Extraction of Text from Optic Nerve Optical Coherence Tomography Reports

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.

SoK: A Study of the Security on Voice Processing Systems

As the use of Voice Processing Systems (VPS) continues to become more prevalent in our daily lives through the increased reliance on applications such as commercial voice recognition devices as well as major text-to-speech software, the attacks on these systems are increasingly complex, varied, and constantly evolving. With the use cases for VPS rapidly growing into new spaces and purposes, the potential consequences regarding privacy are increasingly more dangerous. In addition, the growing number and increased practicality of over-the-air attacks have made system failures much more probable. In this paper, we will identify and classify an arrangement of unique attacks on voice processing systems. Over the years research has been moving from specialized, untargeted attacks that result in the malfunction of systems and the denial of services to more general, targeted attacks that can force an outcome controlled by an adversary. The current and most frequently used machine learning systems and deep neural networks, which are at the core of modern voice processing systems, were built with a focus on performance and scalability rather than security. Therefore, it is critical for us to reassess the developing voice processing landscape and to identify the state of current attacks and defenses so that we may suggest future developments and theoretical improvements.