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Valente C, Wodzinski M, Guglielmini C, Poser H, Chiavegato D, Zotti A, Venturini R, Banzato T. Development of an artificial intelligence-based algorithm for predicting the severity of myxomatous mitral valve disease from thoracic radiographs by using two grading systems. Res Vet Sci 2024; 178:105377. [PMID: 39137607 DOI: 10.1016/j.rvsc.2024.105377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 07/26/2024] [Accepted: 08/07/2024] [Indexed: 08/15/2024]
Abstract
A heart-convolutional neural network (heart-CNN) was designed and tested for the automatic classification of chest radiographs in dogs affected by myxomatous mitral valve disease (MMVD) at different stages of disease severity. A retrospective and multicenter study was conducted. Lateral radiographs of dogs with concomitant X-ray and echocardiographic examination were selected from the internal databases of two institutions. Dogs were classified as healthy, B1, B2, C and D, based on American College of Veterinary Internal Medicine (ACVIM) guidelines, and as healthy, mild, moderate, severe and late stage, based on Mitral INsufficiency Echocardiographic (MINE) score. Heart-CNN performance was evaluated using confusion matrices, receiver operating characteristic curves, and t-SNE and UMAP analysis. The area under the curve (AUC) was 0.88, 0.88, 0.79, 0.89 and 0.84 for healthy and ACVIM stage B1, B2, C and D, respectively. According to the MINE score, the AUC was 0.90, 0.86, 0.71, 0.82 and 0.82 for healthy, mild, moderate, severe and late stage, respectively. The developed algorithm showed good accuracy in predicting MMVD stages based on both classification systems, proving a potentially useful tool in the early diagnosis of canine MMVD.
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Affiliation(s)
- Carlotta Valente
- Department of Animal Medicine, Production and Health, University of Padua, Viale dell'Università 16, 35020, Legnaro, Padua, Italy.
| | - Marek Wodzinski
- Department of Measurement and Electronics, AGH University of Kraków, Al. A. Mickiewicza 30, 30-059 Krakow, Poland; Information Systems Institute, University of Applied Sciences-Western Switzerland (HES-SO Valais), Rue de Technopôle 3, 3960 Sierre, Switzerland
| | - Carlo Guglielmini
- Department of Animal Medicine, Production and Health, University of Padua, Viale dell'Università 16, 35020, Legnaro, Padua, Italy
| | - Helen Poser
- Department of Animal Medicine, Production and Health, University of Padua, Viale dell'Università 16, 35020, Legnaro, Padua, Italy
| | - David Chiavegato
- AniCura Arcella Veterinary Clinic, Via Cardinale Callegari 48, 35133 Padua, Italy
| | - Alessandro Zotti
- Department of Animal Medicine, Production and Health, University of Padua, Viale dell'Università 16, 35020, Legnaro, Padua, Italy
| | - Roberto Venturini
- AniCura Arcella Veterinary Clinic, Via Cardinale Callegari 48, 35133 Padua, Italy
| | - Tommaso Banzato
- Department of Animal Medicine, Production and Health, University of Padua, Viale dell'Università 16, 35020, Legnaro, Padua, Italy
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2
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Burti S, Banzato T, Coghlan S, Wodzinski M, Bendazzoli M, Zotti A. Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations. Res Vet Sci 2024; 175:105317. [PMID: 38843690 DOI: 10.1016/j.rvsc.2024.105317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 05/22/2024] [Accepted: 05/29/2024] [Indexed: 06/17/2024]
Abstract
The field of veterinary diagnostic imaging is undergoing significant transformation with the integration of artificial intelligence (AI) tools. This manuscript provides an overview of the current state and future prospects of AI in veterinary diagnostic imaging. The manuscript delves into various applications of AI across different imaging modalities, such as radiology, ultrasound, computed tomography, and magnetic resonance imaging. Examples of AI applications in each modality are provided, ranging from orthopaedics to internal medicine, cardiology, and more. Notable studies are discussed, demonstrating AI's potential for improved accuracy in detecting and classifying various abnormalities. The ethical considerations of using AI in veterinary diagnostics are also explored, highlighting the need for transparent AI development, accurate training data, awareness of the limitations of AI models, and the importance of maintaining human expertise in the decision-making process. The manuscript underscores the significance of AI as a decision support tool rather than a replacement for human judgement. In conclusion, this comprehensive manuscript offers an assessment of the current landscape and future potential of AI in veterinary diagnostic imaging. It provides insights into the benefits and challenges of integrating AI into clinical practice while emphasizing the critical role of ethics and human expertise in ensuring the wellbeing of veterinary patients.
