Artificial Intelligence in Veterinary Imaging: An Overview
Abstract
:Simple Summary
Abstract
1. Introduction
2. Machine Learning
2.1. Artificial Neural Networks and Deep Learning
2.2. Overfitting
2.3. Convolutional Neural Networks
2.4. Transfer Learning
2.5. Object Detection and Segmentation Tasks
2.6. Evaluation of the Model’s Performance
3. Veterinary Imaging
3.1. Musculoskeletal
3.2. Thoracic
3.3. Nervous System
3.4. Abdominal
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Body Region | ML Type | Image Dataset | Objective | Diagnostic Imaging | Reference |
---|---|---|---|---|---|
Musculoskeletal | Partial least squares discriminant analysis and an ANN | 256 images: 200 for training and 56 for testing | Identification of the hip region | X-ray | [36] |
Principal component analysis and support vector machine | 1993 images: 936 from diseased dogs and 1057 from healthy dogs | Identification of Golden Retriever Muscular Dystrophy | MRI | [37] | |
Support vector machine, Adaptive boosting, and Monte Carlo feature selection | 38 images: 5 from diseased and 5 from healthy dogs | Classification of Golden Retriever Muscular Dystrophy | MRI | [38] | |
YOLO network (YOLO v3 Tiny ConvNet) | 1835 images: 1686 for training and 149 for testing to the identification of the hip region | Identification of the hip region and hip dysplasia classification as normal or dysplastic | X-ray | [11] | |
CNN (Inception-V3) | 225 images: 165 images for training and 60 for testing | Hip dysplasia classification as normal or dysplastic | X-ray | [39] | |
CNN (AlexNet, GoogLeNet, and ResNet-50) and multi-class support vector machine | 1000 images to evaluate the dog’s maturity, 410 images for fracture dating, and 2027 for fracture detection | Determination of the dog’s maturity, fractures’ dating, and fractures’ detection in long bones | X-ray | [40] | |
CNN (ResNet-50) | 200 images: 80 of tissue with cancerous margins and 80 of normal tissue for training; 20 of tissue with cancerous margins and 20 of normal tissue for validation | Classification of surgical tissue margins as healthy or cancerous | SDOCT | [41] | |
3D CNNs | 1400 images: 800 for training, 400 for validation, and 200 for testing | Hip dysplasia classification as normal or dysplastic | X-ray MRI | [31] | |
U-Net and Feature Pyramid Network (FPN) | 138 images normal and dysplastic: 70% for training, 15% for validation, and 15% for testing | Segmentation of the dog’s femur and acetabulum bones | X-ray | [35] | |
U-Net | 202 images: 70% for training, 15% for validation, and 15% for testing | Active learning in the segmentation of the dog’s femur and acetabulum bones | X-ray | [25] | |
Thorax | Principal component analysis, partial least square discriminant analysis, and support vectors machines | 35 images: 29 images from diseased dogs and 6 images from healthy dogs | Diagnosis of pulmonary thromboembolism in dogs | CT | [42] |
Bag of features and a CNN | 3142 images for cardiomegaly (1571 normal and 1571 abnormal); 2086 images for pulmonary patterns (1043 normal and 1043 abnormal); 892 images for mediastinal shift (446 normal and 446 abnormal); 940 images for pleural effusion (470 normal and 470 abnormal); and 78 images for pneumothorax (39 normal and 39 abnormal) | Identification of cardiomegaly, pneumothorax, pleural effusion, pulmonary patterns, and mediastinal shifts | X-ray | [43] | |
CNN (DenseNet-121) | 2862 images: 80% for training and 20% for validation | Pulmonary lesions identification | X-ray | [44] | |
CNN (Inception V3, Inception-ResNet V2, VGG-19, and ResNet-101) | 1468 images: 1153 images for training and 315 images for testing | Detection of cardiomegaly in thoracic radiographs | X-ray | [45] | |
CNN (Visual Geometry Group 16 network) | 792 images: 711 images for training and 81 for testing | Detection of left atrial enlargement | X-ray | [46] | |
CNN (DenseNet-“PicoxIA”—a commercial program) | 15780 images: 90% for training and 10% for validation | Identification of 15 types of primary thoracic lesions | X-ray | [47] | |
CNN (Inception, MobileNet, ResNet, VGG, and a four-layer network) | 1174 images (65% from healthy dogs and 35% from diseased dogs) and training and test sets sorted using ten-fold cross-validation | Detection of pulmonary coccidioidomycosis | X-ray | [48] | |
