecture for ankylosing spondylitis detection from MRI |
| title_fullStr | FPGA implementation of deep learning architecture for ankylosing spondylitis detection from MRI |
| title_full_unstemmed | FPGA implementation of deep learning architecture for ankylosing spondylitis detection from MRI |
| title_short | FPGA implementation of deep learning architecture for ankylosing spondylitis detection from MRI |
| title_sort | fpga implementation of deep learning architecture for ankylosing spondylitis detection from mri |
| topic | Ankylosing spondilitis FPGA DLNN Diagnosis |
| url | https://doi.org/10.1038/s41598-025-08593-z |
| work_keys_str_mv | AT sıtkıkocaoglu fpgaimplementationofdeeplearningarchitectureforankylosingspondylitisdetectionfrommri |