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Affiliation(s)
- Silvia Burti
- Department of Animal Medicine, Production and Health, University of Padua, Viale dell'Università 16, Legnaro, 35020 Padua, Italy.
| | - Tommaso Banzato
- Department of Animal Medicine, Production and Health, University of Padua, Viale dell'Università 16, Legnaro, 35020 Padua, Italy
| | - Simon Coghlan
- School of Computing and Information Systems, Centre for AI and Digital Ethics, Australian Research Council Centre of Excellence for Automated Decision-Making and Society, University of Melbourne, 3052 Melbourne, Australia
| | - Marek Wodzinski
- Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow, 30059 Kraków, Poland; Information Systems Institute, University of Applied Sciences - Western Switzerland (HES-SO Valais), 3960 Sierre, Switzerland
| | - Margherita Bendazzoli
- Department of Animal Medicine, Production and Health, University of Padua, Viale dell'Università 16, Legnaro, 35020 Padua, Italy
| | - Alessandro Zotti
- Department of Animal Medicine, Production and Health, University of Padua, Viale dell'Università 16, Legnaro, 35020 Padua, Italy
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3
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Suksangvoravong H, Choisunirachon N, Tongloy T, Chuwongin S, Boonsang S, Kittichai V, Thanaboonnipat C. Automatic classification and grading of canine tracheal collapse on thoracic radiographs by using deep learning. Vet Radiol Ultrasound 2024. [PMID: 39012062 DOI: 10.1111/vru.13413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 06/27/2024] [Accepted: 07/05/2024] [Indexed: 07/17/2024] Open
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.
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Affiliation(s)
- Hathaiphat Suksangvoravong
- Department of Veterinary Surgery, Faculty of Veterinary Science, Chulalongkorn University, Bangkok, Thailand
| | - Nan Choisunirachon
- Department of Veterinary Surgery, Faculty of Veterinary Science, Chulalongkorn University, Bangkok, Thailand
| | - Teerawat Tongloy
- College of Advanced Manufacturing Innovation, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand
| | - Santhad Chuwongin
- College of Advanced Manufacturing Innovation, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand
| | - Siridech Boonsang
- Department of Electrical Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand
| | - Veerayuth Kittichai
- Faculty of Medicine, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand
| | - Chutimon Thanaboonnipat
- Department of Veterinary Surgery, Faculty of Veterinary Science, Chulalongkorn University, Bangkok, Thailand
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4
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Zhao Z, Li X, Zhuang Y, Li F, Wang W, Wang Q, Su S, Huang J, Tang Y. A non-invasive method to determine core temperature for cats and dogs using surface temperatures based on machine learning. BMC Vet Res 2024; 20:199. [PMID: 38745195 PMCID: PMC11092218 DOI: 10.1186/s12917-024-04063-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 05/07/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Rectal temperature (RT) is an important index of core temperature, which has guiding significance for the diagnosis and treatment of pet diseases. OBJECTIVES Development and evaluation of an alternative method based on machine learning to determine the core temperatures of cats and dogs using surface temperatures. ANIMALS 200 cats and 200 dogs treated between March 2022 and May 2022. METHODS A group of cats and dogs were included in this study. The core temperatures and surface body temperatures were measured. Multiple machine learning methods were trained using a cross-validation approach and evaluated in one retrospective testing set and one prospective testing set. RESULTS The machine learning models could achieve promising performance in predicting the core temperatures of cats and dogs using surface temperatures. The root mean square errors (RMSE) were 0.25 and 0.15 for cats and dogs in the retrospective testing set, and 0.15 and 0.14 in the prospective testing set. CONCLUSION The machine learning model could accurately predict core temperatures for companion animals of cats and dogs using easily obtained body surface temperatures.