CNN (ResNet-50 and DenseNet-121) | 3839 images randomly divided into training, validation, and test sets in the ratio of 8:1:1 | Classification of dog’s thoracic radiographs as unremarkable, cardiomegaly, alveolar, bronchial, and interstitial patterns, presence of masses, pleural effusion, pneumothorax, and megaesophagus | X-ray | [34] | |
CNN (ResNet-50 and Inception V3) | 1062 images randomly divided into training, validation, and test sets in the ratio of 8:1:1 | Classification of cat’s thoracic radiographs | X-ray | [49] | |
CNN (HRNet) | 2643 images: 1875 for training, 399 for validation, and 369 for testing | Determination of the vertebral heart score to identify cardiomegaly | X-ray | [50] | |
CNN (DenseNet-121-“PicoxIA”—a commercial program) | 60 images: 30 canine and 30 feline | Calculation of the vertebral heart score to identify cardiomegaly | X-ray | [51] | |
U-Net (Improved Attention U-Net) | 1000 images: 800 for training, 100 for validation, and 100 for testing | New automated cardiac index to improve the vertebral heart score and identify cardiomegaly | X-ray | [52] | |
CNN (ResNet-50 v2) | 500 images: 455 for training and validation and 45 for testing | Pulmonary patterns identification in cats | X-ray | [53] | |
CNN (DenseNet-121-“PicoxIA”—a commercial program) | 55780 images: 90% for training and 10% for testing | Classification of thoracic radiographs with 15 possible labels | X-ray | [54] | |
CNN (“Vetology”—a commercial program) | 481 images | Accuracy determination of the “Vetology” for cardiogenic pulmonary edema diagnosis | X-ray | [55] | |
CNN (“Vetology”—a commercial program with VGG-16 CNN architecture) | 4000 images: 2000 of pleural effusion and 2000 of normal patients for training | Accuracy determination of the “Vetology” for pleural effusion diagnosis | X-ray | [56] | |
Nervous system | Linear discriminant analysis | 58 sets of MRI scans of dogs with meningioma, 27 for training and 31 for testing | Prediction of the histological grade in dog’s meningiomas | MRI | [57] |
CNN (AlexNet and scrDNN) | 56 images: 60% for training, 10% for validation, and 30% for testing | Prediction of the histological grade in dog’s meningiomas | MRI | [58] | |
CNN (GoogleNet) | 80 images: 70% for training, 15% for validation, and 15% for testing | Distinction between canine glioma and meningioma | MRI | [59] | |
Sequential floating forward selection, support vector machine | 32 images: 10 normal, 11 with the malformation and clinical signs, and 11 with clinical signs without malformation | Identification of Cavalier King Charles dogs with Chiari-like malformation-associated pain and syringomyelia | MRI | [60] | |
CNN | 500 images: 375 images for training and 125 images for testing | Identification of thoracolumbar spinal cord pathologies in dogs | MRI | [61] | |
Random Forest classifier | 119 images: 80 images for training and 39 images for testing | Differentiation and identification of glial tumor cells and non-infectious inflammatory meningoencephalitis | MRI | [62] | |
Abdomen | CNN (AlexNet) | 48 images: 70% for training, 15% for validation, and 15% for testing | Detection of diffuse degenerative hepatic diseases | US | [63] |
Quadratic discriminant analysis | 40 images | Detecting canine hepatic masses and predicting malignancy | CT | [64] |
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Pereira, A.I.; Franco-Gonçalo, P.; Leite, P.; Ribeiro, A.; Alves-Pimenta, M.S.; Colaço, B.; Loureiro, C.; Gonçalves, L.; Filipe, V.; Ginja, M. Artificial Intelligence in Veterinary Imaging: An Overview. Vet. Sci. 2023, 10, 320. https://doi.org/10.3390/vetsci10050320
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. Veterinary Sciences. 2023; 10(5):320. https://doi.org/10.3390/vetsci10050320
Chicago/Turabian StylePereira, Ana Inês, Pedro Franco-Gonçalo, Pedro Leite, Alexandrine Ribeiro, Maria Sofia Alves-Pimenta, Bruno Colaço, Cátia Loureiro, Lio Gonçalves, Vítor Filipe, and Mário Ginja. 2023. "Artificial Intelligence in Veterinary Imaging: An Overview" Veterinary Sciences 10, no. 5: 320. https://doi.org/10.3390/vetsci10050320