Curved planar reformation and optimal path tracing (CROP) method for false positive reduction in computer-aided detection of pulmonary embolism in CTPA

2013 ◽  
Author(s):  
Chuan Zhou ◽  
Heang-Ping Chan ◽  
Yanhui Guo ◽  
Jun Wei ◽  
Aamer Chughtai ◽  
...  
Keyword(s):  
Pulmonary Embolism ◽  
False Positive ◽  
Optimal Path ◽  
Computer Aided Detection ◽  
Path Tracing ◽  
Curved Planar Reformation ◽  
False Positive Reduction ◽  
Computer Aided

False-positive reduction using Hessian features in computer-aided detection of pulmonary nodules on thoracic CT images

2005 ◽  
Author(s):  
Berkman Sahiner ◽  
Zhanyu Ge ◽  
Heang-Ping Chan ◽  
Lubomir M. Hadjiiski ◽  
Naama Bogot ◽  
...  
Keyword(s):  
False Positive ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Computer Aided Detection ◽  
Thoracic Ct ◽  
False Positive Reduction ◽  
Computer Aided

scholarly journals Present Limitations of Artificial Intelligence in the Emergency Setting – Performance Study of a Commercial, Computer-Aided Detection Algorithm for Pulmonary Embolism

2021 ◽  
Author(s):  
Katharina Müller-Peltzer ◽  
Lena Kretzschmar ◽  
Giovanna Negrão de Figueiredo ◽  
Alexander Crispin ◽  
Robert Stahl ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Pulmonary Embolism ◽  
Emergency Room ◽  
False Positive ◽  
Detection Algorithm ◽  
Emergency Setting ◽  
Computer Aided Detection ◽  
Computer Aided ◽  
Noise Ratio

Purpose Since artificial intelligence is transitioning from an experimental stage to clinical implementation, the aim of our study was to evaluate the performance of a commercial, computer-aided detection algorithm of computed tomography pulmonary angiograms regarding the presence of pulmonary embolism in the emergency room. Materials and Methods This retrospective study includes all pulmonary computed tomography angiogram studies performed in a large emergency department over a period of 36 months that were analyzed by two radiologists experienced in emergency radiology to set a reference standard. Original reports and computer-aided detection results were compared regarding the detection of lobar, segmental, and subsegmental pulmonary embolism. All computer-aided detection findings were analyzed concerning the underlying pathology. False-positive findings were correlated to the contrast-to-noise ratio. Results Expert reading revealed pulmonary embolism in 182 of 1229 patients (49 % men, 10–97 years) with a total of 504 emboli. The computer-aided detection algorithm reported 3331 findings, including 258 (8 %) true-positive findings and 3073 (92 %) false-positive findings. Computer-aided detection analysis showed a sensitivity of 47 % (95 %CI: 33–61 %) on the lobar level and 50 % (95 %CI 43–56 %) on the subsegmental level. On average, there were 2.25 false-positive findings per study (median 2, range 0–25). There was no significant correlation between the number of false-positive findings and the contrast-to-noise ratio (Spearman’s Rank Correlation Coefficient = 0.09). Soft tissue (61.0 %) and pulmonary veins (24.1 %) were the most common underlying reasons for false-positive findings. Conclusion Applied to a population at a large emergency room, the tested commercial computer-aided detection algorithm faced relevant performance challenges that need to be addressed in future development projects. Key Points:  Citation Format

scholarly journals Computer-aided detection of lung nodules: False positive reduction using a 3D gradient field method and 3D ellipsoid fitting

2005 ◽  
Vol 32 (8) ◽  
pp. 2443-2454 ◽  
Author(s):  
Zhanyu Ge ◽  
Berkman Sahiner ◽  
Heang-Ping Chan ◽  
Lubomir M. Hadjiiski ◽  
Philip N. Cascade ◽  
...  
Keyword(s):  
False Positive ◽  
Field Method ◽  
Gradient Field ◽  
Lung Nodules ◽  
Computer Aided Detection ◽  
False Positive Reduction ◽  
Computer Aided

Computer-aided detection of lung nodules: false positive reduction using a 3D gradient field method

2004 ◽  
Author(s):  
Zhanyu Ge ◽  
Berkman Sahiner ◽  
Heang-Ping Chan ◽  
Lubomir M. Hadjiiski ◽  
Jun Wei ◽  
...  
Keyword(s):  
False Positive ◽  
Field Method ◽  
Gradient Field ◽  
Lung Nodules ◽  
Computer Aided Detection ◽  
False Positive Reduction ◽  
Computer Aided
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