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Affiliation(s)
- Zimu Zhao
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Xujia Li
- Center for Artificial Intelligence in Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Department of General Surgery (Hepatobiliary Surgery), The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yan Zhuang
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Fan Li
- College of Computer Science, Sichuan University, Chengdu, China
- College of Blockchain Technology, Chengdu University of Information Technology, Chengdu, China
| | - Weijia Wang
- Genesis AI Lab, Futong Technology, Chengdu, China
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Qing Wang
- Xinwang Animal Hospital, Luzhou, China
| | - Song Su
- Center for Artificial Intelligence in Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
- Department of General Surgery (Hepatobiliary Surgery), The Affiliated Hospital of Southwest Medical University, Luzhou, China.
| | | | - Yong Tang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
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5
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Nyquist ML, Fink LA, Mauldin GE, Coffman CR. Evaluation of a Novel Veterinary Dental Radiography Artificial Intelligence Software Program. J Vet Dent 2024:8987564231221071. [PMID: 38321886 DOI: 10.1177/08987564231221071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
There is a growing trend of artificial intelligence (AI) applications in veterinary medicine, with the potential to assist veterinarians in clinical decisions. A commercially available, AI-based software program (AISP) for detecting common radiographic dental pathologies in dogs and cats was assessed for agreement with two human evaluators. Furcation bone loss, periapical lucency, resorptive lesion, retained tooth root, attachment (alveolar bone) loss and tooth fracture were assessed. The AISP does not attempt to diagnose or provide treatment recommendations, nor has it been trained to identify other types of radiographic pathology. Inter-rater reliability for detecting pathologies was measured by absolute percent agreement and Gwet's agreement coefficient. There was good to excellent inter-rater reliability among all raters, suggesting the AISP performs similarly at detecting the specified pathologies compared to human evaluators. Sensitivity and specificity for the AISP were assessed using human evaluators as the reference standard. The results revealed a trend of low sensitivity and high specificity, suggesting the AISP may produce a high rate of false negatives and may not be a good tool for initial screening. However, the low rate of false positives produced by the AISP suggests it may be beneficial as a "second set of eyes" because if it detects the specific pathology, there is a high likelihood that the pathology is present. With an understanding of the AISP, as an aid and not a substitute for veterinarians, the technology may increase dental radiography utilization and diagnostic potential.
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Affiliation(s)
| | - Lisa A Fink
- Arizona Veterinary Dental Specialists, Scottsdale, AZ, USA
| | | | - Curt R Coffman
- Arizona Veterinary Dental Specialists, Scottsdale, AZ, USA
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Celniak W, Wodziński M, Jurgas A, Burti S, Zotti A, Atzori M, Müller H, Banzato T. Improving the classification of veterinary thoracic radiographs through inter-species and inter-pathology self-supervised pre-training of deep learning models. Sci Rep 2023; 13:19518. [PMID: 37945653 PMCID: PMC10636209 DOI: 10.1038/s41598-023-46345-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 10/31/2023] [Indexed: 11/12/2023] Open
Abstract
The analysis of veterinary radiographic imaging data is an essential step in the diagnosis of many thoracic lesions. Given the limited time that physicians can devote to a single patient, it would be valuable to implement an automated system to help clinicians make faster but still accurate diagnoses. Currently, most of such systems are based on supervised deep learning approaches. However, the problem with these solutions is that they need a large database of labeled data. Access to such data is often limited, as it requires a great investment of both time and money. Therefore, in this work we present a solution that allows higher classification scores to be obtained using knowledge transfer from inter-species and inter-pathology self-supervised learning methods. Before training the network for classification, pretraining of the model was performed using self-supervised learning approaches on publicly available unlabeled radiographic data of human and dog images, which allowed substantially increasing the number of images for this phase. The self-supervised learning approaches included the Beta Variational Autoencoder, the Soft-Introspective Variational Autoencoder, and a Simple Framework for Contrastive Learning of Visual Representations. After the initial pretraining, fine-tuning was performed for the collected veterinary dataset using 20% of the available data. Next, a latent space exploration was performed for each model after which the encoding part of the model was fine-tuned again, this time in a supervised manner for classification. Simple Framework for Contrastive Learning of Visual Representations proved to be the most beneficial pretraining method. Therefore, it was for this method that experiments with various fine-tuning methods were carried out. We achieved a mean ROC AUC score of 0.77 and 0.66, respectively, for the laterolateral and dorsoventral projection datasets. The results show significant improvement compared to using the model without any pretraining approach.
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Affiliation(s)
- Weronika Celniak
- University of Applied Sciences Western Switzerland (HES-SO), 3960, Sierre, Switzerland.
- Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow, 30059, Kraków, Poland.
| | - Marek Wodziński
- University of Applied Sciences Western Switzerland (HES-SO), 3960, Sierre, Switzerland
- Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow, 30059, Kraków, Poland
| | - Artur Jurgas
- University of Applied Sciences Western Switzerland (HES-SO), 3960, Sierre, Switzerland
- Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow, 30059, Kraków, Poland
| | - Silvia Burti
- Department of Animal Medicine, Productions, and Health, Legnaro (PD), University of Padua, 35020, Padua, Italy
| | - Alessandro Zotti
- Department of Animal Medicine, Productions, and Health, Legnaro (PD), University of Padua, 35020, Padua, Italy
| | - Manfredo Atzori
- University of Applied Sciences Western Switzerland (HES-SO), 3960, Sierre, Switzerland
- Department of Neuroscience, University of Padua, 35121, Padua, IT, Italy
- Padova Neuroscience Center, University of Padova, Via Orus 2/B, 35129, Padova, Italy
| | - Henning Müller
- University of Applied Sciences Western Switzerland (HES-SO), 3960, Sierre, Switzerland
- Medical Faculty, University of Geneva, 1206, Geneva, Switzerland
- The Sense Research and Innovation Insitute, 1950, Sion, Switzerland
| | - Tommaso Banzato
- Department of Animal Medicine, Productions, and Health, Legnaro (PD), University of Padua, 35020, Padua, Italy
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7
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Valente C, Wodzinski M, Guglielmini C, Poser H, Chiavegato D, Zotti A, Venturini R, Banzato T. Development of an artificial intelligence-based method for the diagnosis of the severity of myxomatous mitral valve disease from canine chest radiographs. Front Vet Sci 2023; 10:1227009. [PMID: 37808107 PMCID: PMC10556456 DOI: 10.3389/fvets.2023.1227009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 09/12/2023] [Indexed: 10/10/2023] Open
Abstract
An algorithm based on artificial intelligence (AI) was developed and tested to classify different stages of myxomatous mitral valve disease (MMVD) from canine thoracic radiographs. The radiographs were selected from the medical databases of two different institutions, considering dogs over 6 years of age that had undergone chest X-ray and echocardiographic examination. Only radiographs clearly showing the cardiac silhouette were considered. The convolutional neural network (CNN) was trained on both the right and left lateral and/or ventro-dorsal or dorso-ventral views. Each dog was classified according to the American College of Veterinary Internal Medicine (ACVIM) guidelines as stage B1, B2 or C + D. ResNet18 CNN was used as a classification network, and the results were evaluated using confusion matrices, receiver operating characteristic curves, and t-SNE and UMAP projections. The area under the curve (AUC) showed good heart-CNN performance in determining the MMVD stage from the lateral views with an AUC of 0.87, 0.77, and 0.88 for stages B1, B2, and C + D, respectively. The high accuracy of the algorithm in predicting the MMVD stage suggests that it could stand as a useful support tool in the interpretation of canine thoracic radiographs.
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Affiliation(s)
- Carlotta Valente
- Department of Animal Medicine, Production and Health, University of Padua, Padua, Italy
| | - Marek Wodzinski
- Department of Measurement and Electronics, AGH University of Science and Technology, Krakow, Poland
- Information Systems Institute, University of Applied Sciences—Western Switzerland (HES-SO Valais), Sierre, Switzerland
| | - Carlo Guglielmini
- Department of Animal Medicine, Production and Health, University of Padua, Padua, Italy
| | - Helen Poser
- Department of Animal Medicine, Production and Health, University of Padua, Padua, Italy
| | | | - Alessandro Zotti
- Department of Animal Medicine, Production and Health, University of Padua, Padua, Italy
| | | | - Tommaso Banzato
- Department of Animal Medicine, Production and Health, University of Padua, Padua, Italy
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8
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Pomerantz LK, Solano M, Kalosa-Kenyon E. Performance of a commercially available artificial intelligence software for the detection of confirmed pulmonary nodules and masses in canine thoracic radiography. Vet Radiol Ultrasound 2023; 64:881-889. [PMID: 37549965 DOI: 10.1111/vru.13287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 06/29/2023] [Accepted: 06/30/2023] [Indexed: 08/09/2023] Open
Abstract
Advancements in the field of artificial intelligence (AI) are modest in veterinary medicine relative to their substantial growth in human medicine. However, interest in this field is increasing, and commercially available veterinary AI products are already on the market. In this retrospective, diagnostic accuracy study, the accuracy of a commercially available convolutional neural network AI product (Vetology AI®) is assessed on 56 thoracic radiographic studies of pulmonary nodules and masses, as well as 32 control cases. Positive cases were confirmed to have pulmonary pathology consistent with a nodule/mass either by CT, cytology, or histopathology. The AI software detected pulmonary nodules/masses in 31 of 56 confirmed cases and correctly classified 30 of 32 control cases. The AI model accuracy is 69.3%, balanced accuracy 74.6%, F1-score 0.7, sensitivity 55.4%, and specificity 93.75%. Building on these results, both the current clinical relevance of AI and how veterinarians can be expected to use available commercial products are discussed.
