Quantitative Computed Tomography (QCT) as a Radiology Reporting Tool by Using Optical Character Recognition (OCR) and Macro Program

Lee, Young Han; Song, Ho Taek

doi:10.1007/s10278-012-9464-8

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…The macro program was applied to a radiologic reading environment [11][12][13], and it was also utilized for the automation of a reporting system based on quantitative computed tomography (QCT) [14]. We applied this macro program to the detection and correction of laterality errors in generating final radiology reports.…”

Section: Discussionmentioning

confidence: 99%

Detection and Correction of Laterality Errors in Radiology Reports

Lee

Yang

2015

J Digit Imaging

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The objectives of the study are to introduce the development of supervising software for double-checking of laterality error in radiology reports and to evaluate the usefulness of detection and correction software by applying it to radiology report systems. An AutoHotkey macro program was applied to the design for double-checking of laterality errors. The software was performed according to the flowchart below: (1) detecting laterality discrepancies between radiologic examination names and the context of the radiology report and (2) providing conditioned discrepancy correction with a pop-up window. The accuracy of the detection was evaluated with 300 radiologic examinations that include the intended discrepancies and concordance of lateralities. The number of detections and corrections were quantified, and the confidence intervals were calculated for accuracy. We also applied this module to previous radiology reports with laterality errors from the radiologic examination database to validate the module. The AutoHotkey-scripted macro program functioned well in the reading workstation, and it was acted successfully as additional software. The detection accuracy was 99.67 % (95 % CI; 99.01-%) in the 300 radiologic examinations from the radiologic reading session. There was one running failure, caused by a temporary lag in the hospital's computer network, but no failures resulted during the second trial. We found that there were laterality errors in 0.048 % (n=14/29,257) of the examinations from the database. We developed detection and correction software aimed at double-checking for laterality errors. This method can be successfully adopted in any hospital software and is expected to be included for a better radiologic reading environment.

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Section: Discussionmentioning

confidence: 99%

Detection and Correction of Laterality Errors in Radiology Reports

Lee

Yang

2015

J Digit Imaging

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“…OCR refers to the technology that electronic devices (scanners, digital cameras, etc.) determine the shape of handwritten or printed characters by detecting dark and bright patterns, and then use character recognition methods to convert the shape into digital character codes that can be recognized by computers [4]. It can convert paper materials into digital electronic information, so as to reduce the storage of image materials, reuse and analyze the identified characters.…”

Section: Operating Principle and Characteristics Of Ocr Systemmentioning

confidence: 99%

Electronic Bill Fast Recognition Based on OCR and CNN

Zheng,

Chen,

et al. 2024

Advances in Transdisciplinary Engineering

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The existing methods used for bill recognition need specific equipment scanning or a large number of bill labels to achieve a good recognition effect. To solve above problems, a bill recognition scheme based on OCR technology is proposed. In the part of image preprocessing, the methods of binarization, filtering and denoising, image rectification are used. Then, we use deep learning as the basic model of the bill text detection and recognition phase, and use ResNet to complete the detection of specific fields in the way of image classification. Finally, on the basis of the rapid identification method designed in this paper, the feasibility and effectiveness of the scheme are verified through the demand analysis and functional test of the bill identification system. The results show that it has achieved good results in both simulation data sets and practical applications, and it has more advantages than traditional recognition technologies in text and image recognition.

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“…To solve these challenges, several approaches have been proposed: voice recognition dictation, optical character recognition, and raw data retrieval [ 9 , 17 , 18 ]. Voice recognition dictation (e.g., Agfa TalkStation) and optical character recognition have replaced the manual key process, reducing typing errors; however, neither is 100% accurate, given the verbal nature of the data, miscategorization, and segmentation [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

BatchBMD as an Efficient and Accurate Dual-Energy X-ray Absorptiometry Report Generator

Chan

Huang

et al. 2021

Diagnostics

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Dual-energy X-ray absorptiometry is the gold standard for evaluating Bone Mineral Density (BMD); however, a typical BMD report is generated in a time-inefficient manner and is prone to error. We developed a rule-based automated reporting system, BatchBMD, that accelerates DXA reporting while improving its accuracy over current systems. BatchBMD generates a structured report, customized to the specific clinical purpose. To compare BatchBMD to a Web-based Reporting (WBR) system for efficiency and accuracy, 500 examinations were randomly chosen from those performed at the Taipei Municipal Wanfang Hospital from January to March 2021. The final assessment included all 2326 examinations conducted from September 2020 to March 2021. The average reporting times were 6.7 and 10.8 minutes for BatchBMD and the WBR system, respectively, while accuracy was 99.4% and 98.2%, respectively. Most of the errors made by BatchBMD were digit errors in the appendicular skeletal muscle index. After correcting this, 100% accuracy across all 2326 examinations was validated. This automated and accurate BMD reporting system significantly reduces report production workload for radiologists and technicians while increasing productivity and quality. Additionally, the portable software, which employs a simple framework, can reduce deployment costs in clinical practice.

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Quantitative Computed Tomography (QCT) as a Radiology Reporting Tool by Using Optical Character Recognition (OCR) and Macro Program

Cited by 5 publications

References 6 publications

Detection and Correction of Laterality Errors in Radiology Reports

Detection and Correction of Laterality Errors in Radiology Reports

Electronic Bill Fast Recognition Based on OCR and CNN

BatchBMD as an Efficient and Accurate Dual-Energy X-ray Absorptiometry Report Generator

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