CardioScope: A Web-Based System for Managing and Automating Echocardiographic Data Using Optical Character Recognition and Automated Report Generation

Abstract

Cardiovascular diseases remain the leading cause of mortality in the Philippines, with ischemic heart disease as the most prevalent condition. At the Philippine General Hospital (PGH), echocardiography is a critical diagnostic tool used in managing these conditions; however, the process of documenting and managing echocardiographic reports remains inefficient and labor-intensive, relying on paperbased systems and manual data transcription into the OpenMRS platform. This study aimed to develop a web-based system that automates the extraction and structuring of 2D echocardiographic data from scanned PDF reports to improve workflow efficiency and data usability for clinical and research purposes. The system, built using Laravel and integrated with Tesseract OCR and OpenCV, allows users to upload scanned PDF files, preprocess images (grayscale conversion, binarization, and resizing), define Regions of Interest (ROI), and extract structured data. Comparative analysis showed that while Tesseract OCR benefitted from preprocessing, PyMuPDF produced more accurate outputs, even from imagebased PDFs. The system also enables automatic generation of patient summary reports and export of structured data in CSV format. The results demonstrate that a combined OCR and ROI approach, along with manual ROI definition, significantly enhances the accuracy of text extraction from medical documents. This solution reduces administrative workload, minimizes human error, and provides structured datasets for advanced research and potential AI-driven applications—ultimately improving the quality of cardiovascular care at PGH.