Extraction of Text from Optic Nerve Optical Coherence Tomography Reports
Iyad Majid Youchen Victor Zhang Robert Chang Sophia Y. Wang
Purpose: The purpose of this study was to develop and evaluate rule-based
algorithms to enhance the extraction of text data, including retinal nerve
fiber layer (RNFL) values and other ganglion cell count (GCC) data, from Zeiss
Cirrus optical coherence tomography (OCT) scan reports. Methods: DICOM files
that contained encapsulated PDF reports with RNFL or Ganglion Cell in their
document titles were identified from a clinical imaging repository at a single
academic ophthalmic center. PDF reports were then converted into image files
and processed using the PaddleOCR Python package for optical character
recognition. Rule-based algorithms were designed and iteratively optimized for
improved performance in extracting RNFL and GCC data. Evaluation of the
algorithms was conducted through manual review of a set of RNFL and GCC
reports. Results: The developed algorithms demonstrated high precision in
extracting data from both RNFL and GCC scans. Precision was slightly better for
the right eye in RNFL extraction (OD: 0.9803 vs. OS: 0.9046), and for the left
eye in GCC extraction (OD: 0.9567 vs. OS: 0.9677). Some values presented more
challenges in extraction, particularly clock hours 5 and 6 for RNFL thickness,
and signal strength for GCC. Conclusions: A customized optical character
recognition algorithm can identify numeric results from optical coherence scan
reports with high precision. Automated processing of PDF reports can greatly
reduce the time to extract OCT results on a large scale.