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Affiliation(s)
- Leah Kathleen Pomerantz
- Department of Clinical Sciences, Tufts University Cummings School of Veterinary Medicine, North Grafton, Massachusetts, USA
| | - Mauricio Solano
- Department of Clinical Sciences, Tufts University Cummings School of Veterinary Medicine, North Grafton, Massachusetts, USA
| | - Eric Kalosa-Kenyon
- Department of Clinical Sciences, Tufts University Cummings School of Veterinary Medicine, North Grafton, Massachusetts, USA
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9
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Franco-Gonçalo P, Pereira AI, Loureiro C, Alves-Pimenta S, Filipe V, Gonçalves L, Colaço B, Leite P, McEvoy F, Ginja M. Femoral Neck Thickness Index as an Indicator of Proximal Femur Bone Modeling. Vet Sci 2023; 10:371. [PMID: 37368757 DOI: 10.3390/vetsci10060371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/18/2023] [Accepted: 05/21/2023] [Indexed: 06/29/2023] Open
Abstract
The alteration in the shape of the femoral neck is an important radiographic sign for scoring canine hip dysplasia (CHD). Previous studies have reported that the femoral neck thickness (FNT) is greater in dogs with hip joint dysplasia, becoming progressively thicker with disease severity. The main objective of this work was to describe a femoral neck thickness index (FNTi) to quantify FNT and to study its association with the degree of CHD using the Fédération Cynologique Internationale (FCI) scheme. A total of 53 dogs (106 hips) were randomly selected for this study. Two examiners performed FNTi estimation to study intra- and inter-examiner reliability and agreement. The paired t-test, the Bland-Altman plots, and the intraclass correlation coefficient showed excellent agreement and reliability between the measurements of the two examiners and the examiners' sessions. All joints were scored in five categories by an experienced examiner according to FCI criteria. The results from examiner 1 were compared between FCI categories. Hips that were assigned an FCI grade of A (n = 19), B (n = 23), C (n = 24), D (n = 24), and E (n = 16) had a mean ± standard deviation FNTi of 0.809 ± 0.024, 0.835 ± 0.044, 0.868 ± 0.022, 0.903 ± 0.033, and 0.923 ± 0.068, respectively (ANOVA, p < 0.05). Therefore, these results show that FNTi is a parameter capable of evaluating proximal femur bone modeling and that it has the potential to enrich conventional CHD scoring criteria if incorporated into a computer-aided diagnosis capable of detecting CHD.
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Affiliation(s)
- Pedro Franco-Gonçalo
- Department of Veterinary Science, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
- Animal and Veterinary Research Centre (CECAV), University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
- Associate Laboratory for Animal and Veterinary Sciences (AL4AnimalS), University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
| | - Ana Inês Pereira
- Department of Veterinary Science, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
- Department of Animal Science, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
| | - Cátia Loureiro
- Department of Engineering, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
| | - Sofia Alves-Pimenta
- Animal and Veterinary Research Centre (CECAV), University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
- Associate Laboratory for Animal and Veterinary Sciences (AL4AnimalS), University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
- Department of Animal Science, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
| | - Vítor Filipe
- Department of Engineering, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
- Institute for Systems and Computer Engineering (INESC-TEC), Technology and Science, 4200-465 Porto, Portugal
| | - Lio Gonçalves
- Department of Engineering, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
- Institute for Systems and Computer Engineering (INESC-TEC), Technology and Science, 4200-465 Porto, Portugal
| | - Bruno Colaço
- Animal and Veterinary Research Centre (CECAV), University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
- Associate Laboratory for Animal and Veterinary Sciences (AL4AnimalS), University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
- Department of Animal Science, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
| | - Pedro Leite
- Neadvance Machine Vision SA, 4705-002 Braga, Portugal
| | - Fintan McEvoy
- Department of Veterinary Clinical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, 1870 Copenhagen, Denmark
| | - Mário Ginja
- Department of Veterinary Science, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
- Animal and Veterinary Research Centre (CECAV), University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
- Associate Laboratory for Animal and Veterinary Sciences (AL4AnimalS), University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
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10
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Pereira AI, Franco-Gonçalo P, Leite P, Ribeiro A, Alves-Pimenta MS, Colaço B, Loureiro C, Gonçalves L, Filipe V, Ginja M. Artificial Intelligence in Veterinary Imaging: An Overview. Vet Sci 2023; 10:vetsci10050320. [PMID: 37235403 DOI: 10.3390/vetsci10050320] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/21/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023] Open
