Automated abstraction of myocardial perfusion imaging reports using natural language processing

Sharedalal, Parija; Singh, Ajay Kumar; Shah, Neal; Jain, Diwakar

doi:10.1007/s12350-020-02507-4

Cited by 1 publication

(1 citation statement)

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“…In nuclear cardiology, such methods have been used to facilitate attenuation correction 17 and automate reports of myocardial perfusion imaging. [18][19][20][21] However, very few have used artificial intelligence to optimize the interpretation of PET imaging. 22 We have done this to facilitate assessment of early atherosclerosis in the large arteries and changes therein over a period of 2 years.…”

Section: Strengths and Weaknesses Of The Studymentioning

confidence: 99%

“Global” cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation in 18F-sodium fluoride PET/CT scans: Head-to-head comparison

Piri

Edenbrandt

Larsson³

et al. 2022

Journal of Nuclear Cardiology

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Background: Artificial intelligence (AI) is known to provide effective means to accelerate and facilitate clinical and research processes. So in this study it was aimed to compare a AI-based method for cardiac segmentation in positron emission tomography/computed tomography (PET/CT) scans with manual segmentation to assess global cardiac atherosclerosis burden.Methods: A trained convolutional neural network (CNN) was used for cardiac segmentation in 18 Fsodium fluoride PET/CT scans of 29 healthy volunteers and 20 angina pectoris patients and compared with manual segmentation. Parameters for segmented volume (Vol) and mean, maximal, and total standardized uptake values (SUVmean, SUVmax, SUVtotal) were analysed by Bland-Altman Limits of Agreement. Repeatability with AI-based assessment of the same scans is 100%.Repeatability (same conditions, same operator) and reproducibility (same conditions, two different operators) of manual segmentation was examined by re-segmentation in 25 randomly selected scans.

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