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Automated vertebral heart size estimation from thoracic radiographs in dogs with AI-assisted clinical decision
Aymard Nguemo1, Fozame Bryan1, Asma Ghamacha1
1aivancity School of AI & Data for Business & Society, France.
Insights
This study introduces an AI framework to automate Vertebral Heart Size (VHS) measurement in dogs, aiding early detection of cardiac disease. The system rapidly estimates VHS and generates veterinary summaries, improving diagnostic efficiency.
Area of Science:
- Veterinary Radiology
- Artificial Intelligence in Medicine
- Canine Cardiology
Background:
- Cardiomegaly in dogs is a key indicator of cardiac disease, necessitating precise and timely diagnosis.
- Manual Vertebral Heart Size (VHS) measurement from radiographs is common but prone to variability and time constraints.
- Existing methods for assessing cardiac enlargement in companion animals require improvement for efficiency and accuracy.
Purpose of the Study:
- To develop and evaluate an AI-assisted integrated framework for automated VHS estimation in canine thoracic radiographs.
- To support veterinarians in radiographic assessment of cardiac size and streamline the diagnosis of cardiomegaly.
- To leverage deep learning and Large Language Models (LLMs) for rapid VHS computation and structured clinical summary generation.
Main Methods:
- A deep learning computer vision model was trained on annotated canine thoracic DICOM radiographs to detect landmarks and compute VHS.
- An integrated framework combined VHS estimation with DICOM metadata and clinical inputs.
- A Large Language Model (LLM) processed the data to generate structured veterinary summaries.
Main Results:
- The AI framework achieved rapid VHS computation and automatic generation of preliminary clinical summaries within seconds.
- Quantitative evaluation confirmed accurate detection of anatomical landmarks and reliable VHS estimation by the deep learning pipeline.
- Qualitative assessment showed generated veterinary summaries were coherent, context-aware, and consistent with radiographic findings.
Conclusions:
- The multimodal AI system demonstrates significant potential to enhance veterinary radiology workflows.
- The framework serves as an effective clinical decision-support tool for timely cardiomegaly assessment in companion animals.
- AI-driven automation can improve the accuracy and efficiency of cardiac size assessment in veterinary diagnostics.
Abstract:
Cardiomegaly is a clinically significant indicator of cardiac disease in canine companion animals, where early and accurate detection is essential for effective treatment planning. Vertebral Heart Size (VHS) measurement from thoracic radiographs is a widely used method for assessing cardiac enlargement; however, manual measurement and report drafting can be time-consuming and subject to inter-observer variability. This study proposes an AI-assisted, integrated framework to support veterinarians in radiographic assessment of cardiac size by automating VHS estimation from canine thoracic DICOM radiographs. A deep learning-based computer vision model is trained to detect anatomical landmarks and compute VHS using a curated dataset of annotated images. The extracted VHS measurements, together with relevant DICOM metadata and structured clinical inputs, are subsequently processed by a Large Language Model (LLM) to generate a structured veterinary summary for clinician review. The proposed framework enables rapid VHS computation and automatic generation of a preliminary clinical summary within seconds of image upload. Quantitative evaluation of our deep learning pipeline demonstrates accurate detection of thoracic anatomical landmarks and reliable VHS estimation. In contrast, qualitative evaluations indicate that the generated veterinary summaries are coherent, context-aware, and consistent with radiographic findings. Collectively, these results demonstrate the potential of multimodal AI systems to enhance veterinary radiology workflows and serve as effective clinical decision-support tools for the timely assessment of cardiomegaly in companion animals.