Abstract
Artificial intelligence and machine learning have been increasingly used in the medical imaging field in the past few years. The evaluation of medical images is very subjective and complex, and therefore the application of artificial intelligence and deep learning methods to automatize the analysis process would be very beneficial. A lot of researchers have been applying these methods to image analysis diagnosis, developing software capable of assisting veterinary doctors or radiologists in their daily practice. This article details the main methodologies used to develop software applications on machine learning and how veterinarians with an interest in this field can benefit from such methodologies. The main goal of this study is to offer veterinary professionals a simple guide to enable them to understand the basics of artificial intelligence and machine learning and the concepts such as deep learning, convolutional neural networks, transfer learning, and the performance evaluation method. The language is adapted for medical technicians, and the work already published in this field is reviewed for application in the imaging diagnosis of different animal body systems: musculoskeletal, thoracic, nervous, and abdominal.
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Affiliation(s)
- Ana Inês Pereira
- Department of Veterinary Science, University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal
| | - Pedro Franco-Gonçalo
- Department of Veterinary Science, University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal
- Veterinary and Animal Research Centre (CECAV), University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal
- Associate Laboratory for Animal and Veterinary Sciences (AL4AnimalS), 5000-801 Vila Real, Portugal
| | - Pedro Leite
- Neadvance Machine Vision SA, 4705-002 Braga, Portugal
| | | | - Maria Sofia Alves-Pimenta
- Veterinary and Animal Research Centre (CECAV), University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal
- Associate Laboratory for Animal and Veterinary Sciences (AL4AnimalS), 5000-801 Vila Real, Portugal
- Department of Animal Science, University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal
| | - Bruno Colaço
- Veterinary and Animal Research Centre (CECAV), University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal
- Associate Laboratory for Animal and Veterinary Sciences (AL4AnimalS), 5000-801 Vila Real, Portugal
- Department of Animal Science, University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal
| | - Cátia Loureiro
- School of Science and Technology, University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal
- Department of Engineering, University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal
| | - Lio Gonçalves
- School of Science and Technology, University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal
- Department of Engineering, University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal
- Institute for Systems and Computer Engineering (INESC-TEC), Technology and Science, 4200-465 Porto, Portugal
| | - Vítor Filipe
- School of Science and Technology, University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal
- Department of Engineering, University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal
- Institute for Systems and Computer Engineering (INESC-TEC), Technology and Science, 4200-465 Porto, Portugal
| | - Mário Ginja
- Department of Veterinary Science, University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal
- Veterinary and Animal Research Centre (CECAV), University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal
- Associate Laboratory for Animal and Veterinary Sciences (AL4AnimalS), 5000-801 Vila Real, Portugal
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Hennessey E, DiFazio M, Hennessey R, Cassel N. Artificial intelligence in veterinary diagnostic imaging: A literature review. Vet Radiol Ultrasound 2022; 63 Suppl 1:851-870. [PMID: 36468206 DOI: 10.1111/vru.13163] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 05/05/2022] [Accepted: 07/07/2022] [Indexed: 12/09/2022] Open
Abstract
Artificial intelligence in veterinary medicine is an emerging field. Machine learning, a subfield of artificial intelligence, allows computer programs to analyze large imaging datasets and learn to perform tasks relevant to veterinary diagnostic imaging. This review summarizes the small, yet growing body of artificial intelligence literature in veterinary imaging, provides necessary background to understand these papers, and provides author commentary on the state of the field. To date, less than 40 peer-reviewed publications have utilized machine learning to perform imaging-associated tasks across multiple anatomic regions in veterinary clinical and biomedical research. Major challenges in this field include collection and cleaning of sufficient image data, selection of high-quality ground truth labels, formation of relationships between veterinary and machine learning professionals, and closure of the gap between academic uses of artificial intelligence and currently available commercial products. Further development of artificial intelligence has the potential to help meet the growing need for radiological services through applications in workflow, quality control, and image interpretation for both general practitioners and radiologists.
