Automatic classification and grading of canine tracheal collapse on thoracic radiographs by using deep learning
| dc.contributor.author | Suksangvoravong, Hathaiphat | |
| dc.contributor.author | Choisunirachon, Nan | |
| dc.contributor.author | Tongloy, Teerawat | |
| dc.contributor.author | Chuwongin, Santhad | |
| dc.contributor.author | Boonsang, Siridech | |
| dc.contributor.author | Kittichai, Veerayuth | |
| dc.contributor.author | Thanaboonnipat, Chutimon | |
| dc.date.accessioned | 2026-08-06T10:47:26Z | |
| dc.date.available | 2026-08-06T10:47:26Z | |
| dc.date.issued | 2024-11-01 | |
| dc.description.abstract | Tracheal collapse is a chronic and progressively worsening disease; the severity of clinical symptoms experienced by affected individuals depends on the degree of airway collapse. Cutting-edge automated tools are necessary to modernize disease screening using radiographs across various veterinary settings, such as animal clinics and hospitals. This is primarily due to the inherent challenges associated with interpreting uncertainties among veterinarians. In this study, an artificial intelligence model was developed to screen canine tracheal collapse using archived lateral cervicothoracic radiographs. This model can differentiate between a normal and collapsed trachea, ranging from early to severe degrees. The you-only-look-once (YOLO) models, including YOLO v3, YOLO v4, and YOLO v4 tiny, were used to train and test data sets under the in-house XXX platform. The results showed that the YOLO v4 tiny-416 model had satisfactory performance in screening among the normal trachea, grade 1–2 tracheal collapse, and grade 3–4 tracheal collapse with 98.30% sensitivity, 99.20% specificity, and 98.90% accuracy. The area under the curve of the precision–recall curve was >0.8, which demonstrated high diagnostic accuracy. The intraobserver agreement between deep learning and radiologists was κ = 0.975 (P <.001), with all observers having excellent agreement (κ = 1.00, P <.001). The intraclass correlation coefficient between observers was >0.90, which represented excellent consistency. Therefore, the deep learning model can be a useful and reliable method for effective screening and classification of the degree of tracheal collapse based on routine lateral cervicothoracic radiographs. | |
| dc.identifier.citation | Veterinary Radiology and Ultrasound, 65(6), 679-688, 2024 | |
| dc.identifier.doi | 10.1111/vru.13413 | |
| dc.identifier.issn | 10588183 | |
| dc.identifier.other | 2-s2.0-85198540019 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/15989 | |
| dc.source | Veterinary Radiology and Ultrasound | |
| dc.subject | deep learning | |
| dc.subject | dogs | |
| dc.subject | radiographs | |
| dc.subject | screening | |
| dc.subject | tracheal collapse | |
| dc.title | Automatic classification and grading of canine tracheal collapse on thoracic radiographs by using deep learning | |
| dc.type | Article |