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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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Park J, Cho H, Ji Y, Lee K, Yoon H. Detection of spondylosis deformans in thoracolumbar and lumbar lateral X-ray images of dogs using a deep learning network. Front Vet Sci 2024; 11:1334438. [PMID: 38425836 PMCID: PMC10902442 DOI: 10.3389/fvets.2024.1334438] [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: 11/07/2023] [Accepted: 01/30/2024] [Indexed: 03/02/2024] Open
Abstract
Introduction Spondylosis deformans is a non-inflammatory osteophytic reaction that develops to re-establish the stability of weakened joints between intervertebral discs. However, assessing these changes using radiography is subjective and difficult. In human medicine, attempts have been made to use artificial intelligence to accurately diagnose difficult and ambiguous diseases in medical imaging. Deep learning, a form of artificial intelligence, is most commonly used in medical imaging data analysis. It is a technique that utilizes neural networks to self-learn and extract features from data to diagnose diseases. However, no deep learning model has been developed to detect vertebral diseases in canine thoracolumbar and lumbar lateral X-ray images. Therefore, this study aimed to establish a segmentation model that automatically recognizes the vertebral body and spondylosis deformans in the thoracolumbar and lumbar lateral radiographs of dogs. Methods A total of 265 thoracolumbar and lumbar lateral radiographic images from 162 dogs were used to develop and evaluate the deep learning model based on the attention U-Net algorithm to segment the vertebral body and detect spondylosis deformans. Results When comparing the ability of the deep learning model and veterinary clinicians to recognize spondylosis deformans in the test dataset, the kappa value was 0.839, indicating an almost perfect agreement. Conclusions The deep learning model developed in this study is expected to automatically detect spondylosis deformans on thoracolumbar and lumbar lateral radiographs of dogs, helping to quickly and accurately identify unstable intervertebral disc space sites. Furthermore, the segmentation model developed in this study is expected to be useful for developing models that automatically recognize various vertebral and disc diseases.
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Affiliation(s)
- Junseol Park
- Department of Veterinary Medical Imaging, College of Veterinary Medicine, Jeonbuk National University, Iksan, Republic of Korea
- Biosafety Research Institute and College of Veterinary Medicine, Jeonbuk National University, Iksan, Republic of Korea
| | - Hyunwoo Cho
- Department of Electronic Engineering, Sogang University, Seoul, Republic of Korea
| | - Yewon Ji
- Department of Veterinary Medical Imaging, College of Veterinary Medicine, Jeonbuk National University, Iksan, Republic of Korea
| | - Kichang Lee
- Department of Veterinary Medical Imaging, College of Veterinary Medicine, Jeonbuk National University, Iksan, Republic of Korea
| | - Hakyoung Yoon
- Department of Veterinary Medical Imaging, College of Veterinary Medicine, Jeonbuk National University, Iksan, Republic of Korea
- Biosafety Research Institute and College of Veterinary Medicine, Jeonbuk National University, Iksan, Republic of Korea
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Liu Y, Liu Y, Li J, Chen Y, Xu F, Xu Y, Cao J, Ma Y. ECA-TFUnet: A U-shaped CNN-Transformer network with efficient channel attention for organ segmentation in anatomical sectional images of canines. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:18650-18669. [PMID: 38052573 DOI: 10.3934/mbe.2023827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Automated organ segmentation in anatomical sectional images of canines is crucial for clinical applications and the study of sectional anatomy. The manual delineation of organ boundaries by experts is a time-consuming and laborious task. However, semi-automatic segmentation methods have shown low segmentation accuracy. Deep learning-based CNN models lack the ability to establish long-range dependencies, leading to limited segmentation performance. Although Transformer-based models excel at establishing long-range dependencies, they face a limitation in capturing local detail information. To address these challenges, we propose a novel ECA-TFUnet model for organ segmentation in anatomical sectional images of canines. ECA-TFUnet model is a U-shaped CNN-Transformer network with Efficient Channel Attention, which fully combines the strengths of the Unet network and Transformer block. Specifically, The U-Net network is excellent at capturing detailed local information. The Transformer block is equipped in the first skip connection layer of the Unet network to effectively learn the global dependencies of different regions, which improves the representation ability of the model. Additionally, the Efficient Channel Attention Block is introduced to the Unet network to focus on more important channel information, further improving the robustness of the model. Furthermore, the mixed loss strategy is incorporated to alleviate the problem of class imbalance. Experimental results showed that the ECA-TFUnet model yielded 92.63% IoU, outperforming 11 state-of-the-art methods. To comprehensively evaluate the model performance, we also conducted experiments on a public dataset, which achieved 87.93% IoU, still superior to 11 state-of-the-art methods. Finally, we explored the use of a transfer learning strategy to provide good initialization parameters for the ECA-TFUnet model. We demonstrated that the ECA-TFUnet model exhibits superior segmentation performance on anatomical sectional images of canines, which has the potential for application in medical clinical diagnosis.
