A Novel Machine Learning Model for the Automated Diagnosis of Nasal Pathology in Canine Patients
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 neoplas...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Animals |
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| Online Access: | https://www.mdpi.com/2076-2615/15/12/1718 |
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| author | Andreea Istrate Radu Constantinescu Lithicka Anandavel Shraddha Rajeshkumar Tandel Simon Dye Charlotte Dye |
| author_facet | Andreea Istrate Radu Constantinescu Lithicka Anandavel Shraddha Rajeshkumar Tandel Simon Dye Charlotte Dye |
| author_sort | Andreea Istrate |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a4be12d3841d4936b708ae15dc4f1cf8 |
| institution | Matheson Library |
| issn | 2076-2615 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj-art-a4be12d3841d4936b708ae15dc4f1cf82025-06-25T13:21:48ZengMDPI AGAnimals2076-26152025-06-011512171810.3390/ani15121718A Novel Machine Learning Model for the Automated Diagnosis of Nasal Pathology in Canine PatientsAndreea Istrate0Radu Constantinescu1Lithicka Anandavel2Shraddha Rajeshkumar Tandel3Simon Dye4Charlotte Dye5Pride Veterinary Referrals, Independent Vetcare (IVC) Evidensia, Riverside Road, Derby DE24 8HX, UKDick White Referrals Veterinary Specialists, Part of Linnaeus, Station Farm, London Road, Six Mile Bottom, Cambridgeshire CB8 0UH, UKSchool of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UKSchool of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UKSchool of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UKPride Veterinary Referrals, Independent Vetcare (IVC) Evidensia, Riverside Road, Derby DE24 8HX, UKComputed 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.https://www.mdpi.com/2076-2615/15/12/1718convolutional neural networkmachine learningcomputed tomographyintranasal neoplasiafungal rhinitiscanine head |
| spellingShingle | Andreea Istrate Radu Constantinescu Lithicka Anandavel Shraddha Rajeshkumar Tandel Simon Dye Charlotte Dye A Novel Machine Learning Model for the Automated Diagnosis of Nasal Pathology in Canine Patients Animals convolutional neural network machine learning computed tomography intranasal neoplasia fungal rhinitis canine head |
| title | A Novel Machine Learning Model for the Automated Diagnosis of Nasal Pathology in Canine Patients |
| title_full | A Novel Machine Learning Model for the Automated Diagnosis of Nasal Pathology in Canine Patients |
| title_fullStr | A Novel Machine Learning Model for the Automated Diagnosis of Nasal Pathology in Canine Patients |
| title_full_unstemmed | A Novel Machine Learning Model for the Automated Diagnosis of Nasal Pathology in Canine Patients |
| title_short | A Novel Machine Learning Model for the Automated Diagnosis of Nasal Pathology in Canine Patients |
| title_sort | novel machine learning model for the automated diagnosis of nasal pathology in canine patients |
| topic | convolutional neural network machine learning computed tomography intranasal neoplasia fungal rhinitis canine head |
| url | https://www.mdpi.com/2076-2615/15/12/1718 |
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