- AI
- Molecular Imaging
- CT
- X-Ray
- Ultrasound
- MRI
- Facility Management
- Mammography
Home-grown data-mining system strikes gold
Researchers at Mallinckrodt Institute of Radiology have developed a secure web-based, HIPAA-compliant data-mining tool for radiology reports based on the Google search engine using free and open source technologies.
Researchers at Mallinckrodt Institute of Radiology have developed a secure web-based, HIPAA-compliant data-mining tool for radiology reports based on the Google search engine using free and open source technologies.
Dr. Joseph Erinjeri and colleagues downloaded 20 months of radiology reports (915,000 studies, 2.8 GB) in text format from their RIS to a file server running the Windows 2003 Server operating system. Indexing of the document took approximately 36 hours, averaging 25,000 reports per hour.
The search engine (Google Desktop), web server (Apache), and scripting language (PERL) are all open source and/or freely available.
A keyword search of a common term like patient yielded the first 10 most relevant results of 915,000 total matches in 0.72 seconds. A search of a less common term like moderate cardiomegaly identified 7300 matches in 0.43 seconds.
By using the existing Google search algorithm and framework, radiologists can quickly perform useful searches, the authors said at the American Roentgen Ray Society meeting.
Study Reaffirms Low Risk for csPCa with Biopsy Omission After Negative Prostate MRI
December 19th 2024In a new study involving nearly 600 biopsy-naïve men, researchers found that only 4 percent of those with negative prostate MRI had clinically significant prostate cancer after three years of active monitoring.
Study Examines Impact of Deep Learning on Fast MRI Protocols for Knee Pain
December 17th 2024Ten-minute and five-minute knee MRI exams with compressed sequences facilitated by deep learning offered nearly equivalent sensitivity and specificity as an 18-minute conventional MRI knee exam, according to research presented recently at the RSNA conference.
Can Radiomics Bolster Low-Dose CT Prognostic Assessment for High-Risk Lung Adenocarcinoma?
December 16th 2024A CT-based radiomic model offered over 10 percent higher specificity and positive predictive value for high-risk lung adenocarcinoma in comparison to a radiographic model, according to external validation testing in a recent study.