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Affiliation(s)
- Yunling Liu
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
| | - Yaxiong Liu
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
| | - Jingsong Li
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
| | - Yaoxing Chen
- College of Veterinary Medicine, China Agricultural University, Beijing 100193, China
| | - Fengjuan Xu
- Animal and Plant Disease Prevention and Control Center, Chaoyang District, Beijing 100016, China
| | - Yifa Xu
- Shandong Digihuman Technology Co., Ltd, Shandong 250100, China
| | - Jing Cao
- College of Veterinary Medicine, China Agricultural University, Beijing 100193, China
| | - Yuntao Ma
- College of Land Science and Technology, China Agricultural University, Beijing 100193, China
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Barge P, Oevermann A, Maiolini A, Durand A. Machine learning predicts histologic type and grade of canine gliomas based on MRI texture analysis. Vet Radiol Ultrasound 2023. [PMID: 37133981 DOI: 10.1111/vru.13242] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 03/16/2023] [Accepted: 03/21/2023] [Indexed: 05/04/2023] Open
Abstract
Conventional MRI features of canine gliomas subtypes and grades significantly overlap. Texture analysis (TA) quantifies image texture based on spatial arrangement of pixel intensities. Machine learning (ML) models based on MRI-TA demonstrate high accuracy in predicting brain tumor types and grades in human medicine. The aim of this retrospective, diagnostic accuracy study was to investigate the accuracy of ML-based MRI-TA in predicting canine gliomas histologic types and grades. Dogs with histopathological diagnosis of intracranial glioma and available brain MRI were included. Tumors were manually segmented across their entire volume in enhancing part, non-enhancing part, and peri-tumoral vasogenic edema in T2-weighted (T2w), T1-weighted (T1w), FLAIR, and T1w postcontrast sequences. Texture features were extracted and fed into three ML classifiers. Classifiers' performance was assessed using a leave-one-out cross-validation approach. Multiclass and binary models were built to predict histologic types (oligodendroglioma vs. astrocytoma vs. oligoastrocytoma) and grades (high vs. low), respectively. Thirty-eight dogs with a total of 40 masses were included. Machine learning classifiers had an average accuracy of 77% for discriminating tumor types and of 75.6% for predicting high-grade gliomas. The support vector machine classifier had an accuracy of up to 94% for predicting tumor types and up to 87% for predicting high-grade gliomas. The most discriminative texture features of tumor types and grades appeared related to the peri-tumoral edema in T1w images and to the non-enhancing part of the tumor in T2w images, respectively. In conclusion, ML-based MRI-TA has the potential to discriminate intracranial canine gliomas types and grades.
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Affiliation(s)
- Pablo Barge
- Division of Clinical Radiology, Department of Clinical Veterinary Science, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Anna Oevermann
- Division of Neurological Sciences, Department of Clinical Research and Veterinary Public Health, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Arianna Maiolini
- Division of Clinical Neurology, Department of Clinical Veterinary Science, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Alexane Durand
- Division of Clinical Radiology, Department of Clinical Veterinary Science, Vetsuisse Faculty, University of Bern, Bern, Switzerland
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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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Schmid D, Scholz VB, Kircher PR, Lautenschlaeger IE. Employing deep convolutional neural networks for segmenting the medial retropharyngeal lymph nodes in CT studies of dogs. Vet Radiol Ultrasound 2022; 63:763-770. [PMID: 35877815 PMCID: PMC9796347 DOI: 10.1111/vru.13132] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 05/04/2022] [Accepted: 05/20/2022] [Indexed: 01/01/2023] Open
Abstract
While still in its infancy, the application of deep convolutional neural networks in veterinary diagnostic imaging is a rapidly growing field. The preferred deep learning architecture to be employed is convolutional neural networks, as these provide the structure preferably used for the analysis of medical images. With this retrospective exploratory study, the applicability of such networks for the task of delineating certain organs with respect to their surrounding tissues was tested. More precisely, a deep convolutional neural network was trained to segment medial retropharyngeal lymph nodes in a study dataset consisting of CT scans of canine heads. With a limited dataset of 40 patients, the network in conjunction with image augmentation techniques achieved an intersection-overunion of overall fair performance (median 39%, 25 percentiles at 22%, 75 percentiles at 51%). The results indicate that these architectures can indeed be trained to segment anatomic structures in anatomically complicated and breed-related variating areas such as the head, possibly even using just small training sets. As these conditions are quite common in veterinary medical imaging, all routines were published as an open-source Python package with the hope of simplifying future research projects in the community.
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Affiliation(s)
- David Schmid
- Clinic of Diagnostic ImagingVetsuisse FacultyUniversity of ZurichZurichSwitzerland
| | - Volkher B. Scholz
- Clinic of Diagnostic ImagingVetsuisse FacultyUniversity of ZurichZurichSwitzerland
| | - Patrick R. Kircher
- Clinic of Diagnostic ImagingVetsuisse FacultyUniversity of ZurichZurichSwitzerland
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