Abstract
Purpose: To introduce and validate hvf_extraction_script, an open-source software script for the automated extraction and structuring of metadata, value plot data, and percentile plot data from Humphrey visual field (HVF) report images.Methods: Validation was performed on 90 HVF reports over three different report layouts, including a total of 1,530 metadata fields, 15,536 value plot data points, and 10,210 percentile data points, between the computer script and four human extractors, compared against DICOM reference data. Computer extraction and human extraction were compared on extraction time as well as accuracy of extraction for metadata, value plot data, and percentile plot data.Results: Computer extraction required 4.9-8.9 s per report, compared to the 6.5-19 min required by human extractors, representing a more than 40-fold difference in extraction speed. Computer metadata extraction error rate varied from an aggregate 1.2-3.5%, compared to 0.2-9.2% for human metadata extraction across all layouts. Computer value data point extraction had an aggregate error rate of 0.9% for version 1, <0.01% in version 2, and 0.15% in version 3, compared to 0.8-9.2% aggregate error rate for human extraction. Computer percentile data point extraction similarly had very low error rates, with no errors occurring in version 1 and 2, and 0.06% error rate in version 3, compared to 0.06-12.2% error rate for human extraction.Conclusions: This study introduces and validates hvf_extraction_script, an open-source tool for fast, accurate, automated data extraction of HVF reports to facilitate analysis of large-volume HVF datasets, and demonstrates the value of image processing tools in facilitating faster and cheaper large-volume data extraction in research settings.
Highlights
Within ophthalmology, large volume data analysis requires structured data to perform
To solve this need in the field of automated perimetry, we have developed and validated a software platform for extraction of Humphrey R Visual Field (HVF) reports, a form of static automated perimetry used widely in clinical environments
Development and testing was performed on a MacBook Air running Catalina 10.15.2 (Apple Inc, Cupertino, CA, USA). This software platform takes as input HVF report image files, “extracts” data from the report image, and outputs structured, digital data represented in that report (Figure 1)
Summary
Large volume data analysis requires structured data to perform. Perimetry data involves large volume of quantitative data for each location tested, often done serially to track longitudinal progression in conditions such as glaucoma or neuro-ophthalmic disease Such data can be analyzed using a variety of analysis techniques with both global and localized metrics [6, 7]. Few studies examining automated perimetry have datasets up to 2,000-3,000 eyes or more, with one study requiring the development of an in-house data extraction software system [10, 11] These studies indicate that there is an unmet need to develop methods to automatically and accurately extract large volume of perimetry studies, which is critical to building massive perimetry datasets for future detection and progression study in the ophthalmology field
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