AI-assisted veterinary radiology: diagnose diseases from X-rays, ultrasound, MRI

AI-assisted diagnosis of animal diseases from medical images

A three-branch clinic spends up to two hours on a single thoracic radiograph description — the radiologist is overloaded, the MRI queue keeps growing. We deployed an AI assistant for preliminary reading. The system is trained on thousands of annotated veterinary images and can detect cardiomegaly, pleural effusion, lung patterns on thoracic X-rays, as well as artifacts on abdominal ultrasound.

We use EfficientNet-B4, ResNet-50, and DenseNet-121 architectures with fine-tuning on specialized datasets. Results are comparable to expert assessment by veterinary radiologists. Our experience in computer vision spans over 15 projects in medical and veterinary diagnostics. Deploying such a system typically pays off within 4–6 months by reducing consultation time and lowering the number of repeat visits. Savings on outsourcing radiologists can reach 50,000 RUB/month per clinic.

Why AI diagnosis of animal diseases from images is challenging

Differences from human radiology: anatomical variability across species (cat, dog, rabbit, horse, exotic), size range (Chihuahua vs. German Shepherd — body mass ratio 1:60), projections (dorsoventral instead of anteroposterior). A model trained on human images cannot be directly transferred.

Main image types:

• Radiographs (thorax, abdomen, skeleton)
• Ultrasound images (abdomen, heart, thyroid)
• MRI/CT (spine, head) — in large clinics

How the AI system diagnoses animal diseases from images

The most demanded task is evaluation of canine and feline thorax: cardiomegaly, pleural effusion, pneumonia, tumors.

Cardiomegaly — assessed via Vertebral Heart Score (VHS): total heart size in vertebrae. A regression model predicts VHS directly. On a dataset of 1,200 thoracic radiographs (dogs): MAE = 0.31 vertebrae vs. normal 9.7±0.5 for most breeds. Comparable to inter-examiner variability among veterinarians (0.4 vertebrae).

Pleural effusion — visually obvious pathology (loss of diaphragmatic contour clarity), but missed when small volume. Binary classifier ResNet-50: AUC = 0.94 on test set.

Lung patterns — bronchial, interstitial, alveolar patterns require multi-label classification (patterns can co-occur). EfficientNet-B4 with multi-label head: micro F1 = 0.82 on 5 pattern classes. EfficientNet-B4 outperforms ResNet-50 by 5% F1 on this task.

Why data is the main challenge in veterinary CV

The primary problem in veterinary computer vision is the lack of public datasets comparable to medical scale. Our strategy:

1. Partner with 2–5 clinics (500–2,000 labeled cases in 3–6 months)
2. Transfer learning from ImageNet or medical datasets (ChestX-ray14) + fine-tuning on veterinary data
3. Synthetic data: augmentation with realistic pathological overlays on normal images

Transfer learning from human thoracic radiographs to canine works: a DenseNet-121 pretrained on ChestX-ray14 after fine-tuning on 800 canine radiographs yields AUC = 0.88 for cardiomegaly — better than training from scratch on the same volume (AUC = 0.79).

One of our partners — a veterinary clinic chain — reduced average image description time from 40 to 12 minutes after deployment. This allowed a 30% increase in patient flow without hiring additional radiologists.

What is included in the work

• Documentation: architecture description, dataset, metrics, and annotation guide
• Trained model with weights and configuration
• REST API for integration into existing PACS or veterinary information system
• Inference scripts and code examples (Python, PyTorch)
• 3-month warranty support
• Staff training on system use (2–4 hours)

Timeline

Base system for thoracic radiographs (dogs/cats): 10–14 weeks. Extended module with ultrasound and multiple modalities: 18–26 weeks. Cost is calculated individually based on data volume and required functionality.

Contact us to evaluate your project. We guarantee a prototype on your data within 10 weeks. Get a consultation on adapting the model to your tasks.

How Distribution Shift Kills CV Model Metrics in Industry

On a production line, a camera is installed to control product quality. The model is trained on 10,000 labeled images—test accuracy mAP 0.84. Deployed to production, and in the first week it misses 30% of defects. Lighting on the line changes between shifts; distribution shift nullifies the metrics. This is a classic story with computer vision in industry, where pattern recognition fails without proper drift handling.

Our engineers, with experience from 60+ computer vision projects, know how to eliminate such scenarios. We guarantee stable model performance under real conditions.

Object Detection: YOLO, RT-DETR, and Everything in Between

YOLO is the standard for real-time detection. YOLOv8 and YOLOv11 from Ultralytics are the most used versions in production: simple API, active community, built-in validation, and export to ONNX/TensorRT. For tasks with high accuracy requirements and less critical latency, RT-DETR, a transformer-based architecture without NMS, gives better mAP on COCO at comparable speed to YOLOv8l.

A typical mistake when training a detector: dataset of 8000 images, 3 classes, fine-tune YOLOv8m—F1 0.73 on validation. Look at confusion matrix—one class is almost never detected. Cause: imbalance 1:23. Solution: oversampling rare class, focal loss for objectness, augmentations (Mosaic, MixUp disabled for rare class as they "blur" it). Transfer learning is mandatory: pretrained on COCO weights reduces data requirement by 10 times. Fine-tuning on 500–2000 domain images yields a working model in 1–2 days on a single GPU.

For edge deployment: export to ONNX → TensorRT engine. YOLOv8n in TensorRT FP16 on Jetson AGX Orin gives 150+ FPS at P99 latency < 8 ms—3 times faster than ONNX Runtime without TensorRT. On server A10G: 700+ FPS for YOLOv8n in TensorRT INT8.

How Does Fine-Tuning YOLO Help in Pattern Recognition?

