Automated Clinical, Etiological, Anatomical, and Pathophysiological classification of venous duplex reports using
Joseph Cutteridge1, Henry Bergman2, Will Jackson3
1School of Public Health, Faculty of Medicine, Imperial College London, London, United Kingdom; Medical Sciences Division, Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom.
Objective:
To develop and internally validate a prototype multimodal artificial intelligence system for automated Clinical, Etiological, Anatomical, and Pathophysiological (CEAP) classification of venous duplex ultrasound (VDUS) reports, integrating natural language processing of free-text components with computer vision analysis of hand-drawn anatomical diagrams.
Methods:
Single-center retrospective observational study using routinely collected clinical data. One thousand consecutive VDUS reports from Cambridge University Hospitals National Health Service Foundation Trust, the United Kingdom (July 2024-May 2025) were labeled according to the CEAP classification, excluding the Etiological component, which could not be reliably determined from duplex reports alone. Transfer learning was applied using ClinicalBERT for text and MobileNetV3 for diagrammatic data. Clinical classes were predicted from request line text. Text- and image-based pathophysiological models were developed for four anatomical territories (great saphenous vein, small saphenous vein, deep system, and perforators), combined using late fusion with probability averaging.
Results:
The clinical CEAP model achieved accuracy of 0.91, macro-F1 of 0.82, and macro-area under the curve of 0.98. Pathophysiological prediction varied, with text models broadly outperforming image models. Fusion yielded heterogeneous benefits, improving small saphenous vein performance but reducing deep system accuracy. The performance of the final pathophysiological CEAP fusion models varied across anatomical territories: accuracy ranged from 0.70 to 0.92 and macro-area under the curve from 0.80 to 0.92.
Conclusions:
This study demonstrates the feasibility of automated CEAP classification from VDUS reports. Although class imbalance affected minority class predictions, the strong discriminatory performance validates this multimodal machine learning model for extracting clinically meaningful information from real-world data. This approach offers potential, pending external validation, to streamline vascular services through automated triage and guideline-compliant decision-making.
