Noise-Optimized CBCT Imaging of Temporomandibular Joints—The Impact of AI on Image Quality
<b>Background:</b> Temporomandibular joint disorder (TMD) is a common medical condition. Cone beam computed tomography (CBCT) is effective in assessing TMD-related bone changes, but image noise may impair diagnosis. Emerging deep learning reconstruction algorithms (DLRs) could minimize n...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/13/5/1502 |
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| author | Wojciech Kazimierczak Kamila Kędziora Joanna Janiszewska-Olszowska Natalia Kazimierczak Zbigniew Serafin |
| author_facet | Wojciech Kazimierczak Kamila Kędziora Joanna Janiszewska-Olszowska Natalia Kazimierczak Zbigniew Serafin |
| author_sort | Wojciech Kazimierczak |
| collection | DOAJ |
| description | <b>Background:</b> Temporomandibular joint disorder (TMD) is a common medical condition. Cone beam computed tomography (CBCT) is effective in assessing TMD-related bone changes, but image noise may impair diagnosis. Emerging deep learning reconstruction algorithms (DLRs) could minimize noise and improve CBCT image clarity. This study compares standard and deep learning-enhanced CBCT images for image quality in detecting osteoarthritis-related degeneration in TMJs (temporomandibular joints). This study analyzed CBCT images of patients with suspected temporomandibular joint degenerative joint disease (TMJ DJD). <b>Methods:</b> The DLM reconstructions were performed with ClariCT.AI software. Image quality was evaluated objectively via CNR in target areas and subjectively by two experts using a five-point scale. Both readers also assessed TMJ DJD lesions. The study involved 50 patients with a mean age of 28.29 years. <b>Results:</b> Objective analysis revealed a significantly better image quality in DLM reconstructions (CNR levels; <i>p</i> < 0.001). Subjective assessment showed high inter-reader agreement (κ = 0.805) but no significant difference in image quality between the reconstruction types (<i>p</i> = 0.055). Lesion counts were not significantly correlated with the reconstruction type (<i>p</i> > 0.05). <b>Conclusions:</b> The analyzed DLM reconstruction notably enhanced the objective image quality in TMJ CBCT images but did not significantly alter the subjective quality or DJD lesion diagnosis. However, the readers favored DLM images, indicating the potential for better TMD diagnosis with CBCT, meriting more study. |
| first_indexed | 2024-04-25T00:26:00Z |
| format | Article |
| id | doaj.art-e2bbd91e2e334e6ca5a6c5a6b1ca77f5 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-04-25T00:26:00Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-e2bbd91e2e334e6ca5a6c5a6b1ca77f52024-03-12T16:48:41ZengMDPI AGJournal of Clinical Medicine2077-03832024-03-01135150210.3390/jcm13051502Noise-Optimized CBCT Imaging of Temporomandibular Joints—The Impact of AI on Image QualityWojciech Kazimierczak0Kamila Kędziora1Joanna Janiszewska-Olszowska2Natalia Kazimierczak3Zbigniew Serafin4Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, PolandDepartment of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, PolandDepartment of Interdisciplinary Dentistry, Pomeranian Medical University in Szczecin, 70-111 Szczecin, PolandKazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, PolandDepartment of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland<b>Background:</b> Temporomandibular joint disorder (TMD) is a common medical condition. Cone beam computed tomography (CBCT) is effective in assessing TMD-related bone changes, but image noise may impair diagnosis. Emerging deep learning reconstruction algorithms (DLRs) could minimize noise and improve CBCT image clarity. This study compares standard and deep learning-enhanced CBCT images for image quality in detecting osteoarthritis-related degeneration in TMJs (temporomandibular joints). This study analyzed CBCT images of patients with suspected temporomandibular joint degenerative joint disease (TMJ DJD). <b>Methods:</b> The DLM reconstructions were performed with ClariCT.AI software. Image quality was evaluated objectively via CNR in target areas and subjectively by two experts using a five-point scale. Both readers also assessed TMJ DJD lesions. The study involved 50 patients with a mean age of 28.29 years. <b>Results:</b> Objective analysis revealed a significantly better image quality in DLM reconstructions (CNR levels; <i>p</i> < 0.001). Subjective assessment showed high inter-reader agreement (κ = 0.805) but no significant difference in image quality between the reconstruction types (<i>p</i> = 0.055). Lesion counts were not significantly correlated with the reconstruction type (<i>p</i> > 0.05). <b>Conclusions:</b> The analyzed DLM reconstruction notably enhanced the objective image quality in TMJ CBCT images but did not significantly alter the subjective quality or DJD lesion diagnosis. However, the readers favored DLM images, indicating the potential for better TMD diagnosis with CBCT, meriting more study.https://www.mdpi.com/2077-0383/13/5/1502cone beam computed tomographydeep learning modelimage qualitynoise reductiondental imagingtemporomandibular joint |
| spellingShingle | Wojciech Kazimierczak Kamila Kędziora Joanna Janiszewska-Olszowska Natalia Kazimierczak Zbigniew Serafin Noise-Optimized CBCT Imaging of Temporomandibular Joints—The Impact of AI on Image Quality Journal of Clinical Medicine cone beam computed tomography deep learning model image quality noise reduction dental imaging temporomandibular joint |
| title | Noise-Optimized CBCT Imaging of Temporomandibular Joints—The Impact of AI on Image Quality |
| title_full | Noise-Optimized CBCT Imaging of Temporomandibular Joints—The Impact of AI on Image Quality |
| title_fullStr | Noise-Optimized CBCT Imaging of Temporomandibular Joints—The Impact of AI on Image Quality |
| title_full_unstemmed | Noise-Optimized CBCT Imaging of Temporomandibular Joints—The Impact of AI on Image Quality |
| title_short | Noise-Optimized CBCT Imaging of Temporomandibular Joints—The Impact of AI on Image Quality |
| title_sort | noise optimized cbct imaging of temporomandibular joints the impact of ai on image quality |
| topic | cone beam computed tomography deep learning model image quality noise reduction dental imaging temporomandibular joint |
| url | https://www.mdpi.com/2077-0383/13/5/1502 |
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