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Your search keyword '"Anthony Reeve"' showing total 2 results
Start Over Author "Anthony Reeve" Topic artificial intelligence
2 results on '"Anthony Reeve"'

1. Opportunistic AI-enabled automated bone mineral density measurements in lung cancer screening and coronary calcium scoring CT scans are equivalent

Author

Morteza Naghavi, Isabel De Oliveira, Song Shou Mao, Amirhossein Jaberzadeh, Juan Montoya, Chenyu Zhang, Kyle Atlas, Venkat Manubolu, Marlon Montes, Dong Li, Thomas Atlas, Anthony Reeves, Claudia Henschke, David Yankelevitz, and Matthew Budoff

Subjects
Bone mineral density, Artificial intelligence, Deep learning, Opportunistic, Coronary artery calcium score, Quantitative computed tomography, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract

Rationale and objectives: We previously reported a novel manual method for measuring bone mineral density (BMD) in coronary artery calcium (CAC) scans and validated our method against Dual X-Ray Absorptiometry (DEXA). Furthermore, we have developed and validated an artificial intelligence (AI) based automated BMD (AutoBMD) measurement as an opportunistic add-on to CAC scans that recently received FDA approval. In this report, we present evidence of equivalency between AutoBMD measurements in cardiac vs lung CT scans. Materials and methods: AI models were trained using 132 cases with 7649 (3 mm) slices for CAC, and 37 cases with 21918 (0.5 mm) slices for lung scans. To validate AutoBMD against manual measurements, we used 6776 cases of BMD measured manually on CAC scans in the Multi-Ethnic Study of Atherosclerosis (MESA). We then used 165 additional cases from Harbor UCLA Lundquist Institute to compare AutoBMD in patients who underwent both cardiac and lung scans on the same day. Results: Mean±SD for age was 69 ± 9.4 years with 52.4% male. AutoBMD in lung and cardiac scans, and manual BMD in cardiac scans were 153.7 ± 43.9, 155.1 ± 44.4, and 163.6 ± 45.3 g/cm3, respectively (p = 0.09). Bland-Altman agreement analysis between AutoBMD lung and cardiac scans resulted in 1.37 g/cm3 mean differences. Pearson correlation coefficient between lung and cardiac AutoBMD was R2 = 0.95 (p

Published
2023
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2. Prediction of clinical behaviour and treatment for cancers

Author

Matthias E, Futschik, Mike, Sullivan, Anthony, Reeve, and Nikola, Kasabov

Subjects
Lymphoma, B-Cell, Reproducibility of Results, Antineoplastic Agents, Prognosis, Risk Assessment, Sensitivity and Specificity, Pattern Recognition, Automated, Fuzzy Logic, Artificial Intelligence, Humans, Diagnosis, Computer-Assisted, Neural Networks, Computer, Algorithms, Oligonucleotide Array Sequence Analysis
Abstract

Prediction of clinical behaviour and treatment for cancers is based on the integration of clinical and pathological parameters. Recent reports have demonstrated that gene expression profiling provides a powerful new approach for determining disease outcome. If clinical and microarray data each contain independent information then it should be possible to combine these datasets to gain more accurate prognostic information. Here, we have used existing clinical information and microarray data to generate a combined prognostic model for outcome prediction for diffuse large B-cell lymphoma (DLBCL). A prediction accuracy of 87.5% was achieved. This constitutes a significant improvement compared to the previously most accurate prognostic model with an accuracy of 77.6%. The model introduced here may be generally applicable to the combination of various types of molecular and clinical data for improving medical decision support systems and individualising patient care.

Published
2004

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