{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:23:10Z","timestamp":1760059390495,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T00:00:00Z","timestamp":1749513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Independent Vet Care (IVC). R PUBL AI MACHINE LEARNING NASAL PATHOLOGY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Animals"],"abstract":"<jats:p>Computed tomography (CT) is the imaging method of choice for evaluating the canine nasal cavity, being invaluable in determining disease extent, guiding sampling, and planning treatment. While predictions of pathology type can be made, there is significant overlap between CT changes noted in neoplastic, inflammatory, and infectious nasal disease. Recent years have seen remarkable advancement in computer-aided detection systems in human medicine, with machine and deep learning techniques being successfully applied for the identification and accurate classification of intranasal pathology. This study aimed to develop a neural network pipeline for differentiating nasal pathology in dogs using CT studies of the head. A total of 80 CT studies were recruited for training and testing purposes. Studies falling into one of the three groups (normal nasal anatomy, fungal rhinitis, and intranasal neoplasia) were manually segmented and used to train a suite of neural networks. Standard accuracy metrics assessed performance during training and testing. The machine learning algorithm showed reasonable accuracy (86%) in classifying the diagnosis from an isolated scan slice but high accuracy (99%) when aggregating over slices taken from a full scan. These results suggest that machine learning programmes can accurately discriminate between intranasal pathologies based on canine computed tomography.<\/jats:p>","DOI":"10.3390\/ani15121718","type":"journal-article","created":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T12:53:16Z","timestamp":1749559996000},"page":"1718","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Machine Learning Model for the Automated Diagnosis of Nasal Pathology in Canine Patients"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1056-8415","authenticated-orcid":false,"given":"Andreea","family":"Istrate","sequence":"first","affiliation":[{"name":"Pride Veterinary Referrals, Independent Vetcare (IVC) Evidensia, Riverside Road, Derby DE24 8HX, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Radu","family":"Constantinescu","sequence":"additional","affiliation":[{"name":"Dick White Referrals Veterinary Specialists, Part of Linnaeus, Station Farm, London Road, Six Mile Bottom, Cambridgeshire CB8 0UH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6953-4356","authenticated-orcid":false,"given":"Lithicka","family":"Anandavel","sequence":"additional","affiliation":[{"name":"School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shraddha","family":"Rajeshkumar Tandel","sequence":"additional","affiliation":[{"name":"School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1318-8343","authenticated-orcid":false,"given":"Simon","family":"Dye","sequence":"additional","affiliation":[{"name":"School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Charlotte","family":"Dye","sequence":"additional","affiliation":[{"name":"Pride Veterinary Referrals, Independent Vetcare (IVC) Evidensia, Riverside Road, Derby DE24 8HX, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Guo, M., Zang, X., Fu, W., Yan, H., Bao, X., Li, T., and Qiao, J. (2023). Classification of nasal polyps and inverted papillomas using CT-based radiomics. Insights Into Imaging, 14.","DOI":"10.1186\/s13244-023-01536-0"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Du, W., Kang, W., Lai, S., Cai, Z., Chen, Y., and Lin, Y. (2024). Deep learning in computed tomography to predict endotype in chronic rhinosinusitis with nasal polyps. BMC Med. Imaging, 24.","DOI":"10.1186\/s12880-024-01203-w"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"942","DOI":"10.21037\/qims.2019.05.15","article-title":"Improvement diagnostic accuracy of sinusitis recognition in paranasal sinus X-ray using multiple deep learning models","volume":"9","author":"Kim","year":"2019","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.3349\/ymj.2021.62.12.1125","article-title":"Effective End-to-End Deep Learning Process in Medical Imaging Using Independent Task Learning: Application for Diagnosis of Maxillary Sinusitis","volume":"62","author":"Oh","year":"2021","journal-title":"Yonsei Med. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1111\/coa.13988","article-title":"Differentiation of eosinophilic and non-eosinophilic chronic rhinosinusitis on preoperative computed tomography using deep learning","volume":"48","author":"Hua","year":"2023","journal-title":"Clin. Otolaryngol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Jeon, Y., Lee, K., Sunwoo, L., Choi, D., Oh, D.Y., Lee, K.J., Kim, Y., Kim, J.W., Cho, S.J., and Baik, S.H. (2021). Deep Learning for Diagnosis of Paranasal Sinusitis Using Multi-View Radiographs. Diagnostics, 11.","DOI":"10.3390\/diagnostics11020250"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1002\/alr.22854","article-title":"Feasibility of a deep learning-based algorithm for automated detection and classification of nasal polyps and inverted papillomas on nasal endoscopic images","volume":"11","author":"Girdler","year":"2021","journal-title":"Int. Forum Allergy Rhinol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Tao, W., Lu, M., Zhou, X., Montemezzi, S., Bai, G., Yue, Y., Li, X., Zhao, L., Zhou, C., and Lu, G. (2021). Machine Learning Based on Multi-Parametric MRI to Predict Risk of Breast Cancer. Front. Oncol., 11.","DOI":"10.3389\/fonc.2021.570747"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kim, K.S., Kim, B.K., Chung, M.J., Cho, H.B., Cho, B.H., and Jung, Y.G. (2022). Detection of maxillary sinus fungal ball via 3-D CNN-based artificial intelligence: Fully automated system and clinical validation. