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American Journal of Oral Medicine and Radiology

Volume 8, Issue 2, 2021
Mcmed International
American Journal of Oral Medicine and Radiology
Issn
XXX-XXXX (Print), 2394 - 7721 (Online)
Frequency
bi-annual
Email
editorajomr@mcmed.us
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Abstract
Title
AUTOMATED ANALYSIS OF RADIOLOGY REPORTS FOR PULMONARY EMBOLISM AND INCIDENTAL FINDINGS USING NATURAL LANGUAGE PROCESSING
Author
Dr. Pasupuleti Santosh Kumar
Email
keyword
Computed Tomography Angiography (CTA), Computed Tomography Venography (CTV), Pulmonary Embolism (PE), Natural Language Processing (NLP) and Incidental Findings
Abstract
This study evaluates the effectiveness of automated analysis of 7,000 radiology reports to detect pulmonary embolism, deep vein thrombosis, and incidental findings using computed tomography angiography and venography. A structured machinelearning-based classification model was developed to analyze radiology reports, achieving high precision (0.98) and recall (0.87) for thromboembolic conditions. The study also highlights the complementary diagnostic value of computed tomography angiography and computed tomography venography, particularly in detecting incidentalomas in 32 percent of cases. The findings emphasize the role of automated text analysis in enhancing radiological diagnosis and optimizing patient management. By improving classification accuracy and standardizing incidental finding detection, this study contributes to efficient clinical decision-making and supports the broader integration of artificial intelligence-driven methodologies in radiology
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