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Development and Validation of Automated Visual Field Report Extraction Platform Using Computer Vision Tools

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机构: [1]Department of Ophthalmology, University of California, San Francisco, San Francisco, CA, United States, [2]Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Institute of Ophthalmology, Capital Medical University, Beijing, China, [3]Department of Ophthalmology, Shandong Provincial Hospital, Shandong First Medical University, Jinan, China, [4]Department of Ophthalmology, Bhumibol Adulyadej Hospital, Bangkok, Thailand, [5]Center for Preventive Ophthalmology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States, [6]Ophthalmology Section, Surgical Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, United States
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关键词: glaucoma visual field neuroophthalmogy optical character reader computer vision and image processing

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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.

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出版当年[2020]版:
大类 | 3 区 医学
小类 | 3 区 医学:内科
最新[2025]版:
大类 | 3 区 医学
小类 | 3 区 医学:内科
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出版当年[2019]版:
Q1 MEDICINE, GENERAL & INTERNAL
最新[2024]版:
Q1 MEDICINE, GENERAL & INTERNAL

影响因子: 最新[2024版] 最新五年平均 出版当年[2019版] 出版当年五年平均 出版前一年[2018版] 出版后一年[2020版]

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第一作者机构: [1]Department of Ophthalmology, University of California, San Francisco, San Francisco, CA, United States,
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通讯机构: [1]Department of Ophthalmology, University of California, San Francisco, San Francisco, CA, United States, [6]Ophthalmology Section, Surgical Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, United States
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