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Affiliation(s)
- Erin Hennessey
- Department of Clinical Sciences, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas, USA.,Army Medical Department, Student Detachment, San Antonio, Texas, USA
| | - Matthew DiFazio
- Department of Clinical Sciences, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas, USA
| | - Ryan Hennessey
- Department of Computer Science, College of Engineering, Kansas State University, Manhattan, Kansas, USA
| | - Nicky Cassel
- Department of Clinical Sciences, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas, USA
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Joslyn S, Alexander K. Evaluating artificial intelligence algorithms for use in veterinary radiology. Vet Radiol Ultrasound 2022; 63 Suppl 1:871-879. [PMID: 36514228 DOI: 10.1111/vru.13159] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 02/16/2022] [Accepted: 03/30/2022] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence is increasingly being used for applications in veterinary radiology, including detection of abnormalities and automated measurements. Unlike human radiology, there is no formal regulation or validation of AI algorithms for veterinary medicine and both general practitioner and specialist veterinarians must rely on their own judgment when deciding whether or not to incorporate AI algorithms to aid their clinical decision-making. The benefits and challenges to developing clinically useful and diagnostically accurate AI algorithms are discussed. Considerations for the development of AI research projects are also addressed. A framework is suggested to help veterinarians, in both research and clinical practice contexts, assess AI algorithms for veterinary radiology.
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Affiliation(s)
- Steve Joslyn
- ACVR/ECVDI AI Education and Development Committee, Vedi, Perth, Western Australia, Australia
| | - Kate Alexander
- ACVR/ECVDI AI Education and Development Committee, DMV Veterinary Center, Lachine, Quebec, Canada
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13
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Dumortier L, Guépin F, Delignette-Muller ML, Boulocher C, Grenier T. Deep learning in veterinary medicine, an approach based on CNN to detect pulmonary abnormalities from lateral thoracic radiographs in cats. Sci Rep 2022; 12:11418. [PMID: 35794167 PMCID: PMC9258008 DOI: 10.1038/s41598-022-14993-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 06/16/2022] [Indexed: 12/03/2022] Open
Abstract
Thoracic radiograph (TR) is a complementary exam widely used in small animal medicine which requires a sharp analysis to take full advantage of Radiographic Pulmonary Pattern (RPP). Although promising advances have been made in deep learning for veterinary imaging, the development of a Convolutional Neural Networks (CNN) to detect specifically RPP from feline TR images has not been investigated. Here, a CNN based on ResNet50V2 and pre-trained on ImageNet is first fine-tuned on human Chest X-rays and then fine-tuned again on 500 annotated TR images from the veterinary campus of VetAgro Sup (Lyon, France). The impact of manual segmentation of TR’s intrathoracic area and enhancing contrast method on the CNN’s performances has been compared. To improve classification performances, 200 networks were trained on random shuffles of training set and validation set. A voting approach over these 200 networks trained on segmented TR images produced the best classification performances and achieved mean Accuracy, F1-Score, Specificity, Positive Predictive Value and Sensitivity of 82%, 85%, 75%, 81% and 88% respectively on the test set. Finally, the classification schemes were discussed in the light of an ensemble method of class activation maps and confirmed that the proposed approach is helpful for veterinarians.
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Basran PS, Appleby RB. The unmet potential of artificial intelligence in veterinary medicine. Am J Vet Res 2022; 83:385-392. [PMID: 35353711 DOI: 10.2460/ajvr.22.03.0038] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Veterinary medicine is a broad and growing discipline that includes topics such as companion animal health, population medicine and zoonotic diseases, and agriculture. In this article, we provide insight on how artificial intelligence works and how it is currently applied in veterinary medicine. We also discuss its potential in veterinary medicine. Given the rapid pace of research and commercial product developments in this area, the next several years will pose challenges to understanding, interpreting, and adopting this powerful and evolving technology. Artificial intelligence has the potential to enable veterinarians to perform tasks more efficiently while providing new insights for the management and treatment of disorders. It is our hope that this will translate to better quality of life for animals and those who care for them.
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Affiliation(s)
- Parminder S Basran
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY
| | - Ryan B Appleby
- Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada
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