Suppose you need to find micro-defects on a metal surface—a task with high resolution and class imbalance. We use YOLOv8m pretrained on COCO and fine-tune on 2000 proprietary images. Apply augmentations Mosaic, MixUp, random perspective. After 200 epochs, mAP 0.5 reaches 0.93. Key techniques:

• Focal loss for the objectness head—reduces contribution of easily classified examples.
• Class-balanced sampling—equalizes representation of rare classes.
• Test Time Augmentation (TTA)—increases recall by 5–7% through averaging over flips and scales.

Get a consultation on architecture selection for your task—contact us.

Segmentation: SAM, Mask R-CNN, and Instance Segmentation

SAM (Segment Anything Model) from Meta changed the approach to segmentation. SAM 2 works with video, supports object tracking across frames—for interactive object selection by point or bbox, it's the best out-of-the-box choice. For production instance segmentation without interactive prompting, Mask R-CNN or YOLOv8-seg are used. YOLOv8-seg trains like a regular detector with additional masks, convenient in the same pipelines. Semantic segmentation (each pixel is a class) uses SegFormer, DeepLabV3+. SegFormer-B5 provides a good balance of accuracy and speed for satellite imagery or medical segmentation.

Case study: cell segmentation on microscopic images. Dataset of 400 images with manual annotation. Training Mask R-CNN on ResNet-50 backbone gave IoU 0.61—poor. Problem: objects (cells) overlap; standard NMS kills overlapping predictions. Solution: switch to cellpose (specialized architecture for biomedical tasks) + soft-NMS. IoU increased to 0.79.

OCR: When Tesseract Fails

Tesseract is a starting point for simple tasks: printed text, good lighting, straight layout. As soon as there are handwritten elements, non-standard fonts, perspective distortions, or multi-column layouts, Tesseract degrades quickly.

PaddleOCR is a production-grade solution: text block detection + recognition + structural analysis. Works out of the box for 80+ languages, including Russian. Supports tables and complex document structures. TrOCR (Microsoft) is a transformer OCR with strong results on handwritten text. For Russian handwritten text, fine-tuning is needed: the base model is trained mostly on Latin script.

What to Do When Tesseract Cannot Handle Pattern Recognition on Documents?

For tasks like "extract data from invoices/contracts/passports," we use LayoutLMv3 or Donut—these models understand document layout, not just text. Integration via Hugging Face Transformers, fine-tuning on 200–500 annotated documents. Typical pipeline:

1. Preprocessing: deskew, denoising, binarization via OpenCV.
2. Text block detection: PaddleOCR detection or CRAFT.
3. Recognition: PaddleOCR recognition or TrOCR.
4. Post-processing: normalization, validation via regex or LLM for structured fields.

For documents with fixed structure, template matching + OCR by coordinates is often more reliable than an end-to-end solution.

Face Recognition: Identification and Verification

Face recognition = detection + alignment + embedding + matching. Each stage matters.

Detection: RetinaFace or InsightFace for accurate face localization and keypoints. MTCNN is older but reliable. Embedding: ArcFace (InsightFace) is state-of-the-art for face recognition embeddings. Models iresnet50/iresnet100 pretrained on MS1MV3 (5M identities). Embedding vector 512 float32, comparison by cosine similarity. Threshold tuning: decision threshold is a critical parameter. At threshold 0.6, typical FPR on LFW benchmark is 0.001, TPR is 0.985. In production, threshold must be calibrated to the real distribution: people in masks, with changed appearance, different lighting conditions. Liveness detection is mandatory: MiniFASNet—lightweight model on CPU; FaceX-Zoo contains several pretrained liveness detectors.

Video Analytics

Video is a sequence of frames plus a temporal dimension. A naive approach—detecting on every frame—is expensive.

Tracking: ByteTrack and BoT-SORT are the standard for multi-object tracking. They work on top of any detector, adding persistent IDs to objects across frames—enabling object counting, motion tracking, velocity.

Optimization: not every frame needs processing. For static scenes, detect every 5–10 frames, with tracking in between. For event detection (person entering a zone), background subtraction (OpenCV MOG2) serves as a lightweight pre-filter before neural detection. Action recognition: SlowFast, VideoMAE for action classification. Heavy models—for production use ONNX export + TensorRT or offline processing.

How to Measure Pattern Recognition Model Quality in Production?

Quality monitoring is key to MLOps. We track:

• Prediction confidence distribution.
• Share of low-confidence predictions (indicator of OOD data).
• Drift of input images via feature distribution (embeddings from backbone).

A drop in average confidence from 0.87 to 0.71 over a week is an early signal of distribution shift. NVIDIA Triton Inference Server recommends tracking these metrics via Prometheus. Our certified engineers set up monitoring and guarantee SLA for inference quality.

Deployment of CV Models

For online inference, we use Triton Inference Server (NVIDIA)—production standard for serving CV models. Supports TensorRT, ONNX, PyTorch, dynamic batching, multiple instances. REST and gRPC API. We guarantee stable operation under load.

Edge deployment: ONNX Runtime on ARM/x86 CPU. TensorFlow Lite for mobile devices. OpenVINO for Intel CPU/GPU/VPU—gives 2–3× speedup on Intel hardware compared to ONNX Runtime. After deployment, we hand over the model with documentation and train personnel.

What Is Included in the Work

Deadlines and Cost

A prototype detector on existing data takes 1–2 weeks. Production system with optimization for target hardware takes 4–8 weeks. Full cycle including data labeling (1000–5000 images) takes 2–4 months. Cost is calculated individually for each task. Typical savings from implementing a quality control system can be significant per production line.

We have been in the market for over 5 years and completed 60+ computer vision projects. We will evaluate your project end-to-end—request a consultation to get a quote and technical proposal.