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.

开发和评估基于规则的算法以提取来自 Zeiss Cirrus 光学相干断层扫描报告的文本数据，包括视网膜神经纤维层（RNFL）值和其他神经节细胞计数（GCC）数据。通过手动审核一组 RNFL 和 GCC 报告，验证算法的性能高，可以准确提取数据。定制化的光学字符识别算法可以在大规模上自动处理 PDF 报告以大大减少提取光相干扫描结果的时间。

从视神经光学相干断层扫描报告中提取文本

Extraction of Text from Optic Nerve Optical Coherence Tomography Reports

Discovering what other people think has always been a key aspect of our
information-gathering strategy. People can now actively utilize information
technology to seek out and comprehend the ideas of others, thanks to the
increased availability and popularity of opinion-rich resources such as online
review sites and personal blogs. Because of its crucial function in
understanding people's opinions, sentiment analysis (SA) is a crucial task.
Existing research, on the other hand, is primarily focused on the English
language, with just a small amount of study devoted to low-resource languages.
For sentiment analysis, this work presented a new multi-class Urdu dataset
based on user evaluations. The tweeter website was used to get Urdu dataset.
Our proposed dataset includes 10,000 reviews that have been carefully
classified into two categories by human experts: positive, negative. The
primary purpose of this research is to construct a manually annotated dataset
for Urdu sentiment analysis and to establish the baseline result. Five
different lexicon- and rule-based algorithms including Naivebayes, Stanza,
Textblob, Vader, and Flair are employed and the experimental results show that
Flair with an accuracy of 70% outperforms other tested algorithms.

本研究提出了一个基于用户评价的新型多类乌尔都数据集，旨在为乌尔都情感分析构建手动注释的数据集，并建立基准结果。使用五种不同的词库和基于规则的算法，最终实验结果表明，Flair 的准确率为 70%，优于其他测试的算法。