Early Detection of Periapical Lesions Using Deep Learning in CBCT Imaging
Bibliographic Data
| ID | 22200038 |
|---|---|
| Authors | Rais Allauddin Mulla (School of Visual Arts, corresponding author) |
| Year | 2025 |
| Volume | 5 |
| Issue | 3 |
| Publication date | 2025-07-18 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ShodhKosh: Journal of Visual and Performing Arts (JOURNAL) |
| Journal identifiers | ISSN: 2582-7472 • E-ISSN: 2582-7472 |
| Publisher | Granthaalayah Publications and Printers (PUBLISHER • IN) |
| DOI | 10.29121/shodhkosh.v5.i3.2024.5830 |
| OpenAlex | W4413060383 |
| Language | EN |
| References cited | 5 |
Timely diagnosis of periapical lesions are vital to the overall management of the patients in endodontics. Cone Beam Computed Tomography (CBCT) offers clear, three-dimensional radiographs with higher resolution than standard radiographs for detection of such lesions. In this paper, a deep learning based framework using a CNN is proposed for automatic periapical lesion detection from CBCT scans. With pre-training on the well-analyzed dataset covering multiple ethnicity and annotations, the model achieved superior accuracy, sensitivity and specificity, indicating its potential to provide assistance to the clinicians for projective diagnosis process while reducing diagnostic time and subjective variation. The findings indicate the promise of AI-based tools in improving workflow of diagnoses in dental radiology
Computed tomography · Cone beam computed tomography · Diagnostic accuracy · Endodontics · Medical diagnosis · Radiography · Radiology · Workflow · Advanced X-ray and CT Imaging · Computer Science · Dental Radiography and Imaging · Dentistry · Medicine · Radiology practices and education
| Citation velocity | historical |
|---|---|
| Highly cited | No |