Lung CT Screening Reporting and Data System Speed and Accuracy Are Increased With the Use of a Semiautomated Computer Application
| Autor: | Toshimasa J. Clark, Suresh Maximin, Peter B. Sachs, Thomas F. Flood |
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| Rok vydání: | 2015 |
| Předmět: |
Male
medicine.medical_specialty Decision support system Lung Neoplasms ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION computer.software_genre Pattern Recognition Automated medicine Humans Computer Simulation Radiology Nuclear Medicine and imaging Medical physics Implementation Decision Making Computer-Assisted Early Detection of Cancer Solitary pulmonary nodule Computer program business.industry Solitary Pulmonary Nodule Nodule (medicine) respiratory system medicine.disease Quality Improvement United States respiratory tract diseases Radiology Information Systems ComputingMethodologies_PATTERNRECOGNITION Categorization Radiographic Image Interpretation Computer-Assisted Table (database) Female Data mining medicine.symptom Tomography X-Ray Computed business computer Algorithms Lung cancer screening |
| Zdroj: | Journal of the American College of Radiology. 12:1301-1306 |
| ISSN: | 1546-1440 |
| Popis: | The Lung CT Screening Reporting and Data System (Lung-RADS™) is an algorithm that can be used to classify lung nodules in patients with significant smoking histories. It is published in table format but can be implemented as a computer program. The aim of this study was to assess the efficiency and accuracy of the use of a computer program versus the table in categorizing lung nodules.The Lung-RADS algorithm was implemented as a computer program. Through the use of a survey tool, respondents were asked to categorize 13 simulated lung nodules using the computer program and the Lung-RADS table as published. Data were gathered regarding time to completion, accuracy of each nodule's categorization, users' subjective categorization confidence, and users' perceived efficiency using each method.The use of a computer program to categorize lung nodules resulted in significantly increased interpretation speed (80.8 ± 37.7 vs 156 ± 105 seconds, P.0001), lung nodule classification accuracy (99.6% vs 76.5%, P.0001), and perceived confidence and efficiency compared with the use of the table. There were no significant differences in accuracy when comparing thoracic radiologists with the remainder of the group.Radiologists were both more efficient and more accurate in lung nodule categorization when using computerized decision support tools. The authors propose that other institutions use computerized implementations of Lung-RADS in the interests of both efficiency and patient outcomes through proper management. Furthermore, they suggest the ACR design future iterations of the Lung-RADS algorithm with computerized decision support in mind. |
| Databáze: | OpenAIRE |
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