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0263125"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chen, Z., Wang, M., De Wilde, R.L., Feng, R., Su, M., Torres-de la Roche, L.A., and Shi, W. (2021). A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype. Front. Immunol., 17.","DOI":"10.3389\/fimmu.2021.749459"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1934","DOI":"10.1038\/s41416-021-01386-x","article-title":"Artificial Intelligence-based methods in head and neck cancer diagnosis: An overview","volume":"124","author":"Mahmood","year":"2021","journal-title":"Br. J. Cancer"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Fujima, N., Shimizu, Y., Yoshida, D., Kano, S., Mizumachi, T., Homma, A., Yasuda, K., Onimaru, R., Sakai, O., and Kudo, K. (2019). Machine-Learning-Based Prediction of Treatment Outcomes Using MR Imaging-Derived Quantitative Tumor Information in Patients with Sinonasal Squamous Cell Carcinomas: A Preliminary Study. Cancers, 11.","DOI":"10.3390\/cancers11060800"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Nuutinen, M., Haukka, J., Virkkula, P., Torkki, P., and Toppila-Salmi, S. (2022). Using machine learning for the personalised prediction of revision endoscopic sinus surgery. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0267146"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1111\/vru.13132","article-title":"Employing deep convolutional neural networks for segmenting the medial retropharyngeal lymph nodes in CT studies of dogs","volume":"63","author":"Schmid","year":"2022","journal-title":"Vet. Radiol. Ultrasound"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Park, J., Choi, B., Ko, J., Chun, J., Park, I., Lee, J., Kim, J., Kim, J., Eom, K., and Kim, J.S. (2022). Deep-Learning-Based Automatic Segmentation of Head and Neck Organs for Radiation Therapy in Dogs. Front. Vet. Sci., 8.","DOI":"10.3389\/fvets.2021.721612"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Groendahl, A.R., Huynh, B.N., Tomic, O., S\u00f8vik, \u00c5., Dale, E., Malinen, E., Skogmo, H.K., and Futsaether, C.M. (2023). Automatic gross tumor segmentation of canine head and neck cancer using deep learning and cross-species transfer learning. Front. Vet. Sci., 10.","DOI":"10.3389\/fvets.2023.1143986"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1111\/j.1748-5827.2009.00729.x","article-title":"Comparison of computed tomography and magnetic resonance imaging for the evaluation of canine intranasal neoplasia","volume":"50","author":"Drees","year":"2009","journal-title":"J. Small Anim. Pract."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1111\/jsap.13736","article-title":"Diagnostic findings in sinonasal aspergillosis in dogs in the United Kingdom: 475 cases (2011\u20132021)","volume":"65","author":"Prior","year":"2024","journal-title":"J. Small Anim. Pract."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1111\/vru.12981","article-title":"Computed tomography may be helpful in discriminating grass awn foreign body rhinitis from non-foreign body rhinitis in dogs","volume":"62","author":"Lafuma","year":"2021","journal-title":"Vet. Radiol. Ultrasound"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1111\/j.1740-8261.2003.tb00477.x","article-title":"Diagnostic value of computed tomography in dogs with chronic nasal disease","volume":"44","author":"Saunders","year":"2003","journal-title":"Vet. Radiol. Ultrasound"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1111\/j.1748-5827.2005.tb00321.x","article-title":"Computed tomography as an aid in the diagnosis of chronic nasal disease in dogs","volume":"46","author":"Lefebvre","year":"2005","journal-title":"J. Small Anim. Pract."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1111\/jsap.12187","article-title":"Diagnostic accuracy of three biopsy techniques in 117 dogs with intra-nasal neoplasia","volume":"55","author":"Harris","year":"2014","journal-title":"J. Small Anim. Pract."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Milne, J., Ekkebus, S., Thompson, D., and Brash, R. (2025). Retrospective evaluation of computed tomographic-guided Tru-Cut biopsies in 16 dogs and 14 cats with nasal cavity mass lesions. J. Vet. Intern. Med., 39.","DOI":"10.1111\/jvim.17296"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lv, Q., Zhang, S., and Wang, Y. (2022). Deep Learning Model of Image Classification Using Machine Learning. Adv. Multimed., 2022.","DOI":"10.1155\/2022\/3351256"},{"key":"ref_25","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). Identity mappings in deep residual networks. Computer Vision\u2014ECCV 2016, Springer International Publishing.","DOI":"10.1007\/978-3-319-46493-0"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). Mobilenetv2: Inverted Residuals and Linear Bottlenecks. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Jha, D., Smedsrud, P.H., Riegler, M.A., Johansen, D., de Lange, T., Halvorsen, P., and Johansen, H.D. (2019). ResUNet++: An Advanced Architecture for Medical Image Segmentation. arXiv.","DOI":"10.1109\/ISM46123.2019.00049"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., and Tang, X. (2017). Residual Attention Network for Image Classification. arXiv.","DOI":"10.1109\/CVPR.2017.683"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kuo, C.-C., Wang, M.-J., and Lu, J.-M. (2019). Developing sizing systems using 3D scanning head anthropometric data. Measurement, 152.","DOI":"10.1016\/j.measurement.2019.107264"}],"container-title":["Animals"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-2615\/15\/12\/1718\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:49:41Z","timestamp":1760032181000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-2615\/15\/12\/1718"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,10]]},"references-count":31,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["ani15121718"],"URL":"https:\/\/doi.org\/10.3390\/ani15121718","relation":{},"ISSN":["2076-2615"],"issn-type":[{"type":"electronic","value":"2076-2615"}],"subject":[],"published":{"date-parts":[[2025,6,10]]}}}